RL Techniques for Personalizing a Curriculum
One of the most extensively researched RL applications in education is to design an educational approach that can train an instructional policy to provide personalized learning materials to students. In such a situation, an RL agent is trained to generate an instructional policy in an intelligent tutoring system, with the student forming an integral part of the environment [1,2]. The instructional policy is responsible for keeping track of the student’s response history and finding ways to optimize his or her long-term learning. The intricacy of the prerequisite dependencies and the curriculum’s organizational structure are essential elements that affect student progress and, in turn, graduation rates. However, we are not aware of any closed-form techniques for calculating the correlation between a curriculum’s complexity and the percentage of students who successfully complete it. In [3], a novel approach is presented for quantifying this relationship using an MDP. The MDP is an appropriate method for addressing such a problem since student growth is non-deterministic and because their states change throughout each semester. See also [4].
Example:
An example for personalizing a curriculum using RL is the characterization of complex curricular patterns in engineering programs, see [4].
Reference:
[1] Sutton, R.S.; Barto, A.G. Reinforcement Learning: An Introduction; MIT Press: Cambridge, MA, USA, 2018.
[2] Durik, A.M.; Hulleman, C.S.; Harackiewicz, J.M. One size fits some: Instructional enhancements to promote interest. In Interest in Mathematics and Science Learning; American Educational Research
Association location: Washington, DC, USA, 2015; pp. 49–62
[3] Slim, A.; Al Yusuf, H.; Abbas, N.; Abdallah, C.T.; Heileman, G.L.; Slim, A. A Markov Decision Processes Modeling for Curricular Analytics. In Proceedings of the 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA ), Virtually Online, 13–15 December 2021; IEEE: Piscataway, NJ, USA, 2021; pp. 415–421.
[4] Fahad Mon, B.; Wasfi, A.; Hayajneh, M.; Slim, A.; Abu Ali, N. Reinforcement Learning in Education: A Literature Review. Informatics 2023, 10, 74. https://doi.org/10.3390/informatics10030074
[2] Durik, A.M.; Hulleman, C.S.; Harackiewicz, J.M. One size fits some: Instructional enhancements to promote interest. In Interest in Mathematics and Science Learning; American Educational Research
Association location: Washington, DC, USA, 2015; pp. 49–62
[3] Slim, A.; Al Yusuf, H.; Abbas, N.; Abdallah, C.T.; Heileman, G.L.; Slim, A. A Markov Decision Processes Modeling for Curricular Analytics. In Proceedings of the 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA ), Virtually Online, 13–15 December 2021; IEEE: Piscataway, NJ, USA, 2021; pp. 415–421.
[4] Fahad Mon, B.; Wasfi, A.; Hayajneh, M.; Slim, A.; Abu Ali, N. Reinforcement Learning in Education: A Literature Review. Informatics 2023, 10, 74. https://doi.org/10.3390/informatics10030074
Author of the tip:
Ivo Nowak
HAW Hamburg