Proposal for A Course in Basic Information Technology Skills for Displaced Manufacturing Workers

Planning for Instruction in Non-Traditional Settings

Central Michigan University

8/11/2017

Proposal for A Course in Basic Information Technology Skills for Displaced Manufacturing Workers

Introduction

This proposal addresses the need for customized, and effective learning experiences for adult learners to learn IT skills after having had a career in manufacturing or other industrial jobs which may not have had the opportunity to use computers as part of their jobs.  The learning experience design will address this unique population of adult learners from a previous workforce to learn marketable IT skills in a community college setting.

The learning program outlined here incorporates principles, concepts, theories, and models of andragogy and the adult learner.

Purpose

To address the needs of adult learners who may have been displaced from traditional industrial workforce environments.  The learners will be non-traditional in that they have been out of the secondary education system for at least 10 years, and are seeking a new career path involving computing and Information Technology.  This may include learning to code (web, apps, desktop), or to work with administrative tasks like PC repair, computer networking, cybersecurity and other areas within IT.

The scope of this training plan is a large metropolitan area, partnered with both public education (community colleges) and industry.  There are also opportunities to partner with governmental agencies such as the SBA.  The organization of this proposal will seek grant funding and government contracts.  This project will be beneficial to the community, create jobs, and aid in workforce retraining.  It can be scaled up to deliver to a larger population and/or metropolitan area.

Problem Statement

Displaced workers from industrial environments need to upgrade the skills and knowledge to conform to the technological society.  There is a large skills gap within the general population of workers and the needs of the IT workforce.  This training is seeking to assist with filling the skills gap by providing IT trained individuals to local businesses, and start them on a new career path.

Goals

The overall goal is to train and prepare displaced workers, the students, to become micro-credentialed from one of the many certification organizations including CompTIA which offers such certifications as A+, Network+, Security+, CSA+, Linux+, etc.  The idea is to provide apprenticeship-like programs for IT careers, and to help fill the skills gap which exists in today’s workforce with regard to IT skills.  The intention is not to deliver coursework toward traditional degrees, but to develop students with alternative credentialing.  The focus on foundational information will be essential, reinforcing new literacies, especially digital literacies.

Success will be measured by the students’ successful completion of the objectives of the course, and attainment of a particular certification through a testing facility.  The training delivery will be fairly open-ended, enabling students to choose to pursue certification within a group of similar certifications whether provided by an organization or corporate entity.  In addition, success will rely on having qualified faculty to teach the courses and meaningful partnerships to be established and maintained.

Background

Certifications will not be granted, but simply a certificate of completion.  However, certification can be achieved by students enrolling in and completing certification tests.  The instructors will facilitate the learning as well as arranging for the 3rd party credentialing.  The courses, since they will be community based, will not be associated with a particular accreditation.  The participant/students will be required to sign agreements that state the training is for personal and professional development, but will not promise a credential or job placement.  The students will be self-directed in terms of completing the certification of their choice after having gone through coursework and content related to their choice of IT topic areas.

Analysis

Various literature was reviewed to relate learning theories to the design of this training plan.  Articles, books, and papers were sought to support the teaching and learning approaches and methodologies which were chosen to deliver the training course.

Given the nature of today’s adult learner, at any age, having a myriad of technologies available to aid in learning and acquiring knowledge, i.e. the Internet, the approach to this training will tap heavily into educational technologies accessible via the Internet.  These will afford both learner and educator to optimize time spent in the classroom by having immediate information available and resources to facilitate learning much beyond a traditional textbook and lab manual.  The reach to like-minded individuals who can aid in student learning via the Internet through blogging, tweeting, forums, special interest groups, professional organizations, publications and other resources freely accessible via the Internet will prove invaluable to the course design and success (Lubefeld, 2017).

The rationale for using both onsite and web based instruction for this course speaks to the need that older adults have for a feeling of community.  The traditional meeting, information gathering and communication methods of older adults can be extended to cyberspace in addition to the adoption of new literacies in this population.  There are strong positive associations between the use of Internet-based communications and a strong sense of community and well-being among adult learners (Sum, 2009).

 

Learning Content for PC Technician course (maps to CompTIA A+):

  • Assemble components based on customer requirements
  • Install, configure and maintain devices, PCs and software for end users
  • Understand the basics of networking and security/forensics
  • Properly and safely diagnose, resolve and document common hardware and software issues
  • Apply troubleshooting skills
  • Provide appropriate customer support
  • Understand the basics of virtualization, desktop imaging and deployment

Design & Implementation

The IT training courses will be 5 weeks in length (3 per traditional college semester), 8 per year for a total of 40 weeks, and will be scalable.  The courses will be delivered using open source tools such as Moodle, Atutor, Eliademy, ILIAS and OLAT.  The coursework will be hybrid, providing a face-to-face instructor as well as an LMS with assignments throughout the week.  Other educational technologies will be utilized such as social media, blogging, mobile learning and online video based training.  Utilizing open source LMS, as well as open documentation (i.e. Wikipedia and other reference materials) which features useful interfaces, multimedia support, mobile-friendly support and community-based learning provides a rich and economical approach to teaching community based adult education courses (Pappas, 2017).

Since many of the target audience may work low-paying retail or food service jobs, they will need a schedule to support evening and weekend availability.  Therefore, the course timeline will be structured to be offered for 4 hours once a week, M-F 6-10, or Saturday from 8-12 and 1-5.  It will have some aspects of boot camps already established, and may refer to content in openly available MOOCs.

The course will utilize facilities either provided through public education venues such as K-12 rooms after hours, within community colleges, or in spaces available from corporate donors.  The learning environments will resemble traditional adult education classrooms, with additional online components necessary to assimilate knowledge during the time between class meetings.  There will be group-based and active learning activities to cover objectives, using such things as flash cards (paper or electronic), hands-on labs breakdown of computers, laptops and other computer systems.  The aim of the training is to simulate real-world environments so the resources would include realistic scenario’s using modern systems, networks and software.

Evaluation Procedures/Assessment

Informal assessments will be administered regularly in order to measure student learning.  The expectation is that some students will succeed at a particular certification, and others will choose different learning paths, perhaps to a less involved certification.

Since the training will be mapped to large certifiable content through CompTIA, the courses may piecemeal the content into several sections.  For example, splitting the A+ (Hardware and Operating Systems) into 10 modules which can be taken in sequence.  Beyond the 10-module sequence for A+, there will be advanced courses with multiple modules, covering networking, computer security and cybersecurity.

Communications and Marketing Plan

The coursework will be published on a website and posted in various local publications, as well as on bulletin boards at companies which the target audience may be employed.  There will be active communication and solicitation to the community with the message that economic development can be furthered for those that participate in the program.  Partnerships will be formed with manufacturing companies, community colleges, job placement organizations, local governments and municipalities, churches and other religious organizations, and other public areas (supermarkets, etc.).  Direct mail can also be used to gain participants.

 

 

Budget

Estimated Costs

Start-Up Costs:  $12,000.00

  • 12 seats per venue x $1000 per seat, including computer workstations, tools, breakdown computers.
  • The workstations should be portable so they can be popped-up anywhere for training to commence.

On-Going Costs Per Session:  $4,800.00

  • $20.00 per pupil contact hour. Limiting courses to 12 students x 4 hours per week x 5 weeks.  This will cover the books, materials, online/software based resources, venue, and instructional costs.
  • Cost per student/session will be $400.00
  • Trainer Cost Per Session:                $2000.00
  • Materials Cost Per Session:            $1600.00
  • Facilities Cost Per Session: $1200.00

Annual Cost for Six Sessions

  • 1 Course Per Session (including start-up): $40,800.00
  • Annually there will be six sessions, at least 1 course per venue

 

 

References:

Combéfis, S., Bibal, A., & Van Roy, P. (2014). Recasting a Traditional Course into a MOOC by Means of a SPOC. Proceedings of the European MOOCs Stakeholders Summit, 205-208.

CompTIA Certifications. (n.d.). Retrieved August 01, 2017, from https://certification.comptia.org/certifications

CompTIA A. (n.d.). Retrieved July 29, 2017, from https://certification.comptia.org/certifications/a

Ellithorpe, J. O. (2016). The Role and Impact of Cyber Security Mentoring(Doctoral dissertation, Walden University).

Fogg, N., & Harrington, P. (2011). Increased Presence of Older Workers in the Massachusetts Labor Market: Implications for Workforce Development Policies, Workplace Accommodation, and Universal Design.

Howley, C. Serving Displaced Workers: A Rural Community College Initiative.

​Jill Jusko | May 14, 2012. (2014, January 08). Training the Manufacturing Workforce: Don’t Go It Alone. Retrieved August 04, 2017, from http://www.industryweek.com/workforce-training

Lerman, R., Kramer, F., & Pedroza, J. (2008). Retrospective on Registered Apprenticeship: A Review of Program Initiatives and Their Policy Implications. A Report to the Office of Apprenticeship, US Department of Labor. Washington, DC: Urban Institute.

Lubelfeld, M., & Polyak, N. (2017). The Unlearning Leader: Leading for Tomorrow’s Schools Today. Rowman & Littlefield.

Merriam, S. B., & Bierema, L. L. (2015).  Adult learning: linking theory and practice.

https://certification.comptia.org/docs/default-source/exam-objectives/comptia-a-220-901-exam-objectives.pdf

Pappas, C. (2017, August 02). The Top 8 Open Source Learning Management Systems. Retrieved August 10, 2017, from https://elearningindustry.com/top-open-source-learning-management-systems

Sum, S., Mathews, R. M., Pourghasem, M., & Hughes, I. (2009). Internet use as a predictor of sense of community in older people. CyberPsychology & Behavior, 12(2), 235-239.

 

Educational Technology Infrastructure

Educational Technology Infrastructure

By Dan Grigoletti and Derek George

May 2, 2017

Abstract

Educational institutions today are faced with many challenges in establishing, developing and maintaining technology infrastructure within their organizations in support of student learning.  As educational organizations have great responsibility in educating all levels of student for future occupations and academic careers, foundational technologies need to be examined and researched to find ways to improve upon them and to find solutions to issues such as data privacy and security, bring your own device (BYOD), educational technology policy, and many other areas.  Fundamental to public, private, K-12 higher education, and corporate training and development is producing effective and sustainable educational technology infrastructures. There is a need for a thorough understanding of the major issues, factors, and variables that influence educational stakeholders to use and/or implement technology for maintaining and sustaining services to educators and learners.  The U.S. Department of Education (USDOE), and their technology arm (USDOE EdTech) has published a multi-faceted, practical framework for examining tools, technologies, policies, resources and solutions to challenges facing educational organizations.  This paper seeks to identify and expose the issues and propose solutions available for building and maintaining effective educational technology infrastructures. The elements involved in an educational technology infrastructure system are complex, interrelated and interdependent.  By starting with the established framework from the USDOE, a roadmap can be drafted and followed to address each of the areas. It is imperative that the stakeholders in educational technology be provided with thorough information in order to design new systems and find solutions and applications of technology to serve as the modern digital infrastructure for educational systems.  By doing so, our educators will have a sound, reliable and sustainable foundation to build new pedagogical, teaching and learning processes. In addition, by addressing the leadership challenges and finding better ways for educational leadership to make decisions about EdTech infrastructure, the programs and processes can be made more effective in terms of the ROI to educators, students and the public.

 

Outline

 

  1. Overview
  2. High-Speed Connectivity
    1. Broadband
    2. SETDA Recommendations
  3. Data Privacy & Security
    1. FERPA Regulatory Considerations
    2. Types of Security (i..e Physical, Network, Data)
    3. Open Source and Proprietary Considerations
  4. High-Speed WiFi Throughout Schools
  5. High-Quality, Low-Cost Devices
    1. Use of open hardware solutions: Chromebooks
    2. Mobile Computing
  6. Home Internet Access
    1. USDOE
    2. National Educational Technology Plan (NETP)
  7. Digital Citizenship & Responsible Use
    1. Responsible Use Policy (RUP)
    2. Consortium for School Networking (CoSN)
  8. Quality Digital Content & Resources
    1. Use of Learning Objects
    2. LOR (Learning Object Repositories)
    3. OER (Open Educational Resources)
  9. Leadership
    1. Collaborative Leadership Among Stakeholders
    2. Vision for the Future of EdTech Infrastructure
  10. Teaching
    1. Technology Learning Tools in the Classroom
    2. Use of Internal Resources vs. External Curricula Resources
  11. Assessment
    1. Traditional
    2. Next Generation

 

Overview

 

We will discuss applications of infrastructure technology which can be applied to either primary, secondary and/or tertiary educational institutions.  Some of the key infrastructure topics which will be discussed include:

 

  • Data Privacy & Security
  • High-Quality/Low-Cost Devices
  • Maintaining the quality of digital content.
  • High-Speed Connectivity (including Internet, Mobile and WiFi)
  • Digital Citizenship & Responsible Use

 

We will discuss these in the context of what’s best for teaching and learning, emerging research, and current literature related to infrastructure designs, development and implementations, as well as assessment of effectiveness of this educational technology.

 

In addition, we will explain and examine the components, usage, current issues and future considerations of infrastructure for educational technology, focusing on the framework which the U.S. Department of Education provides, outlining the tools and technology available for building effective educational technology infrastructures systems.  Topics within the subject of infrastructure will apply to primary, secondary and tertiary educational institutions, and include high-speed connectivity (including Internet, mobile and WiFi), data privacy & security, high-quality/low-cost devices, digital citizenship & responsible use, and maintaining the quality of digital content. We will present this in the context of what’s best for teaching and learning, including current literature related to infrastructure designs, development and implementations, as well as assessment of effectiveness of this educational technology.

 

We examined the resources that a university or K-12 school district provides on sight, but also identified issues of offsite resources, which may or may not be considered part of the core infrastructure within an educational environment.  One of the areas that we find infrastructure components like network access (Internet), hardware and software is especially acute, is when we examined the digital divide. Many students face challenges utilizing technology at home because of their socioeconomic standing.  The lack of Internet access or equipment at home to adequately provide the tools needed to complete homework assignments, affects many students success in the classroom. This could happen in K-12 environments, but also may be involved with college students who commute to universities or community colleges and may not have adequate resources to compete with students that have equipment at home.  For example, if a student doesn’t have Internet access, they may be challenged at completing computer-based homework, unable to be responsive to discussion threads, and at a disadvantage when working on group collaborative assignments through email and other asynchronous communication methods. Or, they simply may have to use the campus computers to complete work, taking them away from home responsibilities (The Homework Gap, 2017).

