Multiple Mechanisms for Deep Learning: Overcoming Diminishing Returns in Instructional Systems
- Cite this paper as:
- Ohlsson S. (2011) Multiple Mechanisms for Deep Learning: Overcoming Diminishing Returns in Instructional Systems. In: Biswas G., Bull S., Kay J., Mitrovic A. (eds) Artificial Intelligence in Education. AIED 2011. Lecture Notes in Computer Science, vol 6738. Springer, Berlin, Heidelberg
The design of instructional materials in general and intelligent tutoring systems in particular should be guided by what is known about learning. The purpose of an instructional system is, after all, to supply the cognitive mechanisms in the learner’s mind with the information they need to create new knowledge. It is therefore imperative that the design of instruction is based on explicit models of those mechanisms. From this point of view, research has to date been characterized by two conceptual limitations. The first limitation is that systems are designed to teach to a narrow set of learning mechanisms, sometimes even a single one. There are signs that attempts to build intelligent tutoring systems that address a single mode of learning encounter diminishing returns, in terms of student improvement, with respect to implementation effort. The reason is that people learn in multiple ways. In this talk, I argue that there are approximately nine distinct modes of learning cognitive skills. To be maximally effective, instruction should support all nine modes of learning. This is the way to overcome the diminishing returns of tutoring systems with a narrow bandwidth. The second limitation is the traditional focus in both the science of learning and the practice of instruction on additive or monotonic learning: That is, learning in which the student extends his/her knowledge base without reformulating the knowledge he/she possessed at the outset. Additive extensions of a person’s knowledge are certainly real and important, but they do not exhaust the types of learning of which human beings are capable. In many learning scenarios, the learner must overcome or override the implications of prior knowledge in order to learn successfully. This requires cognitive mechanisms that transform or reject the prior knowledge, in addition to building new knowledge. In this talk, I provide an outline of the essential characteristics of such non-monotonic learning processes. I end the talk by spelling out some implications of the multiple-mechanisms and non-monotonicity principles for the future development of instructional systems.