TY - GEN
T1 - Multi-armed Bandit Algorithms for Adaptive Learning
T2 - 22nd International Conference on Artificial Intelligence in Education, AIED 2021
AU - Mui, John
AU - Lin, Fuhua
AU - Dewan, M. Ali Akber
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Adaptive learning
KW - Bandit algorithms
KW - Exploration and exploitation
KW - Multi-armed bandit algorithm
KW - Personalized learning
UR - http://www.scopus.com/inward/record.url?scp=85111438602&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-78270-2_49
DO - 10.1007/978-3-030-78270-2_49
M3 - Published Conference contribution
AN - SCOPUS:85111438602
SN - 9783030782696
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 273
EP - 278
BT - Artificial Intelligence in Education - 22nd International Conference, AIED 2021, Proceedings
A2 - Roll, Ido
A2 - McNamara, Danielle
A2 - Sosnovsky, Sergey
A2 - Luckin, Rose
A2 - Dimitrova, Vania
Y2 - 14 June 2021 through 18 June 2021
ER -