 

We have discovered that there is a trend toward cloud based infrastructure.  In examining the various technologies, we find that cloud computing is really the virtualization of infrastructure, essentially data centers connected via the Internet.  This new model enables educational institutions and educators to access Internet-based computer and data resources which are available on demand. This shift benefits schools because they save on infrastructure costs of servers, enabling the school to focus even more on educating students instead of being bogged down with infrastructure concerns.  The schools can also take advantage of the scalability of resources per their needs (Infrastructure, 2017).

 

There are many issues that arise with regard to data privacy and computer security in educational environments.  For example, there may be gaps in security training of teachers and staff between the optimal understanding of security threats and the appropriate skills needed to establish and enforce good security policy.  The issue of preventing identity theft and abuse must be addressed to ensure identity protection of students when using internal or external computer resources. Mobile computing using various devices on premise or off can also provide a security risk if the devices are used on open networks, or if the devices is lost or stolen while containing personal and confidential information.  Since students are extremely involved in social networks, the risk of personal and private information being recorded, stolen or abused is high. Students may not use appropriate security mechanisms when using informal and casual social media sites. There are also concerns that when using computing devices over networks that are not locked down to prevent inadvertent download of malware, especially when devices are removed from the premises and operate on open networks.  There are many vulnerabilities that hackers can take advantage of either on wired or wireless networks. In addition, opportunistic individuals can take advantage of younger and less experienced students through deception in the form of social engineering. And, of course, as with users in any environment, there may not be enforcement in educational environments of the use of adequately strong passwords. One possible solution is to use Chromebooks, which are highly secure, reducing computer security expenditures on such things as virus protection.  They enables higher compliance on privacy and offering IT departments in schools many advantages.

 

High Speed Connectivity

 

Central to educational technology infrastructure’s effectiveness is high speed Internet connectivity.  Primary and secondary schools, in order to leverage the power of the Internet for extending content and facilitating learning, need access to high-speed Internet.  The manifestation of high-speed Internet comes in the form of broadband technologies (as opposed to baseband) which include internal networks running Ethernet technologies, and other wireless technologies such as cable modems, DSL and mobile/cell networks.  Broadband is a crucial component necessary in educational environments.  Data service in the form of broadband technology is essentially a new utility, joining water, HVAC, power, and gas infrastructures (all having a “network” of pipes, wires, etc. for delivery) as the new networked resource needed not only in business but in public and private sector organizations, including schools.  The same tools and resources that have transformed our personal, civic and professional lives must be part of learning experiences within educational settings, intended to prepare today’s students for college and careers (The Broadband Imperative, 2017).

 

We are in the midst of the information and telecommunications revolution (c. 1985-present), joining the revolutions the world has undergone historically such as from 1600–1740 (agricultural revolution), 1780–1840 (the industrial revolution), 1870–1920 (the 2nd industrial revolution, or technical revolution), 1940–1970 (the scientific-technical revolution) (Technological Revolution, 2017).  Therefore, by definition, fundamental changes in the organizational structures (data communications) is taking place in a relatively short period of time, and is affecting all aspects of modern society.

 

The table below identifies the State Educational Technology Directors Association’s (SETDA) recommendation for Internet connection speeds between the ISP (Internet Service Provider), educators and students within school districts and among schools within a particular district.  (Fox, 2012). Internet connectivity supporting communications in educational institutions has been one of the missions of the SETDA organization.

 

 

 

There are many resources and organizations that attempt to establish standards and policies for educational technology, including SETDA.  Another objective of SETDA is to help train and develop individuals for leadership roles in order to design, develop and implement better educational technology in our schools.  

 

As a not-for-profit membership association, SETDA has established a number of priorities for 2017-2020.  The key priorities of SETDA are 1) Advocacy for educational technology policies and practices; 2) Assisting states to take action on improving overall educational technology;  3) Forming strategic partnerships with various organizations in order to improve educational technology; 4) Providing EdTech PD opportunities for the membership; 5) Maintaining open communications among the stakeholders in educational technology; and 6) Serving as a resource for planning and making policy on operational issues in educational technology.  Their focus is to improve educational technology for teaching, learning, and school operations.  

 

SETDA members seek to build and increase the capacity of state and national leaders to improve education through technology policy and practice.  In carrying out this mission, SETDA is committed to serving the states and territories within the US. They have set out to maintain a future-focused, holistic view on how to leverage technology for education, and foster collaborative, strategic partnerships with education leaders and policymakers throughout the country.  As a 3rd party organization, SETDA members are making efforts to address and solve complex issues facing public educational systems in the US (SETDA, 2017).

 

The eRate program (also called the universal service Schools and Libraries Program) from the USDOE is a leading resource to bring Internet-based educational technology infrastructure into US schools.  The USDOE’s Office of Nonpublic Education offers assistance in the form of funding to school systems and provides significant discounts to assist eligible schools and libraries in the US to obtain affordable telecommunications technology.  They also partner with leading telecommunication firms such as AT&T to help school districts develop and their educational technology infrastructures.

 

The table below outlines some of the data provided in the FCC 470: 2017 database for eRate engagement of school districts, by state.

 

STATE NUMBER OF SCHOOL DISTRICTS ENROLLED
FL 288
AK 40
NY 526
TX 945
MI 372

 

Additional categories of education technology infrastructure support are logged in the FCC 470 database as follows:
 

  1. Basic Maintenance of Internal Connections
  2. Internal Connections
  3. Internet Access and/or telecommunications
  4. Managed Internal Broadband Services

 

Another USDOE partner, which is assisting with addressing deficits in the US school systems regarding educational technology, is the Universal Service Administrative Company. (USAC), a non-profit corporation which the FCC has designated to administer the $10 billion Universal Service Fund, collecting and delivering funding to schools for broadband and connectivity needs.

 

Data Privacy & Security

 

The proliferation of educational multimedia content, especially video, in education, has amplified the need for high capacity storage systems.  It is becoming increasingly critical for educational institutions to focus on improving security and privacy. These are prime concerns because the amount of data being generated opens up more points of vulnerability to hacking and discovery by users with malicious intent.  Security in educational environments is especially sensitive because of the nature of the data which may refer to minors in K-12, for example, and the regulations on educational data usage and dissemination such as outlined in FERPA.  Hardware and software have been the traditional costs for computing resources, but now we find data being a most valuable component of systems in education.  Data, if exposed, could be gerous in the wrong hands, from student grade records, scholarship records, employee payroll records and benefits data, educational curriculum and assessment data, to records of graduations, degrees and enrollments.

If sensitive data were compromised or lost in educational environments, for example, the data provided as responses to surveys, forms that are filled out when registering for free software or services, information gleaned from social networks, bots, search terms, usage data, location data, and many others can form a profile of a student, which can then be used in malicious ways to target them.  This could be simply nuisance type of invasiveness (as with spam), or truly harmful as with a stalking situation or targeting gullible students with phishing scams. Private data in many forms (student records and financial information such as credit card numbers) needs to be stored and secured in closed (proprietary) systems behind network firewalls to protect it. In addition, much of the subject matter and content, in the form of lectures and lessons (such as the LMS contents) may be stored in the cloud as proprietary data, and if hacked, the results could be catastrophic.  However, if a school pays for an LMS service like Blackboard, the security and protection onice is on the corporation. Furthermore, since university and community college systems and K-12 school districts have large centralized data storage, they may become targets for hackers. The theft of data itself may be more lucrative to hackers than the computer systems and networks at brick-and-mortar, on-ground educational institutions. (NCES 98-297, 2017).

Data security involves the technical and physical requirements that protect against unauthorized entry into a data system and helps maintain the integrity of data. Data privacy is about data confidentiality and the rights of the individual whom the data involve, how the data are used and with whom data can legally be shared (SREB, 2017).

Schools need to follow the FERPA (Family Educational Rights and Privacy Act), which provides guidance to school systems to protect student privacy in educational environments.  This includes the use of a variety of records stored electronically through software and accessible using stationary and mobile computing devices that access the Internet. Schools, in order to comply with FERPA, must have high levels of security policies and practices in place to protect students and other stakeholders (parents, teachers, administrator).  The security infrastructure in which educational technology exists is evolving, utilizing IT resources to keep administrative records, and systems such as LMS with user authentication and password controls in place. There are many categories of information that require different levels of security to access and protect. High degrees of security are necessary for personal data.  Usually, for best security, multiple layers of security are employed such as physical security, backups, encryption, multi-site storage, network authentication and others.

 

One of the primary concerns and biggest challenges for K-12 and Higher Education schools is student safety in all forms.  Security and safety go hand in hand, from physical security to data security. Since technology permeates all aspects education, the ISTE has made both ensuring security and enabling productivity in educational environments a key concern.

 

Many institutions utilize open systems to store and share content.   There is much debate whether or not open-source (Such as Linux, Google Chrome, and the is better than closed-source (such as Microsoft Windows and other Microsoft applications).  For example, the open nature of the Internet, with more sharing of identities through social media sites complicates matters, where hackers may be able to learn ancillary information about students through data mining and reading feeds from social media sources.  

 

Essentially,  educators need to learn best practices for protecting their privacy and data through resources that are available from online sources.  We should make it a priority to protect confidential student records. A close adherence to the regulations in FERPA, and maintaining compliance with COPPA will go a long way to ensuring a safer educational environment, which can also contribute to unimpeded learning.  (Protecting Your Students’ Data and Privacy, 2017).

 

High Speed and WiFi Throughout Schools

 

On its Education Technology website, the USDOE providers several case studies that exemplify how high speed internet and WiFi infrastructure have been brought to and throughout school districts. Appearing below are three (3) particularly compelling examples that show examples of successful implementations of high speed infrastructure and Wifi being being built out throughout schools.  Both of the districts had predominantly low income populations in geographically disparate areas without an existing high speed Internet infrastructure.

 

Case #1:  Starting in 2009-10 Oklahoma’s Choctaw Native American Tribe partnered with the PIne Telephone service provider applying for and winning $56 million in American Reinvestment and Recovery grants. The money (part of a public, private collaborative effort paid for the successful installation high speed internet infrastructure that connected 10 unserved Choctaw communities. This is a significant demonstration of the tremendous value of Technology infrastructure to a community that was not just underserved but not served at all. The case study reports that, “Prior to this investment, the Choctaw Nation Tribal Area lacked access to reliable broadband service. The low population density (8.3 to 19.7 people per square mile), the high poverty rate (25 percent of the population below the poverty line), and the rugged terrain made the economics of broadband infrastructure very challenging. Initial capital costs to deploy broadband meant that broadband service was limited to commercially viable areas.”  Inside the Choctaw nation, the Broken Bow School District has managed to bring its local internet infrastructure to the point where it can deliver a deliver robust IDT education to it students. The Broken Bow Distract, “ has been able to use digital devices, online lesson plans, and supplemental online programming.” Infrastructure (n.d.). Retrieved May 01, 2017, from https://tech.ed.gov/netp/infrastructure/

 

Case #2:  Since 2013 in San Antonio, Texas  “BibloTech”, an all-digital public library which is accredited as a state library has been making significant inroads to provide access to educational content for underserved communities leveraging the mobility and drastically reduced physical space requirements associated with a library connected to the internet with a sufficient internet infrastructure connection. The case study does not go into any technical detail about which form of connectivity allows the “BiblioTech” to function as it does but it instead it references the fact that since, “BiblioTech branches require only 2,100 square feet of space, the library is able to co-locate within local public housing developments to put resources and connectivity within reach of patrons who might otherwise be cut off from its collections.” Infrastructure (n.d.). Retrieved May 01, 2017, from https://tech.ed.gov/netp/infrastructure/

 

Case #3:  The Coachella Valley, Unified School District, California, K-12. had a similar problem in that there was no provision for high-speed wide area Internet access  for students who were part of the 1:1 device distribution plan originated by the school district. The solution was to outfit the district’s school bus fleet with wifi routers and park them in areas around the community so as to create a mobile network overnight allowing students who could not normally connect to the internet at home the opportunity to do so. This case study is significant because the unusual yet technologically sound concept of creating a model Wide Area network has resulted in the school district going on to develop, “a long-term plan for the district to become its own Internet service provider, breaking its dependence on commercial telecom companies.” This is the sort of novel thinking that both provides for the immediate provision of high-speed internet access both at school and at home and is forward thinking enough to make management of the expense associated with maintaining the infrastructure as inexpensive as possible which is essential  for institutions with tight budgets such as most public schools. Infrastructure (n.d.). Retrieved May 01, 2017, from https://tech.ed.gov/netp/infrastructure/

 

Another useful resource that can be utilized to measure educational technology in K-12 is the USDOE’s School Speed Test website.  It provides an interesting tool by which reports revealing information in the effort to assess the adequacy (speed) of the Internet connection serving a given school.

 

Results can vary depending on the the time of day, the location from which the test is conducted and the other variables but interesting differences come to light after conducting tests for just a handful of  for Schools. Here are some test results:

 

School Name Location Type  Results
Brooklyn Tech High School Brooklyn, NY H.S 7.24 D; 5.5 U
New York University New York, NY University 53.72 D; 9.19 U*
Central Michigan University Mt. Pleasant, MI University 19.93 D; 5.8 U
Duke University Durham, NC University 85.13 D; 10.04 U*
CC Spaulding Elementary Durham, NC K12 13.17 D; 6.94 U
M.I.T Cambridge, MA University 22.02 D; 4.8 U
Spelman College Atlanta, GA University 7.96 D; 5.07 U

 

The table below can be analyzed in many ways. It is presented here in an effort to illustrate the differences in download speeds that exist between public k-12 schools in Brooklyn, New York and Durham, North Carolina (Brooklyn Tech and CC Spaulding) and between CC Spaulding and Duke University (within 30 minutes of each other in Durham,North Carolina) and between MIT in Cambridge, MA and Spelman College In Atlanta, GA (A University with an international reputation as a leading engineering research facility versus a school with more modest reputation) THis tables makes clear the vast differences which exist in the type of technology infrastructure which supports schools and the communities they are situated in.

 

High-Quality, Low-Cost Devices

 

As an infrastructure technology, devices to access resources on the Internet including technical, social and educational communities can be leveraged by educational stakeholders.  An example of a browser based laptop is the Chromebook, which runs Chrome OS rather than Windows or OSX, and takes advantage of the client-server model of program execution, accessing applications running on servers located on the Internet.  In addition, there is a steady stream of new mobile devices entering our educational organizations. The BYOD (Bring Your Own Device) movement that has infiltrated corporate environments, is now prominent in educational institutions.  We can leverage the BYOD movement with cloud applications that are browser based, therefore do not require homogenous devices to run.  However, BYOD poses a management challenge in that Mobile Device Management (MDM) requires additional IT resources, hence expense.

