Information Age Education Blog
Individualizing Education with AI
History of Computing for Learning in Education
IAE Blog Guest Entry
This article was originally posted 9/30/2016 at https://openeducativesystems.wordpress.com/2016/09/30/individualizing-education-with-ai/. Used with permission of the author. Quoting from the Wikipedia:
Liza Loop is an educational technology pioneer, futurist, technical author, and consultant. She is notable for her early use of computers in education, her creation of a public-access computer center, consulting work with Atari, Apple, Radio Shack and others as well as philosophical musings on the future of learning environments from the 1970s on.
As I surf the Web, I find an increasing number of posts about “deep learning.” I’m always disappointed to discover the learner is a neural network computing machine, not a human – not even an animal. See, for example Deep Learning in a Nutshell: Reinforcement in Learning (Dettmers, 9/8/2016).
I’d like to apply this kind of deep machine learning to a problem in human education: the matching of learners’ characteristics (background knowledge, learning styles, and goals) with learning objects (more properly labeled “teaching objects” including Open Educational Resources, free-lance teachers, and communities of practice). The machine (AI) would scan the profile of the learner and search online for the most appropriate collection of teaching objects to meet the learner’s goals given his/her abilities and background knowledge.
This is actually not a very challenging logical problem for a computer but it does involve gathering a lot of data and learning (by the machine) about which matches are useful to the learner. Two metadata considerations make this difficult to implement.
First: We don’t have very elaborated ways of describing learner characteristics. At the moment we usually note academic subject, language (natural, such as English, Spanish, etc.) and educational level of the learner. There’s a lot more to know about learning styles (Learning Styles, n.d.).
A few examples include whether the student is:
- self-directed or other-directed
- predominantly visual, auditory or kinesthetic
- intellectual strengths and weaknesses; see Guilford’s Structure of Intellect (Soria, 2004).
- physical abilities/handicaps (accessibility)
- solo or social (independent or classroom)
Second: When we catalog teaching objects Open Educational Resources in publications such as OpenStax CNS, OERCommons, and MIT’s Open CourseWare, we don’t provide the learner with enough information about the resource to figure out whether s/he can benefit from the material offered (OER, n.d.), CCCOER (2016).
Note added by IAE Blog’s editor. Quoting from (OER, n.d.):
The OpenCourseWare movement started in 1999 when the University of Tübingen in Germany published videos of lectures online for its timms initiative (Tübinger Internet Multimedia Server). The OCW movement only took off, however, with the launch of MIT OpenCourseWare at the Massachusetts Institute of Technology (MIT) and the Open Learning Initiative at Carnegie Mellon University in October 2002. The movement was soon reinforced by the launch of similar projects at Yale, Utah State University, the University of Michigan, and the University of California Berkeley.
MIT's reasoning behind OCW was to "enhance human learning worldwide by the availability of a web of knowledge". MIT also stated that it would allow students (including, but not limited to, its own) to become better prepared for classes so that they may be more engaged during a class. Since then, a number of universities have created OCW, some of which have been funded by the William and Flora Hewlett Foundation.]
By combining crowdsourcing of feedback on materials as learners try to use them, and by developing better descriptions of learners, we would have the prerequisites to use deep learning AI’s to teach many more students much more effectively.
In future blogs I’ll explore learner characteristics, OER, and other ideas we might use to implement a new “open educative system” that could support learning in this century better than our current classroom-teacher-school-based “educational systems” do today.
References and Resources
CCCOER (2016). Community Colleege Consortium for Open Educational Resources. Retrieved 10/5/2016 from https://www.oerconsortium.org/.
Dettmers, T. (9/8/2016). Deep learning in a nutshell: Reinforcement in learning. nvida Accelerated Computing. Retrieved 10/5/2016 from https://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-reinforcement-learning/.
Learning Styles (n.d.). Overview of learning styles. Learning-styles-online-co. Retrieved 10/5/2016 from http://www.learning-styles-online.com/overview/.
Loop, L. (2016). Welcome to the History of Computing in Learning and Education Project Wiki. Retrieved 10/5/2016 from http://hcle.wikispaces.com/.
OER (n.d.). Open courseware. Wikipedia. Retrieved 10/5/2016 from https://en.wikipedia.org/wiki/OpenCourseWare.
Soria, M. (2004). Guildford's structure of intellect. In B. Hoffman (ed.), Encyclopedia of educational technology. Retrieved 10/5/2016 , from http://www.etc.edu.cn/eet/eet/articles/structureintellect/start.htm.