I'm guessing "instructional design" is referring to how you model the problem domain being learned so that it's amenable to use with adaptive learning systems?
Most research in this area is drawn from the domain of “Intelligent Tutoring Systems,” (ITSs), thoroughly described in Woolf’s book “Building Intelligent Interactive Tutors.” [WOOLFE2009].
I'd say the classic Intelligent Tutor System construction was pioneered by ADVISOR [BWB2000]. It's a two-agent architecture that first trains a student model on real-world data, then uses the student model to train a pedagogical agent using a more resource intensive algorithm. Most modern ITSs make use of Representation Learning (RL) to train the pedagogical agent.
One of the main techniques for structuring the problem domain is Bayesian Knowledge Tracing (BKT) [CA1995], where you model the domain being learned as a Bayesian skills network; i.e. a 4-tuple of probabilities (init, learning, guess, slip), updated in a Bayesian fashion. An excellent survey work in this area is given in [BGTPF2010].
If you're interested in the RL part of the problem, and how it can work with this setup you might want to look into partially observable Markov decision process — POMDPs. Emma Brunskill has done some nice work in this area.
[WOOLF2009]: Woolf, Beverly Park. 2009. Building Intelligent Interactive Tutors: Student-Centered Strategies for Revolutionizing e-Learning. Amsterdam ; Boston: Morgan Kaufmann Publishers/Elsevier.
[BWB2000]: Beck, Joseph, Beverly Park Woolf, and Carole R. Beal. "ADVISOR: A machine learning architecture for intelligent tutor construction." AAAI/IAAI 2000 (2000): 552-557.
[CA1995]: Corbett, Albert T., and John R. Anderson. "Knowledge tracing: Modeling the acquisition of procedural knowledge." User modeling and user-adapted interaction 4, no. 4 (1994): 253-278.
[BGTPF2010]: Brunskill, Emma, Sunil Garg, Clint Tseng, Joyojeet Pal, and Leah Findlater. "Evaluating an adaptive multi-user educational tool for low-resource environments." In Proceedings of the IEEE/ACM International Conference on Information and Communication Technologies and Development, pp. 13-16. 2010.
Most research in this area is drawn from the domain of “Intelligent Tutoring Systems,” (ITSs), thoroughly described in Woolf’s book “Building Intelligent Interactive Tutors.” [WOOLFE2009].
I'd say the classic Intelligent Tutor System construction was pioneered by ADVISOR [BWB2000]. It's a two-agent architecture that first trains a student model on real-world data, then uses the student model to train a pedagogical agent using a more resource intensive algorithm. Most modern ITSs make use of Representation Learning (RL) to train the pedagogical agent.
One of the main techniques for structuring the problem domain is Bayesian Knowledge Tracing (BKT) [CA1995], where you model the domain being learned as a Bayesian skills network; i.e. a 4-tuple of probabilities (init, learning, guess, slip), updated in a Bayesian fashion. An excellent survey work in this area is given in [BGTPF2010].
If you're interested in the RL part of the problem, and how it can work with this setup you might want to look into partially observable Markov decision process — POMDPs. Emma Brunskill has done some nice work in this area.
[WOOLF2009]: Woolf, Beverly Park. 2009. Building Intelligent Interactive Tutors: Student-Centered Strategies for Revolutionizing e-Learning. Amsterdam ; Boston: Morgan Kaufmann Publishers/Elsevier.
[BWB2000]: Beck, Joseph, Beverly Park Woolf, and Carole R. Beal. "ADVISOR: A machine learning architecture for intelligent tutor construction." AAAI/IAAI 2000 (2000): 552-557.
[CA1995]: Corbett, Albert T., and John R. Anderson. "Knowledge tracing: Modeling the acquisition of procedural knowledge." User modeling and user-adapted interaction 4, no. 4 (1994): 253-278.
[BGTPF2010]: Brunskill, Emma, Sunil Garg, Clint Tseng, Joyojeet Pal, and Leah Findlater. "Evaluating an adaptive multi-user educational tool for low-resource environments." In Proceedings of the IEEE/ACM International Conference on Information and Communication Technologies and Development, pp. 13-16. 2010.