 

The Google Chrome based devices (Chromebooks) can be used for student learning and teacher productivity through using Internet-based apps such as G-Suite from Google.  They are cost-effective for economically challenged educational environments that need solutions to provide students to access Internet-based curriculum and online coursework.  ChromeOS is free, provided by Google, so the cost factor for educational software is lowered for schools adopting this technology.  Also, the hardware itself is low cost and is optimized for Internet access, efficiently taking advantage of the Cloud-based applications.  Chromebooks are highly secure offering a great advantage to educational institutions, reducing computer security expenditures on such things as virus protection, enabling higher compliance on privacy and offering IT departments in many advantages:  “The devices are stateless, so any updates needed come from the cloud. It takes all that stress and time away from the IT staff” (Parallels, 2017). However, one disadvantage of Chromebooks is that they rely on constant Internet connectivity, but some applications can be used offline, with the data being synchronized when the system becomes re-connected.

 

Chromebooks can be important as an infrastructure component for schools.  The nature of a Chromebook is as a “client” as opposed to “server,” which fits Chromebooks into the client-server computing model.  The client model enables accessibility to the Internet by students and teachers (O’Donnell & Perry, 2013).  The infrastructure of Chromebooks along with WiFi networks with Internet access in schools provides connectedness which lays the groundwork to support ISTE standard, enabling educators and students far-reaching access to applications and data for creative use.  ChromeBooks, as a platform, may increase student-to-student and student-to-teacher communication and collaboration through wider access to connectivity and community building activities.  Chromebooks can also enable educators another option for conducting research and information fluency through access of online libraries and databases. As a low-cost option for equipping students with computers, Chromebooks are appealing, but not the only solution.  Windows computers are also available in low-cost implementations since they utilize similar hardware, and may be referred to as Netbooks.  

 

Teachers and educational institutions can benefit greatly from Chromebooks since the costs are low and they are easy to use.  Teachers and schools are adopting Chromebooks at a high rate, since they are easy to use, fast, and are less complex than Windows-based computers. (Parallels, 2017).  Educators can take advantage of features of devices like Chromebooks, and that align with ISTE standards. For example, assessing data within learning environments and pertaining to student achievement may be easier when utilizing standardized devices.  Also, the ease of access to numerous applications (usually termed apps) through online app stores provide opportunities for educators and students to find new software that fits with their learning objectives. Having ready to use and low-cost, mobile devices available to students both in and out of the classroom, can accelerate the movement to flipping lessons for more effective in-class learning, enabling the teacher to facilitate rather than lecture or try to broadcast content during valuable classroom time.  In addition, the standardized hardware infrastructure platforms like Chromebooks direct students and teachers into a mindset of sharing and collaboration with such tools as Google Drive, the Google+ social media site, Google Classroom, Gmail, and other Google technologies.  Finally, the deployment and use of Chromebooks other Internet-connected devices can be provided to every student, leaving no disparity among the socioeconomic characteristics within a classroom.  They also will enable all students to participate in open educational resources beyond just eBooks, such as MOOC’s like Khan Academy, tutorial sites, Wikipedia, and other Internet based repositories of content (Google in Education, 2017).  The Open Distance Learning (ODL) models and solutions provide an open, secure platform for equipping K-12 and higher education students with cost-effective computers to access the Internet.  They also support Self-Regulated Learning (SRL) which is a strong predictor of academic achievement (Kirmizi 2015).

 

Equipping students with a standardized, accessible, open system for utilizing the Internet also supports self-regulated learning (SRL), providing self-efficacy, and empowering students to acquire knowledge through community, then interact, organize, and reflect on their formed knowledge (Bandura 2001).  Millennial students tend to be computer platform agnostic, and not partial to a particular operating system (like OSX, Windows or Linux) or computer configuration (tablet, laptop, smartphone, netbook, desktop, etc.), and simply need access to the applications and information on the Internet in an open way, preferring the things that matter most such as immediate social community engagement, interactivity, digital literacies, connectivity, experiential learning, and teamwork (Oblinger, D., & Oblinger, 2005).

 

The ChromeBook technology is continually refined through advancements in hardware technology and improvements to the Chrome OS.  It takes advantage of the Open Source Community bringing together software developers from around the world to contribute their skills to producing software which is the best it can be. The critical mass, collective activity and aggregate effort to keep improving upon it, makes the Chromebook a superb quality product, which enables widespread adoption by educators, hence providing another learning tool for students .  (Granovetter, 1978).

 

Home Internet Access

 

The USDOE Office of Technology addresses the critical importance of home Internet access in the infrastructure section of their National Educational Technology Plan (NETP).  Home Internet access appears in the NETP infrastructure section.  This highlights the essential nature of Home Internet Access for students, since learning can be continued outside of the classroom, when students go home.  If students do not have access to the Internet at home, they are at a disadvantage. This “digital divide” has become an issue in K-12 education, and should be addressed when educational technology leadership designs a technology infrastructure.   

 

According to a report from the Council of Economic Advisers, approximately 55 percent of low-income children under the age of 10 in the United States lack Internet access at home.  The not-for-profit group called everyoneon,  reports that 1 in 4 households in the US is without internet access.  Also, data from The Pew organization reports fairly consistent adoption of broadband technology generally in the US but class and income differences make a difference in Internet access in the US.  The research from these organizations have assessed the level of Internet access and use by students at home highlights the concept of Disproportionate Internet Access. This phenomenon occurs largely for students in low-income and minority communities, since these students are somewhat isolated from many of the digital communities necessary to aid students in social scholarship.  The awareness generated by these studies and research can go a long way to help alleviate the problem of the digital divide. If Internet access is propagated to lower income areas, students in those conditions can more freely access information and participate in e-learning opportunities (such as online coursework, MOOCs, tutorial sites, YouTube videos, social networks and many other sites and tools that can contribute to their education, which classmates already do.  Ultimately home Internet access is the means by which the “digital divide” issue is most likely to be  addressed.

Get Connected. (n.d.). Retrieved May 01, 2017, from http://everyoneon.org/ 

 

Digital Citizenship & Responsible Use

 

All of the concerns that the USDOE raise on educational technology are interrelated, so a discussion of one really needs to show the interconnectedness between all aspects of educational infrastructure.  An educational technology infrastructure will be of limited value if processes and procedures that support good digital citizenship and responsible use of systems and the platform as a whole are not taught, encouraged and enforced/enforceable. In fact, it could be argued that the digital citizenship and responsible use training/education dimension of an educational technology program should precede, or at least spin up simultaneously with the educational technology infrastructure because educational technology infrastructure without an effective system for governance is road to nowhere without rules.  

 

There are nine elements of digital citizenship and responsible use.  1) Digital Access (school/home); 2) Digital Rights and Responsibilities; 3) Digital Communication; 4) Digital Literacy; 5) Digital Etiquette; 6) Digital Security (Self-Protection); 7) Digital Health and Wellness; 8) Digital Law; and 9) Digital Commerce.

 

The USDOE refers to Responsible Use Policies (RUP’s), which is a document outlining how computing resources should be used responsibly, and expresses what the consequences should be for misuse.  The document is composed by stakeholders such as parents, students and educators.. They can be used as best practices for school districts that are attempting to adopt, build and/or maintain a best-in-class educational technology infrastructure system.  When schools follow a well-written and effective RUP, they are taking steps to form an environment of success and responsibility for students. They also reinforce the best practices that students and educators should follow to be good digital citizen in today’s increasingly technological society.

 

There is a need in the US to reach underserved students with connectivity resources and Internet access.  The USDOE recommends that Responsible Use Policies should be implemented. When writing these “RUP’s” the USDOE recommends a readable, accessible document that stakeholders such as parents, students and educators can use.  Some important resources that the USDOE recommends to answer questions for administrators responsible for the development of a RUP include 1) Policies for Users of Student Data Checklist 2) The Consortium for School Networking (CoSN); and 3) Rethinking Acceptable Use Policies to Enable Learning: A Guide for School Districts.  

 

The stakeholders need to take ownership of their children’s education and how technology affects it.  Therefore the recommendations frequently include family involvement as well as the educators. They are also sensitive to the diversity of many school districts and recommend translating the policies to other languages.  The policies especially emphasize how schools need to protect students from harmful content on the Internet by good policies and procedures such as monitoring compliance, providing guidance on such things as proper Internet etiquette and behavior so that personally identifiable information (PII) is not at risk.  Other recommendations are that schools should provide students with good access to digital media to support engaged learning.

 

Another resource is the USDOE Privacy Technical Assistance Center (PTAC) .  This is where data security policy and the actual technology meet.  PTAC is a valuable source of information on confidentiality, data privacy and security.  They provide educational materials for families and PD videos for educators on phishing scams, transparency, data breach responses, and best practices in security for K-12 education.  Here is a sample video from their website called Student Privacy 101 which discusses FERPA.

 

Finally, regarding Digital Citizenship, there are many ways we can measure and improve participation.  First, we must find good technology leadership, then develop training programs to educate teachers on being good digital citizens, so they can model this for their students.  It’s part of the culture of an organization to show the stakeholders the level of commitment to digital citizenship. So, the behaviors that the adults exhibit form the normal culture that students will adopt and inherit.  Features of good digital citizenship include good security and safety of the people and systems so that there are not threats to the well-being of the stakeholders. Also, establishing responsible use and ownership of the trappings of technology that are used in educational environments should be encouraged.  When students take responsibility for the implements in their educational experience (laptops, printers, network access, software, etc.), they put a higher value on the technology, become more engaged and communicative and can form better community among their classmates, teachers and the outside world (Ribble, 2004).

 

Quality Digital Content & Resources

 

Public, private organizations and foundations provide repositories called LOR’s (Learning Object Repositories) of open educational resources.  The purpose of these organizations are to maintain quality and consistency, to facilitate the proliferation of reusable digital assets or DLO’s (Digital Learning Objects) which they have accumulated for educational purposes, and to provide robust infrastructures to capture, store, edit, maintain and deliver DLO’s.  DLO’s are comprised of any element that can be reused and is usually packaged to include a lesson, an activity, and an assessment (Oviatt, 2017). Creating and using DLO’s can provide a persistent and accessible set of assets for educators to use to help motivate and engage students as they develop their content. DLO’s should have a stated and specific educational purpose, are reusable and encapsulated or grouped into units, modules, courses, and educational programs (McGreal, 2004).

 

Today, we see a proliferation of these LOR’s.  For example, Blackboard Open Content provides access to a huge storehouse of digital content to use within the LMS.  This provides customized learning designs, enabling collaboration. Collectively, we call this OER, or Open Educational Resources (OER).  Here are some examples of OER’s include OER Commons, UNESCO Open Course Library, and Washington State Open Course Library.  Some examples of digital learning objects (DLO’s) include animations and simulations, digitized course content and assessments, as well as video lectures and lessons followed by discussion opportunities and assessments.  DLO’s are useful since once they are created, they can be reused. They can be made searchable through defining and embedding metadata (data about data) within each one so that they can be identified by search engines, and content management systems.  Typical types of metadata which DLO’s may include are (1) the educational objective which the DLO is instructing; (2) a list of prerequisite skills/objectives required by students before consuming the DLO; (3) The topic area which the DLO is instructing;  (4) the type of interactivity, if any, of the DLO; and (5) which technology is required use or view the DLO (Learning Object, 2017).

 

Leadership in EdTech Infrastructure

 

The US Department of Education has determined that there is an acute need for leadership in the implementation of educational technology at the K-12 school level.   A key factor in developing and implementing new educational technology infrastructure is collaborative leadership, involving all stakeholders in the educational process.  Even though good technology infrastructure is essential to facilitate today’s EdTech, having talented leadership is very important for effective utilization of technology.  Leaders possessing certain leadership attributes and knowledge will affect the successful implementation of EdTech, and in turn contribute to success in teaching and learning outcomes (Anderson, 2005).

 

The goal of developing technologies that facilitate personalized student and professional learning, will require visionary educational leadership to determine the best way technology can be developed and implemented to support learning.  The new leaders should model tolerance for risk and experimentation and create a culture of trust and innovation, excellent communication, and thoughtful strategic plans which affect student learning with educational technology. This will require professional development activities, and, of course, expenditures to support new educational technology initiatives (Leadership, 2017).

 

Teaching with Technology Infrastructure

 

To facilitate the integration of technology into the classroom, educators and institutions need to be equipped with the essential technological infrastructure to serve educator and learner needs.  In addition, schools need institutional resources which serve the needs of all stakeholders in the educational organization. Some common technology infrastructure elements which need to be installed in brick-and-mortar schools, accessible to the onsite classrooms include the network gear (cables, servers, switches, hubs, routers, wireless access points, etc.), general purpose labs (computers could be Linux, Windows and/or Mac), departmental specialized labs, diskless workstations (also called thin clients), file and other types of servers (application, email, web, database, etc.), mobile devices (i.e. Android or other smartphones), projectors, robotic equipment, smart whiteboards, software licenses (for such things as Microsoft applications, and Adobe Suite), subject-related software (i.e. for math, writing, scientific), virtualized environment (such as VMWare Citrix servers), and high-end workstations for specialized applications like CAD (Computer Aided Design) or Game Development.  The main considerations/challenges that are encountered when integrating technology into the classroom involve dealing with 1) Fear of change; 2) Improved training of teachers in basic computer technology; 3) Increased levels of personal (outside of work) usage to become more familiar with student contexts; 4) Which pedagogical models and techniques are utilized; 5) Implementing more learning-based pedagogies; 6) The educational climate; 7) Effective teacher motivation to incorporate new technologies in the classroom , and 8) Providing better support for integrating technology in the classroom (Bitner, 2002).

 

When designing which components to include in educational infrastructure, there are many important characteristics and attributes which the technology should include.  First, the technology for instruction should be in digestible pieces, so keeping the implements accessible and brief in terms of student access is important. Also, utilizing technologies that translate to visual aspects of learning can have a high “bang for the buck.”  Also, facilitating learning through technology infrastructure should include varied and diverse access to resources on the Internet, including video, hypertest, wikis, blogs and LMSs. In addition, educational technology infrastructure components that increase the ability for educators to communicate, connect, and collaborate with students, such as accessible email systems, discussion threads within the LMS, video conferencing systems, and others (which require stable and high bandwidth capabilities) should be present.  Lastly, peer-to-peer tools and technologies that enable engagement among students should be included in the design of the technology educational infrastructure. These design elements overlap, and form scaffolds to learning for K-12, higher education and even corporate learners, but especially for adult learners. Without high quality and thoughtful design of the layers of technology infrastructure for education, implementing half-measures will probably not lead to improved educational outcomes or better student learning

 

Assessment of EdTech Infrastructure

 

Gauging the value of investments made in, and improvements upon educational technology infrastructure most naturally comes through assessments of the students who rely upon and utilize such software as the LMS (Learning Management Systems) and other software tools which are scaffolded upon the educational technology infrastructure. 

