TITLE: 
Multi-Armed Bandit Algorithms for Adaptive Learning: A Survey

ABSTRACT:
Adaptive learning aims to provide each student individual tasks specifically tailed to his/her strengths and weaknesses. However, it is challenging to realize it, overcoming the complexity issue in online learning. There are many unsolved problems such as knowledge component sequencing, activity sequencing, exercise sequencing, question sequencing, and pedagogical strategy, to realize adaptive learning. Bandit algorithms are particularly suitable to model the process of planning and using feedback on the outcome of that decision to inform future decisions. They are finding their way into practical applications in various areas especially in online platforms where data is readily available, and automation is the only way to scale. This paper presents a survey on bandit algorithms for facilitating adaptive learning in different settings. The findings indicate that the various bandit algorithms have great potential to solve the above problems. Also, we discuss issues and challenges of developing and using adaptive learning systems based on the multi-armed bandit framework.

AUTHORS:
John Mui, Fuhua Lin, Ali Dewan

NOTE:
Presented in the workshop as part of the ENCORE track. This paper is from AIED 2021 conference. The paper can be accessed at the following link: https://link.springer.com/chapter/10.1007/978-3-030-78270-2_49