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

 

Wolf, M. A. (2010). Innovate to educate: System [re] design for personalized learning, a report from the 2010 symposium. Washington, DC: Software & Information Industry Association.