 

The US DOE’s Office of Educational Technology National Educational Technology core plan speaks to how the utilization of educational technology improves and accelerates the rate at which valuable information can be ported out of the Educational Technology Infrastructure and utilized by all stakeholders in the system (Students, Teachers, Administrators, Funders and Developers). Referencing the infographic above, the most consistent enhancement, above and beyond the analog system of assessing learning and changes therein, is flexibility and dynamism. These contrast the original system markedly which relied upon a linear, relatively rigid system that applied the same metrics to all students.  The infographics above demonstrates the recommendations of the DOE (Assessment, 2017).

 

When assessing educational processes and systems, we examine activities conducted and performed by the primary agents of educational technology, teachers, and measure their effect on student success. However, in addition, multi-dimensional, multi-faceted assessment activities must be performed in order to bring real insight, measuring rigor and usefulness of the integration of technology in educational settings.  For example, besides seeking the outcomes measured in formative and summative assessment activities are met, we could assess the effectiveness of educational technology professional development and training of teachers, for example. Also, and just as importantly, we could assess how well technology when integrated into the educational environment, can lead to better student learning outcomes. We can collect assessment information for traditional measurements such as feedback, surveys, questionnaires, grade data, etc., and a variety of other well-practiced ways and with methodologies that have been tested.  However, for assessing educational technologies, we have to find other ways to measure their effectiveness on student learning. To assure the effectiveness of evaluation of today’s educational technology, we should design new assessment tools that can be applied to educational technology, just as we have different types of assessment approaches to other elements that affect student learning in educational environments. Having good leadership, systematic planning, rigorous evaluation procedures, and using a project management approach can be strategies to help assess educational infrastructure (Pierson, 2010).

 

Conclusions

 

Educational Technology Infrastructure requires many components, as were discussed in this paper.  These include the hardware and software systems, including high-speed connectivity in the form of wireless or wired technologies.  The study of EdTech Infrastructure also requires examining the technological needs and emerging technologies that can meet these needs in educational environments.  It is not just the hardware and software, but the people, processes and policies that contribute to a sound educational infrastructure. Groups and entities such as SETDA and USDOE have published valuable guides and best-practice recommendations for educational stakeholders in the evaluation, selection and implementation of hardware, software, policies and procedure that constitute current best practices.


The critical issues of data privacy & security can be addressed in many ways such as using secure systems, high quality control and assurance, good technology project management, establishing and enforcing policies which aid in ensuring quality, security and privacy in educational environments.  Some of the challenges to data privacy and security may be addressed through legal memorandum, AUPs, as well as revising and updating policies and procedures as conditions change and new technologies emerge. Some of the other considerations include but are not limited to: 1) Reviewing and updating FERPA regulatory mandates;  2) Paying attention to the level of and adoption of stakeholders in digital citizenship; 3) Seeking out, securing and developing safeguards and privacy of existing hardware system, software and people; and 4) Implementing updated security measures, whether physical or logical, networked or local, data or software related (as in open source vs proprietary software adoption).

 

As with any hardware implementation within an organization, the scope (i.e. whether it is single room, floor, building, campus or metro) and capacity (how many users currently, and how many expected at peak times) should be considered when implementing systems (software or hardware) for educational environments.  For example, the network components should be examined and analyzed so that the correct designs are in place in terms of scope and capacity in such sub components as high-speed WiFi and wired networks, their bandwidths, coverage and costs. These challenges occur throughout any educational environment, including K-12, higher education, or corporate training and development (T&D).  We need to consider not just the universities, schools, districts, but also the level of technology availability in the homes of the students.   In particular, the digital divide, which we can observe is still an issue despite costs of hardware and software being more accessible to families of lower income students.  Solutions have been developed and deployed to address this challenge so that students are all on a level playing field with regard to home computing resources For example, private industry can be tapped to help bridge the gap by providing computing devices that are both high-quality and simultaneously low-cost. Also, the use of open hardware solutions such as Chromebooks and mobile devices can help to bridge the divide, providing a combination of resources provided internally by schools and externally by corporate or charitable donors or community based organizations.

 

When designing which components to include in educational infrastructure, there are many important characteristics and attributes which the technology should include.  First, the technology for instruction should be in digestible pieces, so keeping the implements accessible and brief in terms of student access is important. Also, utilizing technologies that translate to visual aspects of learning can have a high “bang for the buck.”  Also, facilitating learning through technology infrastructure should include varied and diverse access to resources on the Internet, including video, hypertext, wikis, blogs and LMSs. In addition, educational technology infrastructure components that increase the ability for educators to communicate, connect, and collaborate with students, such as accessible email systems, discussion threads within the LMS, video conferencing systems, and others (which require stable and high bandwidth capabilities) should be present.  Lastly, peer-to-peer tools and technologies that enable engagement among students should be included in the design of the technology educational infrastructure. These design elements overlap, and form scaffolds to learning for K-12, higher education and even corporate learners, but especially for adult learners. Without high quality and thoughtful design of the layers of technology infrastructure for education, implementing half-measures will probably not lead to improved educational outcomes or better student learning (Digital Promise, 2016).

 

Infrastructure development for educational environments requires assessment, since it is an essential part of the programs and processes that education students.  Assessment improves learning because it requires a close examination of what is working and what is not. We have a lot of literature available for doing formative and summative assessment on educational units, programs, processes, etc.  However, gauging the effectiveness of EdTech infrastructure can be challenging since it is more of a collective tool for meeting larger educational goals at the institutional level. One way of utilizing traditional assessments like surveys and tests, is to ask about how effective a particular technology was in the learning experience.  We need to select assessment techniques appropriate to the scope of what’s being assessed. We can ask students to reflect on or demonstrate how well a particular technology. We can observe how implementing a new technology like Chromebooks, higher speed Wifi, online augmentations to learning such as utilizing open courseware or MOOCs, and measuring how the level of digital citizenship has contributed to the student’s ability to construct new knowledge (Assessment, 2017).

 

Through developing effective leadership with thoughtful planning of educational technology infrastructure, the assessment process can become more streamlined and adaptable to the infrastructures that are selected, improved upon, or implemented.  The students and teachers become the beneficiaries of a sound, rigorous, secure and capable infrastructure. Through meaningful management and thoughtful decisions on making improvements, leaders can ensure that future investment in educational infrastructure are effective in terms of cost/benefit and outcomes.  Assessing the ancillary technology tools in addition to the core classroom activities and methodologies will make for a comprehensive and holistic examination of the educational environment being examined. So, by including the measurement of not just how teaching affects learning, but also how the increasingly automated and integrated technologies (often times transparent to classroom stakeholders) will enable us to improve overall outcomes. 

 

Ultimately, the examination of educational technology infrastructure ties all the systems, issues, and considerations together including hardware/software, legal/regulatory, cost/disparity, security/privacy, LOR, OER, leadership, and teaching.  All of these areas can be improved iteratively as new technologies emerge and old ones are augmented or replaced. While much of the technology emerging in corporate and consumer settings may seem revolutionary, the adoption of new technology in educational institutions will likely be at a slower, evolutionary pace.

References

 

 

Teaching Computer Science in Blended and Online Modalities

Teaching Computer Science in Blended and Online Modalities

Written by Daniel Grigoletti, December 7, 2016

This paper will synthesize the topics from the EDU811 course with the methods and tools for teaching higher education Computer Science courses in blended and online modalities, how to motivate online programming, and what motivates them to learn, by examining various research and studies on motivation from learning science studies, but specifically focus on online modalities in higher education, and provide insights and important ideas regarding this type of educational experience.  Teaching a technology course online, hybrid or blended modalities requires a unique approach as compared to courses involving non-technological subject matter.  Techniques for motivation can translate from face-to-face to blended and Online learning.  However, within these modalities, there are many differences, expectations by students and methodologies that can be employed by instructors.  Just as with onsite courses, online learning in courses in Engineering and Computer Science must be project-based, which aligns with the expectations in their future workplace.  Student can best prepare for a job in computer programming, by extending their learning beyond the limits of a classroom, whether actual (onsite) or virtual (online).  They need to not only be present in activities that will give them advantages in the workplace, but actively participate in the ancillary characteristics of the field of computer science.

Successful Utilization of Blended Environments and Multimedia to Teach Adult Learners Programming and to Promote Persistence in University Level Coursework

Various activities and mechanisms such as multimedia, SDL, MOOC’s, Course Design, Active Learning and New Literacies can be employed by educators can to teach and develop undergraduate Computer Science students.  These can be effective in promoting persistence and resilience in online and blended environments.  Computer Science learning can be greatly enhanced through enabling and engaging students in multimedia environments, whether publicly available such as video and hypermedia websites on the Internet, as well as the proprietary online textbook-embedded and publisher based self-paced learning modules.  When teaching adult learners, Cross discussed the importance of recognizing characteristics of adult learners, especially their personal characteristics referring to aging, life phases, and developmental stages and their situational characteristics referring to part-time versus full-time and voluntary versus compulsory learning (Cross, 1981).  The online options for Computer Science learners also include MOOC’s which require a great deal of SDL (Self-Directed Learning), and characterized by the theories of andragogy and adult learners.  However, when engaging in MOOCs for any subject including programming, the initial weeks of courses are key for student engagement, and depending upon their engagement level, will affect the attrition rate and how much students access materials and complete assignments (Perna, 2014).   Many educational institutions have rich LMS shells which include lessons for learning programming, embedded videos, animations, and references to resources provided by the instructor or by publishers of Computer Science textbooks.  As stated in the theory of andragogy, when students develop more self-direction and personal control in an activity, they may view it as more enjoyable and interesting.  Individuals take responsibility for their own learning process by determining their needs, setting goals, identifying resources, implementing a plan to meet their goals, and evaluating the outcomes (Knowles, 1980).   Educators can provide and facilitate an environment to enable adult learners to have high level of control over their own education as possible, for example by letting them choose paper and project topics that interest them, enabling them to reflect on their work through customized projects and presentations, giving them the ability to direct their learning.  An online learning experience can be only as good as the structure, content and delivery mechanisms utilized in course design.  Inherently, an online course is loaded with technological components such as discussions, online testing, videos, blogs, email facilities, document repositories, gradebooks.  In addition, a good course has interesting yet challenging, well-integrated lab activities that are achievable without requiring on-ground resources.  Instructors in online environments can employ active teaching and learning to engage students in the material and give them opportunities to achieve a level of mastery.  In addition, educators need to be cognizant of new literacies of the millennial student of Computer Science.  The concept of “New Literacies” include consideration of how “Web 1.0” and “Web 2.0” involve different sets of design patterns and business models in software development, and in concrete examples of how the distinction plays out in real life cases and practices. (Knobel & Lankshear, 2014).  New literacies have emerged largely due to technology and the Internet. They involve shared skills and knowledge for the current generation, in which they leverage ubiquitous technologies to learn, make meaning, and create knowledge in a dynamic and diverse environment.

Instructional Strategies for Motivating Computer Science Students in Higher Education Online Environments

Instructors can employ various strategies in online environments such as promotion of critical thinking skills, modeling positive attributes, providing project-based learning, and enabling collaborative and peer work for learners of Computer Science and programming.  Developing critical Thinking Skills are a huge necessity to enable deep learning, engagement, retention, to analyze other works, then absorb and process criticism from the other students.  Providing various types of criticism are necessary, so instructors should praise in public and provide non judge-mental and constructive criticism in private, and only negate performance rather than the student.  Instructors can model positive approaches to computer problem-solving and demonstrate good behavior and positive energy in interactions with struggling students.  They can also show their passion for the subject, delivering content and with enthusiasm.  In addition, to engage students, the instructor can tie the delivery of content through using references to real-world situations, war stories from IT industry experience, and in a more personal way, getting to know a few personal facts about students such as their hobbies, part-time job, family, city came from, career interests, etc. in order to better connect with them.  In discussion threads, instructors should draw from real-world experience and provide real-world, interesting examples.  They should demonstrate techniques and concepts and be humble about learning when a better or different way to approach a problem is discovered.  They should also collaborate and solicit solutions to problems from students.  When doing group work, students can personalize their interactions, and co-construct knowledge through collaboration. This group orientation factor can be applied to many different areas of an online courses, including threaded discussions and other asynchronous group-oriented communicative instruments such as blogs and wikis.  In addition, by adding peer-collaborative opportunities to an online or blended course, can adds another valuable dimension to active learning, and may help with cognitive processing of the content.  Performing peer code reviews and feedback on programming techniques can enable sharing of ideas, opinions, building of relationships and synergies of thought.  However, this should be coupled to actual instructor feedback otherwise students may become anxious about giving and receiving feedback, concerned about the reliability of the feedback. In addition, students may not be prepared or be comfortable to take on the role of an evaluator (Ertmer, 2007).

Intrinsic/Extrinsic Motivations to Learn Programming in Computer Science

In order for online students to learn programming logic and constructs, coding techniques, they must perform hands-on, project-based lab work because of the complexity and nature of coding in order to internalize and build skills.  This lab work can be accomplished on a personal computer or in a virtual learning environment equipped with the tools for programming including an IDE (Integrated Development Environment) which includes an editor, compiler, debugger, file management, tools for GUI design and development, tools for deployment as well as tools for code management.

A Computer Science student has both intrinsic motivations to learn and create software, and extrinsic motivation to earn a living and become financially independent.  The intrinsic motivation Computer Science students exhibit are a passion and fascination with coding, an outlet for their creativity, and their desire to contribute to solving real-world problems.  which can lead to better success at completing programming projects.  The intrinsic motivations may lead to a student being talented and skilled in programming, and being able to secure well-paying jobs and develop a career with many rewards.  These could be a motivation to work (Herzberg, 1959), a monetary or result in fame when writing or contributing to a successful software application, or be in the form of satisfaction and accomplishment in solving problems.  However, if a student is solely motivated by extrinsic factors, they may not develop the deep learning and skills required to become successful, and become preoccupied with their earning potential or career path.  For example, an aspiring game programmer may love playing games and imagine themselves being the author of the next Halo computer game, but the skillsets required to develop game software is vastly different from that which is needed just to play a game.

Student motivation often depends upon their level of maturity, including age, experience with academic environments, success in school, study skills, as well as having positive experiences with academic professionals.  In addition, students who prepare well and have a positive attitude will do better, even on challenging assignments with more complexity.  According to Kolb’s theory, students with prior knowledge of the subject can engage in the content at a deeper level (Kolb, D 1984).  They apply, synthesize, and evaluate based on Bloom’s taxonomy, reaching higher levels than just memorizing and comprehending content (Bloom, 1956).  However, the continuum of learning styles and motivations show that if a learner lacks motivation and capability to learn Computer Science and programming, they are considered surface learners who simply participate to avoid failure and may be averse to deep learning due to their perceived risks, and performing at a minimum just to pass.  Instructors can help these learners gain confidence in their abilities to perform at higher levels by individualized attention, designing labs that leverages competency and outcome-based learning and enables mastery learning.   Another strategy that educators in online environments can utilize is gamified assignments involving competition.  Educators should encourage surface learners often and help them reflect in an ongoing way, on what they’ve learned and what they’ve accomplished.

