A dynamic thompson sampling hyper-heuristic framework for learning activity planning in personalized learning

Personalized learning is emerging in schools as an alternative to one-size-fits-all education. This study introduces and explores a weekly demand-driven flexible learning activity planning problem of own-pace own-method personalized learning. The introduced problem is a computationally intractable optimization problem involving many decision dimensions and also many soft constraints. We propose batch and decomposition methods to generate good-quality initial solutions and a dynamic Thompson sampling based hyper-heuristic framework, as a local search mechanism, which explores the large solution space of this problem in an integrative way. The characteristics of our test instances comply with average secondary schools in the Netherlands and are based on expert opinions and surveys. The experiments, which benchmark the proposed heuristics against Gurobi MIP solver on small instances, illustrate the computational challenge of this problem numerically. According to our experiments, the batch method seems quicker and also can provide better quality solutions for the instances in which resource levels are not scarce, while the decomposition method seems more suitable in resource scarcity situations. The dynamic Thompson sampling based online learning heuristic selection mechanism is shown to provide significant value to the performance of our hyper-heuristic local search. We also provide some practical insights; our experiments numerically demonstrate the alleviating effects of large school sizes on the challenge of satisfying high-spread learning demands.