Recently I have written a concept paper on using disruptive technology in higher education, and I thought I share it here. It is rather lengthier than my normal posts, so considered yourself forewarned 🙂
The term “Disruptive Technology” has no meaning unless we understand and define the type of problems need solving within a given context. Something that is common and taken for granted in one place can be totally disruptive elsewhere. For example, internet is ubiquitous in the cities now at least in developed countries; however giving access to it in remote areas even in developed countries can be totally disruptive. The challenge we face in higher education is not so much about the advent of new technologies destabilize the university’s founding assumptions. We as educators have always had to compete against the many alternative sources of information, media, and informal learning to engage students. Some had even gone on the extreme of banning laptops in class. However, the real challenge for formal education lies in understanding how people learn in a whole-system, holistic way, and how can we leverage this understanding with technologies. While new media and innovative technologies can potentially help us improve learning, there is a need for a fundamental shift in our perception of the learning and assessment process for true disruption to take place in higher education.
Let us think more about what types of digital technologies are more conducive for disruption. Maddux, Johnson & Willis’ classified digital technologies into Type I and Type II. Type I represents technologies that automate or replicate an existing practice. An example being the use of webinar presentations to replace face-to-face lectures. The technology didn’t “disrupt” as much as it enables the one-to-many broadcasting style lectures to reach a wider audience that can be recorded easily. Type II encompasses technologies that enable us to do things that can not be done before. Learner-driven adaptive or adaptable learning paths embedded within learning objects is an example of such technologies. Type II learning technologies have potential for disrupting learning because this type of technologies fundamentally change the patterns of human interaction. In the example of adaptive learning, when designed with learner-control in mind, alters the relationship between instructors and learners. Learners can choose their learning paths based on system feedback, or they can combine multiple pathways and learning modalities. In some cases, learners are also co-creators and co-producers of knowledge, and there are platforms and systems that encourage students to read, watch, listen, ponder, discuss, collaborate, remix, experiment, reflect, mentor others, etc.
The recent maker movement brings out the innate urges in human to tinker and create things. Maker spaces have been popping up all over the world where people design and market technology-based products using robotic parts, 3-D printers, electronics, woodworking tools, and a whole arrays of techniques. The maker culture emphasizes informal, networked, peer-led, and shared learning motivated by fun and self-fulfillment (or for social goods in some instances). This is a movement born out of Type II technologies. It represents a shift from passive consumption of mass-produced, one-size-fits-all material to an active participation of communities and individuals to create bespoke products. Similarly, how can we leverage Type II technologies in learning to shift this kind of interaction and relationship?
In my own research, I applied the idea of utilizing Type II technologies to shift the relationship between learners and instructors – to create a positive learner-directed learning experience and support learners to assume active control for their own learning in learning computer programming. Based on David Kolb’s Experiential Learning Theory, I have designed a Learner-Directed Model where learners are in control of their learning paths and modes of learning. Modes of learning are created based on the experiential learning cycles: concrete experience (watching a video tutorial or co-creating a video-tutorial); reflective observation (group discussion forum); abstract conceptualization (concept mapping and mental model building); and active experimentation (writing codes/debugging). Initial results have indicated that learners preferred this way of learning and that it improved the overall learning experience.
With the collaboration from interested parties around the world, I have been thinking about how to develop this research further into the following two ideas:
- To model after the maker movement, I am interested in creating and nurturing a peer-regulated adaptive system with peer-contributed content and activities whereby students can easily search, locate, remix, organize, and evaluate content in a granular level. In essence, a space where they can tinker and test ideas out. Students are suppliers as well as consumers of course content. Faculty act as curators and mentors alongside. This could be an extension of the earlier research work I have been doing – building on top of the Learner-Directed Model where content could be recommended/ranked/commented by peers. Based on the popularity and their preferences for learning modes, certain content would be featured more prominently (similar to Netflix algorithm).
- For meta-learning: to develop a three-tier community of practice/group-based learning. Fashion after the Networked Learning Modes where we have three levels of group-based engagement: learning community, learning cohort, and learning pairs. Smart and adaptive software can easily provide insights to community members regarding who within the university (or wider community) is willing and available as mentors to help others learn (similar to MentorCity). It can also curate content to generate suggestions on the most relevant resources at a meta-learning level such as helpful study strategies for a particular topic or subject.
Without looking at learning in a holistic way, efforts in mobile learning, gamification, learning analytics, augmented reality apps, immersive learning environment, and adaptive learning models are going to be superficial at best. A whole-system learning requires active participation from all concerned parties: policy makers, learning designers and developers, instructors, university administrators, the corporate sectors, and professional communities. Policies need changing to support micro-credits and other forms of academic credentials; designers and developers need to be pushed for rapid-prototyping of new ideas; instructors need to change the way they assess learning progress and competencies; administrators need to hire, nurture, and reward staff who are willing to drive the change; the corporate sectors and the professional communities need to provide feedback on the type of graduates they want in entering the workforce.