Motivation of Computer Science Learners in Face-to-Face vs. Online Learning Environments

Face-to-Face (F2F) instruction enables Computer Science learners to interact and collaborate directly with instructors and fellow students in the on-ground classroom environment.  The immediacy of F2F instruction can be beneficial over online environments because if facilitates sharing of coding techniques and ideas, code walk-throughs, especially for early learners of Computer Science.  The on-ground classroom provides a tactile and nurturing environment, encouraging dynamic interactions among students and instructors.  However, in blended and online environments, the use of synchronous chats could approximate the in-class experience.  In addition, the asynchronous tool, discussion threads, can be used in all three modalities:  onsite, hybrid and online courses.  Asynchronous discussions are an essential part of online courses, and provide a substitute for the in-class face-to-face time spent in onsite courses.  The interaction within discussion forums forms the basis and has to be a substitute for online lectures and instructor interaction.  In addition, discussion forums provide social interaction and feedback to be exchanged from teacher to student, as well as student to student.  Using feedback can be a powerful motivational strategy in online courses, enabling students to self-regulate their performance, confirm prior knowledge and improve cognitive engagement.  People are proactive, aspiring organisms who have a hand in shaping their own lives and the social systems (Bandura 1997).  Feedback in all areas of a course, including discussions, assessments, and collaborative activities must be of high quality.  Motivation to interact and share knowledge in online and hybrid environments leverage from the social connections developed via virtual tools like the asynchronous discussion forum.  Therefore, feedback in all forms is essential to make the course compelling, keep students engaged, accelerating and amplifying learning. Students are used to getting feedback from instructors, but when getting it from peers, then it layers the learning by having a non-expert examine responses, allows sharing of ideas, diverse perspectives, and leads to a more collaborative learning environment rather than a patriarchal model.

Assessment of Computer Science Learners for Program and Workplace Preparation in Online Environments

In order to afford higher education learners in online settings the opportunities perform at a high level and produce high quality work, expectations need to be set clearly, and the courses should leverage the results of formative assessments to adjust and adapt content dynamically.  Educators should set realistic and reasonable performance goals at the assignment/lab level, course level, and program level and help students along the way to meet the goals.  Students should be assessed in a variety of ways including with lab assignments, individual and group projects, quizzes/tests/exams, and through writing assignments.  There should also be appropriate summative assessment to measure the overall performance of the students in the class, as they make their way through an academic undergraduate program, maturing in their knowledge and capabilities in preparation for the workforce.  Students are motivated to achieve high level of fulfillment through gaining and succeeding through their educational experiences.  Maslow’s hierarchy of needs tells us that once people achieve lower-level needs such as safety and physiological requirements have, they seek higher-level motivators such as self-fulfillment (Maslow, 1943).

A student has to have a realistic expectation that their skills will meet the demands of the workforce, and that rewards will only come with hard work and perseverance.  If a student is preoccupied with monetary gain, then they may not focus on developing their skills to a level which can command a large salary.

Improving Computer Science Instructor Skills in the New Teaching Paradigm of Blended and Online Instruction

Instructors of Computer Science need to be on top of a myriad of topics, languages, techniques, frameworks, technologies, software applications, advancements in hardware, and many other aspects in order to teach effectively and with currency.  One way that Computer Science instructors can keep up is by viewing videos on these emerging aspects of Computer Science, and to learn new techniques.   In addition to other professional development techniques such as conference attendance, reading books, and collaborating with colleagues, video training affords Computer Science instructors a voluminous and accessible way to attain training and education.  Video can be an innovative way to teach teachers new technologies especially since websites like YouTube have brought video costs drastically down.  Video and other multimedia modes of learning are extremely accessible and useful for teacher training as well as in onsite and virtual classrooms.  When digital video is integrated with hypermedia, the video can be delivered in a very intuitive manner.

 

 

References

Bandura, A. (1997). Self-efficacy: The exercise of control. New York: Freeman.

Bloom, B. S.; Engelhart, M. D.; Furst, E. J.; Hill, W. H.; Krathwohl, D. R. (1956). Taxonomy of educational objectives: The classification of educational goals. Handbook I: Cognitive domain. New York: David McKay Company.

Brophy, J. E. & Gamoran Sherin, M. (2004). Using video in teacher education. Chapter 1:  NEW PERSPECTIVES ON THE ROLE OF VIDEO IN TEACHER EDUCATION, Amsterdam: JAI.

Cross, K. P. (1981). Adults as learners: Increasing participation and facilitating learning. San Francisco: Jossey-Bass.

Ertmer, P. A., Richardson, J. C., Belland, B., Camin, D., Connolly, P., Coulthard, G., Mong, C. (2007). Using Peer Feedback to Enhance the Quality of Student Online Postings: An Exploratory Study. Journal of Computer-Mediated Communication, 12(2), 412-433. doi:10.1111/j.1083-6101.2007.00331.x

Herzberg, F., Mausner, B., & Snyderman, B. B. (1959). The motivation to work. New York, NY:John Wiley & Sons.

Knobel, M., & Lankshear, C. (2014). Studying new literacies. Journal of Adolescent & Adult Literacy, 57(9), 1-5.

Knowles, M. (1980). The modern practice of adult education: Andragogy versus pedagogy. Rev. and updated ed. Englewood Cliffs, NJ: Cambridge Adult Education.

Kolb, D (1984). Experiential Learning as the Science of Learning and Development. Englewood Cliffs, NJ: Prentice Hall.

Lankshear, C. and Knobel, M. 2006. New Literacies: Everyday Practices and Classroom Learning.  2nd edn. New York: Open University Press.

Lehman, R. M., & Conceição, S. C. (2014). Motivating and retaining online students: Research-based strategies that work. San Francisco, CA: Jossey-Bass, a Wiley brand.

Maslow, A.H. (1943). A theory of human motivation. Psychological Review, 50(4)

Means, B., Bakia, M., & Murphy, R. (2014). Learning online: What research tells us about whether, when and how. New York: Routledge, Taylor & Francis Group.

Motivating Students. (n.d.). Retrieved November 29, 2016, from https://cft.vanderbilt.edu/guides-sub-pages/motivating-students/

Perna, L. W., Ruby, A., Boruch, R. F., Wang, N., Scull, J., Ahmad, S., & Evans, C. (2014). Moving through MOOCs: Understanding the progression of users in massive open online courses. Educational Researcher, 43, 421–432. doi:10.3102/0013189X14562423

 

Differentiating Instruction for Postsecondary Computer Science Students Using a Novel Approach, a Virtualized Digital Learning Ecosystem (VDLE)

Differentiating Instruction for Postsecondary Computer Science Students

Using a Novel Approach, a Virtualized Digital Learning Ecosystem (VDLE)

 

By Daniel Grigoletti for EDU 814

Central Michigan University 11/18/18

 

Within a given program of study, how can we design a system that addresses the true needs of a learner in the context of being prepared with the skills, knowledge and extensible tools to enter the workplace ready for the challenges and inevitable and required growth of the knowledge and skills?  There is a need for finding a modular, scalable and reusable methodology for utilizing technology to teach technology in a differentiated fashion, specifically to target and help motivate the diverse learners in the fast-moving field of Computer Science and Software Engineering instruction.   Learning ecosystems have a great deal of research available, but, the current research primarily addresses incorporating existing systems into the ecosystem.  This paper seeks to apply the concepts of learning ecosystems in a new and exciting way by treating the learning infrastructure in which students are immersed as a proposed virtual digital learning environment (VDLE).   A myriad of technologies are needed and are learned in a Computer Science curriculum.  The theoretical, architectural, programmatic, hardware-oriented, software-oriented and networked systems learned need an approach which intertwines both traditional and emergent technologies and STEM content.  This requires a dynamic methodology.  To differentiate instruction (Boelens, 2018) for Computer Science, a new set of tools should be developed for students to learn, combining the ever-present, but invisible, learning ecosystems which have been drawn upon.  These ecosystems leverage learning technology embedded in Computer Science courses either as software systems (applications, websites, mobile apps, social media) for assessment and CBT, or wider networked implementations utilizing cloud computing coupled with mobile and web based applications.

Some of the common ways to surround the core education experience and personalize it, is to augment learning with other treatments incorporating the adult-oriented theory of self-directed learning through utilization of multimedia/hypermedia resources, incorporating next generation digital learning environments (NGDLE) and learning management systems (LMS).  The research into next-generation digital learning environments (NGDLE) discusses how LMS adoption is peaking in higher education, but new tools are emerging to not just help with administration of learning but to also enable a personalized instructional resource.  The new mobile enabled tools provide better collaboration, communication, accessibility, interactivity, adaptability, interoperability, augmenting or replacing LMS functionality.  With NGDLE tools, personalization of educational experience becomes a natural consequence of using them (Maas, 2016).  The next generation of learning integrates collaborative and social learning (Smidt, 2017), digital citizenship, alternative credentialing, blogs and other community-based collaborative tools, reusable learning objects, MOOC’s, applying innovations such as DGBL (De La Roca, 2018), using computer based PBLs, personalization of digital interactions with students via online/blended instruction and support services such as virtual advising and tutoring (McPherson, 2004) to reach the spectrum of digitally literate Computer Science learners (Davies, 2011) possessing mixed skill and with both traditional and non-traditional backgrounds, and incorporating mobile technologies (Corbeil, 2007) into the higher education Computer Science degree program.  These educational technologies can be coupled with modern technologies used in corporate and personal commerce include cloud computing which provides anytime/anywhere access (Chang, 2018), digital network-based infrastructures, big data which can also be used for academic analytics (Campbell, 2007), and machine learning/AI.  Utilizing a connectivist theory approach (Siemens, 2014) and drawing upon elements of TPACK (Mishra, 2006) and UDL frameworks to help define relationships among ecosystem components empower educators to extend and foster the informal ecosystem into a more scalable and authentic tool to support all Computer Science learners.  This paper will explore how Computer Science educators can operationalize the existing informal learning ecosystems into a concrete implementation, the proposed virtualized digital learning ecosystems (VDLE), by drawing upon existing evidence and research into ecosystems and applying traditional learning theories to help personalize the instruction of software development.

The traditional broadcasting of information approach of teaching Computer Science, despite using various learning/instructional modalities and virtualized delivery modalities such as blended and hybrid (Jordan, 2009), or particular pedagogies, may not be sufficient to differentiate instruction for higher ed Computer Science learners who can benefit from massively personalized project-based learning and DGBL (Van Eck, 2006).  There is a need for educators to leverage components of VDLE’s including the pedagogies/educator approaches (Nilson, 2010), curricula/content, learning science/educational technology, and cultures of learners, to differentiate their instruction to the myriad of abilities and prior professional/workplace knowledge which students have for skill acquisition, extension and retention, despite having been steered by entrance assessments and sequencing through a formal degree program, and create dynamic PLE’s (Malamad, 2017).

The primary goal of the higher education Computer Science learner/degree seeker is to acquire workplace skills and earn IT workforce preparedness while gaining the theoretical foundation of Computer Science knowledge to enable future learning of technology.  The traditional teaching and learning modalities of software engineering involve lecture, analysis and design, and implementation (coding).  However, the new models involve virtualized instruction to extend the traditional lecture, and self-directed learning through investigation and practical learning.  There is a shift to embrace more practitioner teaching of Computer Science, utilizing virtualized, informal educational ecosystems which contribute to individualizing and enhancing how students learn software development skills.  The VLDE operationalizes the STEM ecosystem, contributing to dynamic customization of learning Computer Science.   Postsecondary Computer Science programs, while highly theoretical, now have capabilities to advance practical workplace knowledge & skills through the recognition and implementation of VDLE’s.  A VLDE can simulate workplace scenarios by incorporating, and can immerse students in learning through AI, VR and AR, an environment which approximates the real-world software development practice.

Today’s Computer Science students can utilize social mechanisms and collaborative tools enabled by technology, beyond what was used over the several decades the discipline existed.  The cyclical nature of learning ecosystems epitomizes the connectivist theory in that knowledge is generated through studying and practicing, then it is extended the knowledge and generating new ideas via a network of resources (i.e. the Internet, learning communities in colleges and online in MOOC’s, professional organizations, special interest group, or SIG sites, tutorials.  Each participant filters and refines their own knowledge within a unique context and various “climate” controlled environments, and shares it with the community.  Technology enables software engineers to share information about software engineering, leading to a continuous loop of learning, expanding each time it iterates (Siemens, 2014).

In order to serve the needs of adult learners, VDLE’s can help practitioners apply learning theories in networked educational experiences.  Knowles popularized the art and science of helping adults learn, and using an educational ecosystem helps to embed students into a culture of knowledge.  Some other important components of androgogical (adult learning) approaches should be included, such as new learning environments, digital assessments of skill levels, project-based activities, collaboration, and accessing resources in a self-regulated manner (Knowles, 1980).  Adult learners in higher education Computer Science are geared to take responsibility for their own learning, necessarily being self-directed.  They participate in determining their own needs, seek out digital resources, set their own learning goals, and can assess their own knowledge with regard to learning outcomes.  Each self-directed learner has a different context, situation, needs and vocational selection, hence forming a personalized experience by seeking out new knowledge, extending their prior knowledge using Internet-based resources, community and collaboration with peers.  Post-secondary adult learners are more independent and can design and direct various parts of their learning experience from conception to assessment of their own activities when they pursue the objectives and meet the outcomes of their educational experience (Rutherford, 2018).

 

This also reinforces the trend to credential computer science learners using micro credentials, which measure skills and achievements.  Traditional macro credentials such as diplomas or college degrees have been the means for credentialing and strove to reflect a student’s qualifications, since employers require proof that someone knows what they say they do on their resume.  Digital badges, for example, as a measure of competency can be an important addition to one’s credentials providing another form of assessment of skills and knowledge.  Badges can prove extent of STEM learning wherever it occurs, particularly in networked and interconnected environments.  Credentials (whether traditional or emerging) validate new skills, knowledge, achievements and accomplishments.  The innovative development in education, providing an alternative or adjunct way to measure skills and knowledge gained in formal, informal or non-formal educational experiences.  They can be integrated into a variety of educational content and learning situations such as MOOCs, online learning, blended, hybrid and face-to-face modalities, as well as various types of adult learner institutions, both traditional and non-traditional including community colleges and universities.  Digital badging can portray a learner’s skills and knowledge in social media, blogs, web and mobile based environments so that individuals in their virtual network can recognize and recruit based on the digital presence enhanced by badges.  As another component of a digital ecosystem, badges, implemented as image file with rich information bundled via a hyperlink can serve to authenticate a student’s learning and provide evidence of the knowledge complimenting informal credentials such as instructor recommendation or other endorsement from a third party as is demonstrated in LinkedIn (Grant, 2014).

