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

Autor: Ayse Aslan, Iris F. A. Vis, Ilke Bakir
Přispěvatelé: Research programme OPERA
Rok vydání: 2020
Předmět:
Timetabling
Hyper-heuristics
Dynamic thompson sampling
Personalized learning
OR in education
Information Systems and Management
Optimization problem
General Computer Science
Computer science
0211 other engineering and technologies
Dynamic thompson sampling
02 engineering and technology
Personalized learning
Hyper-heuristics
Management Science and Operations Research
Machine learning
computer.software_genre
Industrial and Manufacturing Engineering
0502 economics and business
Local search (optimization)
Timetabling
050210 logistics & transportation
021103 operations research
business.industry
Heuristic
05 social sciences
Solver
OR in education
Modeling and Simulation
Artificial intelligence
Decomposition method (constraint satisfaction)
Hyper-heuristic
Heuristics
business
computer
Thompson sampling
Zdroj: European Journal of Operational Research, 286(2), 673-688. ELSEVIER SCIENCE BV
ISSN: 0377-2217
DOI: 10.1016/j.ejor.2020.03.038
Popis: 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.
Databáze: OpenAIRE
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