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.

 

2. 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.

 

3. 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.