 

The VLDE as a operationalized learning ecosystem, can provide personalized learning, through providing resources to acquire JIT (Just-in-Time) educational content, enabling self-directed navigation through a differentiated instructional environment, allowing educators to more easily flip the classroom, and provide learner-centered instruction.  The areas that can also benefit are MOOC’s and other alternative learning systems which provide micro credentialing and nanodegrees.  In order to provide ideal conditions for learning, a comprehensive, well-rounded, adaptable, dynamic environment, extending the simpler PLE into a universal theory that allows for virtual instances of customized environments to be generated on-demand, as needs arise, anytime and anywhere.  This virtualization of a learning ecosystem provides reusability and scalability, enabling the instantiation of these modules to address individual learning needs.  To achieve personalized, technology frameworks and platforms need to be leveraged, but the design of the instructional content must be thoughtful and involve a myriad of connected and collaborative tools for learners, including mobile learning, enhanced LMS (next generation).  In addition, since technology tools are incorporated, the ability to dynamically differentiate instructional approaches using a combination of modalities (Wolf, 2010).

The power of a biological ecosystem is that it feeds and provides the environment for living things to grow and thrive.  An educational ecosystem, which is metaphorically derived from the biological version, attempts to simulate the optimal learning environment beyond that which is evident in a classroom or online course.  A learning ecosystem requires a community which can be nurtured and fed from a variety of sources.  In a technological learning ecosystem for computer science, for example, the components include social learning through such things as social networking, MOOC’s, synchronous and asynchronous instructional tools, and student involvement through virtualized active learning (Elliot, 2013).  The ecosystem must be centered around the relationships of people involved in the learning process, necessitating the obvious interactions of student-to-student, student-to-instructor, but extending it to the outside professional/workplace environment in order to add currency, authenticity, relevance, sustainability, etc.  Therefore, it is necessary to foster student-to-professional, instructor-to-professional interactions, encouraging participation in professional activities outside of the classroom, including conferences (virtual or physical), student chapters of professional organizations, mentoring by professionals, internships, class visits, and other involvements with the professional world.  This should start early in a degree program with the introductory coursework in order to align student learning with the expectations of the workplace and to promote lifelong learning.  make meaning from their past experiences through critical self-reflection and through constructing knowledge by forming relationships with others through a process of dialogue can derive both psychological and cognitive growth and development in the learner.  Computer Science learners in higher education need to make sense of their learning experiences in order to develop their skills and knowledge to move to the next level, thus transforming and constructing new perceptions of the discipline and creating new knowledge.  This student paradigm shift coupled with new models of instructional facilitation, can enable learners to undergo personal transformation by utilizing technology augmented reflective, critically creative and innovative approaches.  By doing so, these adult learners are more engaged in the learning process, enabling them to more naturally create new knowledge utilizing a myriad of techniques to focus and develop their prior knowledge into new knowledge (Dirkx, 1998).

 

To put the proposed VLDE into a historical context within educational technology, especially with regard to self-directed learning, we have seen the evolution of immobile computing resources, into the current ubiquity of mobile devices, students can now, unconstrained, instantly access Computer Science educational resources whether provided directly by the instructor, or something that they locate or identify as useful to their studies.  Mobile devices have enabled unpacking of complexities of delivering educational content, enhancing communications, and are forming a new literacy in which educators and learners can communicate instantaneously.  Since much of mobile device usage involves social media, it is natural to explore the pros and cons of how to use this technology for self-directed learning, from both a theoretical and a practical standpoint.  Since adult learners frequently utilize self-directed learning, the inclusion of this vital understanding in an adult learning classroom is essential.  Students can seek out knowledge to extend their existing knowledge from resources that they gather from course materials provided by the instructor, or independently.  In addition, for optimal effectiveness, adult learners need to be in control in the design and implementation of their own learning experiences.  This component of a virtual ecosystem enables students to navigate the complexities and challenges of today’s world through lifelong and continual learning, adult students need to learn to learn, as well as to learn where to obtain relevant and applicable information to their pursuit of knowledge and motivation to apply prior educational experiences and overcome challenges (Manning, 2007).

 

Some of the advantages of a VLDE are student engagement and participation, better collaboration and information sharing among students and between students and teacher.  In addition, social media provides opportunities for students to be more creative by being able to readily exchange ideas and activities with others.  However, social media can also be distracting, and involve security and privacy issues.  Mobile learning in higher education has exploded without the necessity to have the “one-laptop per student” implemented by the institution.  Today, students supply by default, a mobile device in their learning environment, enabling them to participate in the ecosystem.  The Wi-Fi enabled devices enable portability of learning in an anywhere, anytime manner.  Since mobile learning involves not just the content, but the delivery method, there is an increased level and sophistication of communication among learners and educator.  Further, the movement of technology to touch enabled screens has virtually eliminated the need for bulky I/O devices.  This extends the traditional multimedia learning using text, images, audio, video, and animation, to include interactivity in a very accessible manner.  In addition, the research shows that next generation learning has arrived based upon definitions from a decade ago, including new LMS implementations which involve mobile devices more prominently for assessments, collaboration, streaming audio and video, synchronous and asynchronous communication, as well as mature digital communications such as email and text messaging (Traxler, 2009).

Educators are an essential part of a technological learning ecosystem, and a VLDE, for computer science.  In order to be most effective, they need to differentiate instructional practices, and adapt their pedagogical approaches to combine and balance educational theory, learning science and practitioner-based approaches (Merriam, 2013).  Computer Science instructors can leverage both academic technology resources and frameworks outlined by ISTE, as well as incorporating content knowledge and professional resources through IEEE and ACM to nurture the proposed VDLE (Burbaitė, 2018).

Using a VLDE, PLE or learning ecosystem affords us with the ability to leverage emerging technologies for teaching and learning.  Since the pedagogical paradigms are in a shift mode in higher education, we can differentiate our instructional delivery, learner assessment, credentialing, educator professional development, and workplace preparedness for higher education, particularly in STEM and Computer Science learning environments.  Using technologies such as AI, cloud storage, mobile, etc. we gain new ways to reinvent and differentiate our LMS, curriculum, communication, and assessment.   The ecosystem concept contributes a comprehensive look at the educational infrastructure and all the affordances within it.  Naturally, when creating alternatives to the existing systems, which have been tested and relied upon.  However, the loss of old technologies and replacement with new ones requires not only a shift in thinking, but adoption of new learning approaches, recognition of new literacies, and hard work toward a new implementation of postsecondary computer science instruction.  Since the paradigm of traditional college level education has been changing as software technology accelerates there are now many new opportunities to deliver education in innovative and disruptive ways.

 

 

References

Boelens, R., Voet, M., & De Wever, B. (2018). The design of blended learning in response to student diversity in higher education: Instructors’ views and use of differentiated instruction in blended learning. Computers & Education, 120, 197-212.

 

Campbell, J. P., DeBlois, P. B., & Oblinger, D. G. (2007). Academic analytics: A new tool for a new era. EDUCAUSE review, 42(4), 40.

 

Chang, V., Gütl, C., & Ebner, M. (2018). Trends and Opportunities in Online Learning, MOOCs, and Cloud-Based Tools. In Second Handbook of Information Technology in Primary and Secondary Education. Springer International Publishing AG.

 

Corbeil, J. R., & Valdes-Corbeil, M. E. (2007). Are you ready for mobile learning?. Educause Quarterly, 30(2), 51.

 

Davies, R. S. (2011). Understanding Technology Literacy: A Framework for Evaluating Educational Technology Integration. TechTrends, 55(5), 45–52. (PDF)

 

De La Roca, M., Morales, M., Teixeira, A. M., Sagastume, F., Rizzardini, R. H., & Barchino, R. (2018). MOOCs as a Disruptive Innovation to Develop Digital Competence Teaching: A Micromasters Program edX Experience. European Journal of Open, Distance and E-learning, 21(2).

 

Dirkx, J. M. (1998). Transformative learning theory in the practice of adult education: An overview. PAACE journal of lifelong learning, 7, 1-14.

 

Grant, S. (2014). Badges: Show what you know. Young Adult Library Services, 12(2), 28-32.

 

Jordan, B. (2009). Blurring boundaries: The” real” and the” virtual” in hybrid spaces. Human Organization, 181-193.

 

Maas, B., Abel, R., Suess, J., & O’Brien, J. (2016). Next-Generation Digital Learning Environments: Closer Than You Think. Communication présentée au Croosroads where the past meets the future, Thessalo-niki, Grèce. Recuperado de: http://www. eunis. org/eunis2016/wp-content/uploads/sites/8/2016/03/EUNIS2016_paper_4. pdf.

 

Malamed, C., & Wildcats. (2017, May 06). Models For Designing Your Personal Learning Environment.

 

Manning, G. (2007). Self-directed learning: A key component of adult learning theory. Business and Public Administration Studies, 2(2), 104.

 

McPherson, M., & Nunes, M. B. (2004). The role of tutors as a integral part of online learning support. European Journal of Open, Distance and E-learning, 7(1).

 

Mishra, P., & Koehler, M. (2006). Technological pedagogical content knowledge: A new framework for teacher knowledge Teachers College Record, 108(6), 1017–1054.

 

Merriam, S. B., & Bierema, L. L. (2013). Adult learning: Linking theory and practice. John Wiley & Sons.

 

Nilson, L. B. (2010). Teaching at its best: A research-based resource for college instructors. San Francisco, CA:  Jossey-Bass.

 

Siemens, G. (2007). Connectivism: creating a learning ecology in distributed environments. Didactics of microlearning. Concepts,discourses,examples

 

Siemens, G. (2014). Connectivism: A learning theory for the digital age.

 

Smidt, H., Thornton, M., & Abhari, K. (2017, January). The future of social learning: A novel approach to connectivism. In Proceedings of the 50th Hawaii International Conference on System Sciences.

 

Traxler, J. (2009). Current state of mobile learning. Mobile learning: Transforming the delivery of education and training, 1, 9-24.

 

Van Eck, R. (2006). Digital game-based learning: It’s not just the digital natives who are restless. EDUCAUSE review

 

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A Qualitative Study to Examine How Individual Technology Choices of Higher Education Computer Science Students Affect Success in Acquiring Knowledge and Skills

A Qualitative Study to Examine How Individual Technology Choices

of Higher Education Computer Science Students Affect Success in Acquiring Knowledge and Skills

 

By Daniel Grigoletti

 

December 9, 2018

 

Introduction

This study performs a qualitative analysis to determine how STEM students are impacted by technology choices in higher education Computer Science coursework.  It seeks to extend the research about student attitudes regarding their individual choices of technology adoption affect their success coursework completion, measured by their ability to engage with the technological content and acquire knowledge and skills (Heflin, 2017).

 

There is a need to study learner behavior when making and implementing their technology choices, and how the personalized instances of technological tools affect their ability to construct knowledge and acquire skills toward successful completion of coursework for higher education STEM students, and to determine which individualized technology augmentations in educational environments, such as mobile computing, web-based resources, social media, networked environments, etc. can lead to deep learning in project-oriented learning environments (Kim, 2017).  Also, to determine, through qualitative analysis, observation and interviewing, what other technology augmentations lead to higher success for these populations of students, ultimately resulting in gainful employment degree completion (Katz, 2017).  The research question the study seeks to answer is:

 

How do STEM students in higher education feel their individual choices of technology adoption affect their success coursework completion and acquiring knowledge and skills?

 

Literature Review/Theoretical Framework

 

Science, Technology, Engineering and Math skills are essential for Computer Science students to scaffold their prior technology knowledge to create software and other new technology.  The Internet, Web 2.0, social learning and MOOC’s, and cloud-computing have created new opportunities and has positively disrupted the way students learn technology.  In order for students to master the complexity of developing software, they need to access resources beyond the classroom and traditional textbook/whiteboard resources (Oremus, 2015).  Since the product of Computer Science students is new science and technology implementations, they increasingly need to self-regulate their ability to learn from artificially intelligent, virtualized, simulated, digitally enabled, and/or technologically mediated instructional resources (Chang, 2018).  There are both private/proprietary and public resources that educators need to leverage in order to teach Computer Science, drawing upon a multitude of technical, social and educational communities, accessible through the Internet.  The classroom, albeit not just confined any more to a room in a building, requires using various networked hardware devices such as PC’s (laptops and desktops), Mac’s, tablets and smartphones, browser based software including hypertext/hypermedia, multimedia, social media such as Twitter, Instagram, Facebook and LinkedIn, and web based applications.

 

Since adult Computer Science students need creativity when developing software, they draw upon tools which enable them to accomplish required tasks, but also need to be supported their way of thinking and approach to solving problems (Oliver, 1999).   They utilize and extend their prior knowledge when accessing interactive information sources such as with hypermedia-aided learning, for example, seen in browsing the Internet (Shapiro, 1999).  Also, through personal technology such as smartphones, laptops and tablets, students are empowered like never before, possessing the ability to immediately extend their prior knowledge and integrate information from globally oriented and diverse sources (Phillips, 2017).  Since Computer Science college students, as adult learners, rely heavily on these technology augmentations, they need to make sense of their learning experiences in order to develop their skills and knowledge to move to the next level whether to another course or into the workplace, thus transforming their capabilities, and constructing new knowledge and perceptions of the world.  Adult learners, through educational facilitation, can undergo personal transformation (Rutherford, 2018).

 

Another important aspect of the technology resources used by Computer Science students involves accessing open source software, open documentation materials (such as Wikipedia, wikis, blogs, etc.), which are essential resources for students using owned devices, for example running the Linux operating system, to install a customized set of low cost software development tools (Ahn, 2015).

 

The one big enabler for accessing these resources is Internet connectivity via school-provided networks, home networks.  However, alternative connections can be made via cell networks.  These infrastructure components for educational environments are essential, including the network, lab, client-computer and software, are additional enablers in addition to the accessibility provided the Internet to students and teachers (O’Donnell & Perry, 2013).  Therefore, an overarching need is connectedness, contributing to application of the connectivist theories, and providing support for ISTE standards for students, giving them far-reaching access to applications and data for creative use.   Students provide their own technology resources, or use that which is embedded in the educational organization, which can benefit the organization by extending in-house resources.  The student/educator provided technology, can lead to improved problem solving abilities, as well as enhancing communication and collaboration through connectivity (Savery, 2015).   However, educators must be cautious to embrace any technology that a student brings to the table.  For example, mobile devices can provide many distractions for learners with social media applications, games, and incessant notifications (Sana, 2013).  However, educators can cautiously leverage the presence of mobile resources provided by students, to enhance the classroom experience.  Mobile computing devices coupled, with social media therefore can provide useful resources for Computer Science students, enabling them to take advantage of a multitude of learning aids, in the cloud, and which are mostly freely available (Lau, 2017).

