Designing technologies for workplace learning is the absolutely major part of my research. But what can technologies do for learning?
I don’t know of authoritative literature that categorises the different roles and functions of technologies in learning activities – so over time I’ve created a brief overview for myself.
The below listed categories of technologies are not completely mutually exclusive. For instance, technologies that support the consumption of digital learning materials might as well be game technologies; and simulations may also support communication, etc.
Additionally, while categories are intended to be exhaustive, in detail one could argue about additions: For instance, open learner modeling is now subsumed in both data analytics and intelligent systems, as open learner modeling in the end has been decided to be a more fine-granular term than the others in the list. Another example is e-Assessment, which has been left out as simple question-based assessment is technologically not research-intensive, although it may be very much from a learning sciences perspective!; and task-based or implicit assessment fits into multiple categories such as game technologies or virtual simulations or data analytics.
Finally, infrastructure and device technology necessary to actually access content and run software for learning, such as network infrastructures or mobile devices have not been listed at all. Nonetheless, these of course play a key role in enabling technology-enhanced learning.
References, where included, have been selected to be relatively modern and introductory to the category.
- Technologies support the documentation of learning activities and results
- Technologies support the distribution and consumption of digital learning materials (including learning management systems, MOOCs)
- Communication technologies and social software featuressupport discussions between learners as well as between learners and teachers (cp. Stahl et al., 2014).
- Virtual simulations support experimentation that is safer, sometimes cheaper, or even impossible in reality (cp. de Jong, 1991).
- Data analytics are used to derive insights about learning activities and results from all kinds of sources for a wide variety of stakeholders. This includes learners, who shall be supported in learning; teachers, who shall be supported in teaching; and institutions, who shall be supported in institutional decision making thereby impacting learning (cp. Siemens, 2013).
- Intelligent technologies proactively recommend learning materials and relevant people and guide learning activities in complement or as substitute to human teachers (recommender systems – cp. Manouselis et al., 2010; intelligent tutoring – see Baker, 2016 for a critical discussion that includes an overview of intelligent tutoring literature).
- Gaming technologies finally design for learning in a playful environment (serious games or learning games – cp. van Eck , 2006 for an overview).
(Baker, 2016) Baker, R. Stupid Tutoring Systems, Intelligent Humans. International Journal of Artificial Intelligence in Education, 2016, Vol. 26/2, p.600-614.
(de Jong, 1991) De Jong, T. Learning and instruction with computer simulations. Education and Computing, 1991, Vol. 6/3, p. 217-229.
(Manouselis et al., 2010) Manouselis, N.; Drachsler, H.; Vuroikari, R.; Hummel, H. & Koper, R. Recommender Systems in Technology Enhanced Learning. Recommender Systems Handbook, Springer, 2010, p. 387-415.
(Siemens, 2013) Siemens, G. Learning Analytics: The Emergence of a Discipline. American Behavioral Scientist, 2013, Vol. 57, No. 10, p. 1380-1400.
(Stahl et al., 2014) Stahl, G., Koschmann, T. & Suthers, D. Computer-Supported Collaborative Learning. The Cambridge Handbook of the Learning Sciences, Cambridge University Press, Ch.24, p. 479-500, 2014.
(van Eck, 2006) Van Eck, R. Digital Game-Based Learning: It’s Not Just the digital Natives Who Are Restless. Educause Review, 2006, Vol 41/2, p.16-30.