Educator pedagogies, especially those in STEM higher education classrooms, require new ways to implement technological beliefs and knowledge epistemologies, (Tondeur, 2017).  Computer Science teachers in higher education need to be cognizant of the needs of adult learners, and adjust their teaching to align with andragogy approaches rather than only pedagogical practices, since students need to mature adequately enough to enter a very adult-oriented workplace, requiring self-directed problem solving skills, adaptability, resilience and collaborative skills (Knowles, 1970).  Computer Science educators need to have a toolkit of pedagogical options at their disposal, augmented, infused with educational technology theory, in order to adapt to the changing landscape of technological instructional modalities, and exponentially expanding content knowledge in order to best prepare students to develop new technology with technology (Howard-Jones, 2018).  Another aid to faculty and is the application of aggregate data collected in the classroom, data obtained via program assessment and evaluation, through machine generated data, as well as data collected through current research methods, is the use of big data and learning analytics.  By collecting, analyzing and utilizing seemingly disparate data, and processing it through algorithms and visualizations, educators can gain insight into programmatically personalizing instruction for Computer Science instruction (Maselno, 2018).  Using professional organizations such as IEEE resources give the educators and learners an arsenal of research and models of coding techniques, and events in the industry (Burbaitė, 2018).

 

College students are necessarily developing digital literacies, associated with the technological environment and within the social cohort and generation to which they belong.  Given the shared social skills and technology knowledge of their generational context, students can leverage ubiquitous networked software technologies, especially as social media platforms.  By participating in social media, Computer Science students connect with others globally, providing them with perspectives they cannot get from localized classroom environments alone.  This serves to motivate the student to learn beyond a particular lesson or assignment while providing them with a necessary social connection (Alhabashm, 2017). The variety of social media interactions via blogs and wikis, for example, help them to further construct knowledge socially and cognitively, and better learn the subject matter.  By sharing ideas students can make new meanings, and creatively develop knowledge to meet the expectations of diverse and dynamic technology learning environments (Ifinedo, 2017).  Some of the essential tools used today include mobile computing and social media such as Twitter, Instagram, LinkedIn, Snapchat and Facebook can be used to extend learning, not only for recreational purposes, but for professional academic purposes (Lau, 2017).  Computer Science learners can also benefit from game-based learning, given the sophistication of software applications, simulations, virtual and augmented reality, animations and other multimedia which is embedded in software today.  They uniquely have the perspective of both using technology and creating technology, including games.  So, DGBL is a natural feature of a student’s college experience, whether intentional or incidental (Erhel, 2013).  When assigning an in-class activity, using a gamified approach can be useful for Computer Science learners.  Since they already have a context and perhaps an interest in playing computer games, the fact that creating computer games involves programming makes a gamified learning experience very powerful, even if not used for entertainment.  Gamified experiences create intrinsic motivations since they are interactive, immersive, and enable a simulation of real-world situations (Behnke, 2015).  The millennial/generation Y and generation Z students are increasingly learning through digital devices such as smartphones, and other resources, especially software, in a very self-directed manner, taking advantage of the new socio-cultural contexts provided by the Internet (McMahon, 2005).  This emphasizes how Computer Science students acquire and use new skills, knowledge and tools such as video, images, podcasts, and hypertext/hypermedia through access to online websites tutorials, libraries and databases for the respective content being studied.  The practical applications of digital literacy, provided with technology, software and new approaches, but competence in more abstract aspects of literacy such as problem-solving, reasoning, critical thinking, and argument play an equally important role (Smidt, 2017).  New digital literacies build upon the existing literacies, enhancing foundational knowledge which learners require to survive and navigate in the modern technological world.  Today’s learners are building diverse intelligences and enhancing natural talents digitally, manifested as new literacies including better collaborative opportunities, diverse modes of communication, emerging languages, requiring higher level abstractions, and promoting creativity.   The next generation digital learning (NGDL) are emerging and coming to fruition (Maas, 2016).  Digital literacies require new skills and strategies to effectively use them, promote creativity and integrated contexts, and coincide with new pedagogies and diversity of thought. The new literacies unleashed by digital technology enable new expressions of intelligence, natural talents through new languages, abstractions, and creations (Pietila, 2017).  By understanding how today’s students acquire, integrate and synthesize knowledge utilizing such tools and techniques as things as micro-blogging, wikis, social networking, hypermedia, search engines, and gamification/game-playing, we can connect with today’s learners by designing and developing pedagogies and systems that meet the needs of the new paradigm of learning. Besides the obvious emergence of digital literacy, we also see other new literacies acquired and possessed by learners that enable higher levels of criticality.

 

However, simply translating the conventional literacies (the 3 R’s) and rendering them digitally, does not alone comprise new literacies, but by operationalizing connectivist and other theories into measurable assessments of these emergent literacies, for example, to enable further research into new literacies and better define information fluency (Siemens, 2014).  Constructivism theory gives us a framework for learning how students and educators in Computer Science can create knowledge from a variety of resources on the Internet (Ben-Ari, 2001).  Educators today can treat onsite educational experiences much like an online or blended, enabling them to reach diverse student populations, encouraging a variety of technology augmentations to differentiate instruction and integrate (via TPACK research) their pedagogy, technology, content knowledge (Mishra, 2006).   Thus, learners can self-direct their exploration and acquisition of new knowledge through new delivery mechanisms, regardless if they originate directly from people or educational technology.  Students, can take advantage of these educational technology inventions and opportunities to actively personalize their learning experience (Boelens, 2018).  Educators, as learners themselves, utilize personal technology for similar reasons, but need to encourage their use outside of the classroom, whether intentionally flipped, or simply as an extension of the in-class experience, such things as multimedia and hypermedia resources such as video, blogs, tutorial sites simulating coding scenarios enable students to acquire knowledge in a self-directed manner (Barab, 1997).

 

Methods

 

The setting of the study involved an urban community college environment, both in a classroom and a tutoring center.  The study examined effects of technology usage and learning by Computer Science students in higher education settings, within the study populations and how they assimilate to available technology on campus when technology resources are scarce or difficult to access (i.e. not owned by the student, or such things as Internet access is not present in the home), and exploring how technology (hardware, software, Internet access, mobile devices) are acquired and how knowledge is constructed given such constraints of lack of monetary and facilitative resources for utilization of technology, and examined how choices in technology resources influenced the progression of knowledge and skills acquisition for the population.  Some research has been conducted regarding the epistemologies of how Computer Science students construct knowledge, utilizing the logic they are immersed in within their programs of study, and how they construct knowledge actively and recursively as they analyze problems and develop software (Ben-Ari, 2001).  The act of developing software is a creative and therefore, a good example of constructivism, since students are expanding their knowledge as they build more complex software, in an iterative manner.

 

The socioeconomic characteristics and culture of the Computer Science subjects involved were mostly working adults, minority, some foreign having relocated outside the US, and mostly non-traditional students taking evening, weekend or online courses.  The age group was mostly within the 18-30 years old group, and included students with lower socio-economic situations based upon the conversations about using public transportation, the fact that most students were full-time students, working their way through college.

The aim of the study is to promote knowledge for improvement of technology augmentations for Computer Science students, and seeks to transform the way technology is utilized to solve problems in Computer Information Systems (CIS) within community college environments, with demographically diverse populations.  Fieldwork was conducted in a community college CIS environment through interviews and essay documents from students in a 100 level programming course, as well as students visiting a tutoring center working on assignments for 100 level programming courses.  The data collection and analysis was tied to the theoretical frameworks discussed in the literature review.  The study performed analysis on the data to help determine the categories and qualitative assessment using word rudimentary word frequency, primarily used Microsoft Excel to collate and analyze the data.  Other software used included Microsoft Word, and online facilities embedded in such sites as in https://www.wordclouds.com/ (i.e. for word counts and visualization of the student responses).

This theoretical approach centered on grounded theory in that it utilizes a social psychology to examine situations existing at a place and time.  It applied grounded theory, generating useful results, answering the research question, and establishing patterns of how Computer Science students in a problem-based learning situation derive value from their choices in personal technology.  Using grounded theory, the study examined how knowledge was derived by the subjects in the study through data collection, and established a baseline to examine the research problem using scenarios to develop conclusions.  The data collected was analyzed, forming models of the scenarios describing attributes, emergent behaviors, roles, and activities.  The student responses confirmed that the they were adaptable to whichever choices they made, including self-provided technology, and that which was provided by the institution (Strauss, 1994).

 

The study used connectivism as an epistemological approach to examine the data, since Computer Science students are digital learners, with digital literacies.  Learning software development frequently requires connecting to collective resources on the Internet to acquire new and extend prior knowledge.  Networks abound in a Computer Science student’s learning experience, whether a social/special interest/professional network, or computer network they are learning to operate and configure, or one which they are building.  The connectivity and distributed nature of digital resources that are inherent in Computer Science learning applies at many levels.  Both educators and learners can benefit from a connectivist approach, however the focus of connectivism is more geared toward seeing how learners can acquire new knowledge when accessing networked resources and facilities (Siemens, 2004).  The data in the study revealed that Computer Science students construct new knowledge from their choices in technology in order to solve problems but also exhibit connectivist behaviors.

 

The techniques used included interviewing (in the classroom) to solicit feedback, making direct observations of Computer Science students in the natural environment (tutoring center), and document review.  Objective observations, interviews, and note-taking were utilized in the research study.  The empirical field notes recorded in the research aggregated and recorded trends that students were engaged in such as accessing mobile resources, especially Internet-enabled technologies to enabled the construction of new knowledge and skills.  These observations were non-interventional, documenting the subject’s behaviors, feelings and reflections via textual means (i.e. students provided written responses in the interviews, or demonstrated their practices during observations).    Social constructivism was used as the theoretical paradigm and interpretive framework/perspective since the adult learning environments studied accommodate self-directed learning, andragogy, and active involvement of learners and provided a useful lens to determining the effects of varying levels of technology augmentation to urban adult learners in order to construct knowledge and acquire skills.  Since the nature of today’s classroom involves a blending of both onsite learning and online resources, the modern college classroom should be considered blended, whether tagged as hybrid or not.  Flipping a classroom to the point where much of the foundational learning is done independently requires that students have resources outside of a classroom/lab provided by the college.  However, students without personal resources could take advantage of open labs and public computers (i.e. at public libraries) to conduct the continued learning outside the formal classroom environment, in order to learn in an individual space where videos and web-based resources can be accessed (le Roux, 2018).

 

Data/Analysis

 

Data gathered from interview sessions on student use of technology in completing a problem-solving assignment.  Interview notes were recorded for the individual student encounters.  Within grounded theory methodologies, the data confirmed how college Computer Science students in urban environments (community colleges and state universities) learned from their different choices of technology resources, leading to successful completion of course assignments, emphasizing how individualized technology augmentations lead to higher success in the studied populations of college students (Stevens, 2018).  The interviews in the form of essay questions, were performed after giving students a brief in-class problem-solving activity to complete in class. Students responses amounted to feedback/reflection of how they solved the problem.  The feedback from students were examined on sample activity using these different methods.  These were the questions posed in the interviews (See Figure 1:  Interview Data):

 

  1. What are technology choices have you made to complete this problem? Please provide a paragraph discussing whether you used a laptop (specify the brand and OS) or the desktop, the software (i.e. browser, IDE, other). Please specify which websites you used to help you solve the problem, including the Blackboard shell.  Also, please specify whether you used a smartphone or other mobile device (specify the brand and apps you have used) for this and other programming problems you’ve completed.

 

  1. What skills have you gained or enhanced by using technology to complete this problem? Please provide 1 paragraph describing the knowledge and skills you have to complete this lab.

 

  1. What challenges have you encountered with the choices in technology which you’ve used to complete this and other programming assignments? Please provide 1 paragraph describing issues that you have encountered using such technologies as websites, laptops, desktops, networks, apps, other software, mobile devices, etc.

Computer Science students were given a lab activity in the Java programming language, requiring synthesis from the current learning unit, and extending their knowledge utilizing technology provided by themselves or within the college/institution.  The technology items which students may be using and their usages will be observed.  For the purposes of this study, additional students were observed in the tutoring center, enrolled in Computer Information Systems courses involving various programming languages (C++, Java, Python, HTML/JavaScript), theory such as Human Computer Interaction (HCI) and applications (Microsoft Office).  All of the assignments involved forms of open-ended project/problem-based learning.  Students were free to use multimedia and hypermedia to solve problems (Oliver, 1999).

The observations provided evidence to produce meaning and an understanding of the research problem regarding how STEM students in higher education Computer Science feel their individual choices of technology adoption affect their success coursework completion and acquiring knowledge and skills.  The problem-solving classroom activities were observed and recorded, utilizing feedback from documents provided as essay responses from students in an online problem-solving activity (Savery, 2015).

The data sources including documents, observations, and written student responses from written interview questions delivered through the Blackboard LMS were collected from individual Computer Science students as they worked through programming problems.  Data from interviews, LMS shell documents and direct observations in the tutoring center were then analyzed using professional approaches and software tools such as Microsoft Office applications, including Excel and Word, Google Drive and other web based tools.  These tools were used for coding and to organize/categorize the qualitative data.  Specifically, coding was performed through identifying phrases and keywords by frequency enabling the formation of categories and to develop themes.

Next, direct observations of CIS students solving programming problems were conducted on students in a natural learning environment, a tutoring center.  The data showed students having dialogue via technology augmentation to provide adjunct resources to the immediate teaching and learning activities.  Given traditional resources (instructions, readings), students, accessed technology resources to get ideas from code samples to help them develop skills and knowledge in order to accomplish the learning objectives.

Objectively observing a set of subjects, and recording field notes provided information enabled the extrapolation of emerging technology trends, and demonstrated how the Computer Science students construct knowledge from the various technologies (hardware/software) which they accessed during the problem-solving process.  The student had the ability to express their software development ideas authentically, which resulted in valuable data being collected and knowledge being derived from the subjects being studied.  Here are the patterns revealed from the observations:

  1. Students all required basic LMS skills, utilizing software tools, following instructions, using various programming languages. Prior knowledge of a particular programming language was helpful for those completing assignments in a new language.  Languages represented included Python, C++, Java, PHP, HTML, JavaScript and SQL.
  2. Students were midstream in the courses, and had requirements and goals to accomplish per the syllabus, typically an assessment such as a lab assignment or quiz/exam. They were usually stuck on a particular issue, or needed additional direction beyond what the instructor provided.  They found using online resources such as tutorial websites such as W3Schools, JavaDocs, Mozilla Developer Network and others helpful.
  3. Most students arrived within 5-10 minutes before/after their appointed schedule, and began their tutoring session. The students generally were prepared to explain what their needs were, but some were in need of remedial assistance due to having not attended a recent class.  Some had printouts of their lesson, but most were using the LMS tool to provide details of the lesson.  Some students took notes as they asked tutors for explanations of material which they did not understand.  The tutors asked probing questions initially to understand where the student needed to start.  Generally, the students responded positively to tutor instruction, and showed appreciation for the help as accomplishing an assignment.  Students expressed concern about their ability to complete future assignments, but generally showed a better understanding of the material after the tutoring session.
  4. The activities performed that required various primary and peripheral computing devices and many different software applications, many of which were delivered through the Internet, including the use of search facilities within browser/search engines, tutorial sites, blogs, and the LMS instructor notes. The individualized instruction and techniques for self-directing learning modeled by tutoring staff provided good reference for students to acquire information on their own, beyond the immediate instructor-provided information.
  5. The tutoring sessions were all individual, so there was no group or social interaction beyond that with the tutor. However, collaboration occurred between the instructor, tutor and student in a virtual manner when they accessed communication facilities such as email, messaging, threaded discussions, and feedback applications provided by the tutoring center.  The tutoring session was confined to around 1 hour, therefore there was a sense of urgency to accomplish the work within that timeframe.
  6. Students used a combination of either all-in-one HP computers, their own laptop, or a mobile device. The social interactions observed included the student interacting among tutors, interfacing with workstation hardware and software such as Blackboard, Chrome and the Java Eclipse IDE (Integrated Development Environment).  Both verbal, and written communications were frequent student-to-tutor, student-to-technology, some nonverbal in the form of interaction kinesthetically with input/output devices.

Findings

Of the 8 subjects, in order to solve programming problems, the majority of the Computer Science students used sample programs and materials posted by the instructor in the LMS shell, Blackboard.  Other major patterns that were observed are as follows:

Interviews Question #1 revealed the following themes:

  1. Problem-Oriented Learning: There was a focus on learning through problem-solving based upon the responses discussing the various devices and software tools which were used to complete the programming exercises (Oliver, 1999).
  2. Standard Technologies Utilized: The majority of the Computer Science students were working on Windows-based computers over Mac or Linux based systems, which is more typical for traditional computer science and programming.  The use of desktop computers provided by the college slightly outnumbered the use of laptop computers, followed by phone/mobile devices.  Brands included Toshiba, Dell, HP, Lenovo and Apple.  Students with Apple computers had issues running some of the software tools common for Windows devices.  Some applications were not available on all computing platforms.
  3. Challenges of Mobile Technology for Computer Science: Mobile devices screen size was a factor for some of the students.
  4. Access to Specialized Technology: Convenience factors were in play because students found that using computers in the lab provided immediate access with simply login authentication required.  Some of the applications were also available via virtualized environment such as Citrix.  However, when needing specialized resources only available on campus, students with inadequate resources at home were at a disadvantage.
  5. Access to Content: Content itself was not a primary concern, but the practices which students used to gain access to resources for solving problems (i.e. finding a website via hyperlinking and searching in a browser) appeared to be more important to their success.  Students tended to use Internet resources before referring to printed text, or even e-books, but also utilized communication via email to the instructor for assistance in getting refinement to instructions when solving programming problems.
  6. Primary Software Resources: The top resources used on the Internet were Blackboard LMS (moving to Brightspace), websites including stackoverflow.com, youtube.com, docs.oracle.com, w3schools, Google.com, and online tutorial sites.  Software both Internet-based and locally installed such as Google Chrome, NetBeans IDE, the Java programming language, with search engines being the most prominent.

Interview Question #2 revealed the following themes:

  1. Conceptual and Soft Skills: Computer Science students are cognizant of both the technical (keyboarding, using an operating system and the various sites on the Internet, and using software applications installed on the computer) and soft skills needed (such as thinking, logic, user orientation, object oriented programming, math, analysis, problem-solving, and creativity) to develop new skills and knowledge while completing their exercise.
  2. Self-Directed Learning: They appear to have expressed self-reflection and realized the gaining, improvement and incorporation of new knowledge into their existing knowledge.  It highlights the unique nature of computer science students using technology intensely to create new technology (Manning, 2007).
  3. Building Specific Programming Skills: This also focused more of the student attention on programming topics such as loops, variables, random numbers, arrays, statements, declarations (data types), importing code classes, interacting with user/user input.
  4. Realization of Need for Multiple Technologies/Tools: An overarching theme in the data was the practical nature of a programming course, which requires utilization of multiple hardware (especially by place and time), and software tools, including such things as MOOC participation, self-directed tutorials and to accomplish the objectives of an assignment (Chang, 2018).  Secondarily to the software technology, the students would access resources such as the instructor and manual material such as a textbook.

Interview question #3 highlighted some learning challenges and technical issues which Computer Science students had with the technology they utilize.  It focused the student reflection more to the content of the assignment, specifically the code in the programming language, Java, they needed to write for the exercise.

  1. Personal Technology Capabilities: More than one student had issues with implementing the necessary software on their own laptops.  Issues such as downloading and installing software from the web came up.
  2. Time Management and Confidence: The factor of time and time management was introduced from students with regard to completing assignments, citing such challenges as employment and other courses.  There was also a theme which brought out the uncertainty of succeeding given the multiple challenges with technology that students face in a traditional programming course (Maslow, 2013).  The feelings of frustration, insecurity of their abilities, and difficulty in learning complexities of programming languages are revealed in the responses to this question reflecting how the needs which Maslow outlined being prominent to college students (Poston, 2009).
  3. Connectivism via Online Communities: Students looked to help solve their technology and challenges in understanding by using blogs/forums, online documentation for Java on Oracle’s site (called Java Docs), watching videos online, and utilizing sample code provided by the instructor to learn how other programmers in the community of programming have solved similar problems (Siemens, 2004).
  4. Value of Personalized and Individualized Learning: Regarding the theoretical frameworks which were applied, there were contrasting lenses between the individualized environment of the tutoring center, and the more collective environment of the CIS classroom environment.  Having both environments provided the study to involve accounts of data collection using a connectivist approach while allowing students to create knowledge through a constructivist approach (Basham, 2016).

The data collected from the aforementioned interviews, was triangulated with the observational data collected in the tutoring center.  However, the tutoring center data was collected from Computer Science students in the same population, but were experiencing acute challenges on an assignment, a set of assignments or in the entire course.  It was found that the technologies utilized by students to solve problems in the tutoring center were the same as those from the CIS class, and involved the following wide range of hardware and software elements:

The biggest finding was that Computer Science students adapted to adopting the software tools that were available, predominantly Windows-based, either those which they provided from their own personal resources, or those provided by the institution.  Sometimes the software tool was web-based and could be dynamically implemented, circumventing the need for a localized installation to be present.  At times, the students had to perform a work-around if a particular editor or reference was not locatable or available.  However, the self-directedness of the learners, coupled with assistance from educators, allowed the students to accomplish learning goals (Rutherford, 2018).

Students of Computer Science, are both users and developers, and need to learn how to work around issues.  For example, students with outlier resources such as Linux or Mac laptops, would have to take on the responsibility to install/configure/maintain the alternative device, requiring more independence given support was not provided for these systems, but giving them a further “side-lesson” in configuring the other device to use for their coursework.  This mimics the issue that arises in implementations of technology in the workplace where workarounds are sometimes necessary, albeit usually temporary (Pollock, 2005).

Another finding was that the choices were not so much the main factor in learning technology with technology, but the patterns of use were significant.  Computer Science students will find a way to adapt any technology, given a network connection, to solve problems.  Problems in Computer Science can have multiple solution paths, in selection of language, operating system, storage mechanism, or network interoperation.  If the technology tools (hardware, software) are prearranged or fixed to a location by the institution (not chosen), but do not lead to a solution, the student is forced to adjust.  Often that means reverting to an owned piece of hardware (sometimes inferior, but configurable) which can be mobilized to a different time/place (i.e. laptops) but may require additional effort in reworking/repurposing it for the learning assignment or task at hand.  Some students may give up in these situations, or be relegated to always do their work on campus.  These problems occur with both physical and virtualized computing resources (Elliot, 2013).

 

 

Discussion

This research study explored how Computer Science students in college-level STEM courses utilized their technology resources and how their choices affect learning and skills acquisition.  From the findings, the paper confirms the research provided in the theoretical framework that Computer Science students rely heavily on self-directed activities such as seeking ideas from collective sources to solve problems to programming assignments.  This self-regulation, from previous research does lead to success, providing a way for individualized instances of a learning experience applied to the same problem assigned to a Computer Science student.  However, the problem referred to in the research is that educators may not be well-prepared to help students learn to be more self-directed.   (Zimmerman, 2002).

This study aligned with existing research about technology choices made and patterns used by Computer Science students in general affects their acquisition of, utilization, and construction of new knowledge.  Specifically, this confirmed that Computer Science students rely on more unique mixes of technology augmentations but there was no one set of technology tools (i.e. smart phone brand/OS, laptop choice, choice in storage/backup, software tools choice) that a student used that led to their success.  The students decided dynamically how to differentiate their technology resources to accomplish the tasks assigned.  The interviews yielded information on challenges which students experienced when using technology in their coursework in Computer Science, and that their personal choices were not always important if the resources provided by the institution were adequate.  Their personal technology was secondary in the case where adequate resources were available in computer labs.  However, the unique mixes of personal technology choices were widely varied (i.e. smart phone brand/OS, laptop choice, choice in storage/backup, software tools choice).  The observations revealed how students will use their personal technology to complete an assignment when those provided by the school were scarce or not available, or if they had foresight and realized the value of providing their own equipment.  These students had added responsibility of maintaining their own system, but were able to customize it as a tool to aid them in their learning (Maselno, 2018).

During observations, new meanings were derived from interactions between tutors and Computer Science students, providing individualized instruction and learning on specific lab assignments.  Most of the interactions involved gaining understanding through practical applications, rather than building a theoretical foundation.  Students utilized their laptops and other mobile devices when they had them, otherwise, they utilized the stationary workstations in the center and stored their completed work either on cloud storage (iCloud, OneDrive, Google Drive), sent themselves an email of a zip file, or used a flash drive.  Some of the students immediately posted their completed work onto the Blackboard LMS submission link.  However, students sometimes did not have good knowledge of file management and needed the tutor to assist them with that, which was apart from the immediate content which they were learning on the assignment.  Some of the students seemed desperate because they were behind in the course and expressed that they needed to complete multiple assignments (Chang, 2018).

Comparing the aggregate student solution results to the baseline programming problem led the research to find that networked information provided great value to the Computer Science students’ ability to solve the problem.  The student outputs helped to form a model of the learning scenarios, particularly to find the effects of choices technology usage on learning Computer Science in higher education settings, studying lower income and urban socioeconomic populations, and how they assimilate to technology when resources are scarce or not available.  There were no surprises as to how hardware, software, Internet access, mobile devices were utilized, if present for basic activities such as browsing websites, accessing tutorials and blogs, using applications, and file management.  There were instances of Computer Science students expressing the lack of adequate laptop resources, therefore require them to use the desktops provided on campus.  However, it was observed that knowledge is constructed regardless of such constraints as lack of monetary resources (i.e. not owning current technology).  Social interaction in virtual environments provide students with an equalizing factor regardless of availability of resources (Ifenedo, 2017).

However, weak facilitative resources, and time management were significant factors in the Computer Science students’ ability to construct solutions.  For example, the weakness in instructional support/access (i.e. contacting instructor via email often was not fruitful to help solve problems, or the basic LMS material was not sufficient to solve programming problems).  The study revealed how lack of personal resources were not great influences on the progression and skills acquisition for the potentially underprivileged populations.  Rather, the cognitive abilities of the student, the tenacity (scrappiness) of the student, and the motivation to succeed far outweighed the lack of resources.  Computer Science students were able to take advantage of available resources on campus such as software applications, hardware devices and adapted them to their needs.  However, the convenience factor was a stress on the urban student (i.e. having to commute using public transportation, challenges regarding not having Internet access available at home, working a job which detracted from the time they were able to spend on CIS coursework, and challenges with course load).  The students utilized various human relationships, when available outside of class, including communications with instructors, tutors, administrative staff, and fellow student in a socially constructive manner.  The adult-learning theoretical context was evident, given that the college students, as adult learners, demonstrated maturity of their cognitive abilities.  The self-regulation of learning Computer Science which students exhibited was a significant accommodating factor to encourage independent learning leading to clear demonstration of constructivism.  Constructivism was the initial lens used to view how varying levels of technology augmentation aided adult Computer Science learners to acquire new knowledge and form additional skills (Knowles, 1970).

The research study observations performed used an in-class programming activity.  Field notes were taken to see how Computer Science students utilize technology to solve the specific problem.  The data were collected through written student reflection and feedback from a standard set of interview questions.  The technologies which Computer Science students reported having used for problem solving activities was analyzed to determine how it helped them to complete assignments.  In addition, observations were performed, and data was gathered from observational notes.

Conclusion

 

The choices made, acquisition of, and utilization of technology by Computer Science students of technology in higher education environments involves unique mixes for each student.  There is no one set of technology tools (i.e. smart phone brand/OS, laptop choice, choice in storage/backup, software tools choice) that a higher education student has that will lead to their success.  Because of this differentiation and diversity of technology resources among post-secondary Computer Science students, there is a need to further study how the individual technology choices made by these students will or will not culminate in a successful pursuit of degrees.

 

Reflection:

 

The data collection went well since I was able to draw from two settings with the same student population.  I utilized a tutoring center environment where individualized instruction and one-on-one interviews and feedback was collected.  The insights gained through personalized interactions provided rich data regarding the practice of using technology to solve problems in Computer Information Systems (CIS).  The Computer Science students were candid about their challenges with using their own laptops, smartphones, Internet resources, software installations, and public access to technology utilized outside the college facilities.  In the classroom, having a controlled environment, the student responses were recorded in a consistent format, with every student solving the same problem and answering the same questions, albeit in an open-ended questioning format.

 

If I could, I would change the framing of the observations in the tutoring center by comparing the performance of Computer Science students for specific problem, similar to how I collected data in a controlled classroom setting.  I learned about myself, as a researcher during this project that qualitative data analysis is difficult as compared to quantitative analysis.  Having to codify behaviors and find patterns and themes in order to analyze data is quite a contrast to using statistical analysis on survey responses that are quantifiable.  However, using both methodologies for future researching will be valuable, providing me with a toolkit to draw upon, enabling me to be flexible to collect data in multiple forms, and preparing me to conduct a comprehensive study to address any future research problem.