TY - GEN
T1 - Combining difficulty ranking with multi-armed bandits to sequence educational content
AU - Segal, Avi
AU - Ben David, Yossi
AU - Williams, Joseph Jay
AU - Gal, Kobi
AU - Shalom, Yaar
N1 - Publisher Copyright: © Springer International Publishing AG, part of Springer Nature 2018.
PY - 2018/1/1
Y1 - 2018/1/1
N2 - We address the problem of how to personalize educational content to students in order to maximize their learning gains over time. We present a new computational approach to this problem called MAPLE (Multi-Armed Bandits based Personalization for Learning Environments) that combines difficulty ranking with multi-armed bandits. Given a set of target questions MAPLE estimates the expected learning gains for each question and uses an exploration-exploitation strategy to choose the next question to pose to the student. It maintains a personalized ranking over the difficulties of question in the target set and updates it in real-time according to students’ progress. We show in simulations that MAPLE was able to improve students’ learning gains compared to approaches that sequence questions in increasing level of difficulty, or rely on content experts. When implemented in a live e-learning system in the wild, MAPLE showed promising initial results.
AB - We address the problem of how to personalize educational content to students in order to maximize their learning gains over time. We present a new computational approach to this problem called MAPLE (Multi-Armed Bandits based Personalization for Learning Environments) that combines difficulty ranking with multi-armed bandits. Given a set of target questions MAPLE estimates the expected learning gains for each question and uses an exploration-exploitation strategy to choose the next question to pose to the student. It maintains a personalized ranking over the difficulties of question in the target set and updates it in real-time according to students’ progress. We show in simulations that MAPLE was able to improve students’ learning gains compared to approaches that sequence questions in increasing level of difficulty, or rely on content experts. When implemented in a live e-learning system in the wild, MAPLE showed promising initial results.
UR - http://www.scopus.com/inward/record.url?scp=85049373565&partnerID=8YFLogxK
U2 - https://doi.org/10.1007/978-3-319-93846-2_59
DO - https://doi.org/10.1007/978-3-319-93846-2_59
M3 - Conference contribution
SN - 9783319938455
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 317
EP - 321
BT - Artificial Intelligence in Education - 19th International Conference, AIED 2018, Proceedings
A2 - Luckin, Rose
A2 - Porayska-Pomsta, Kaska
A2 - du Boulay, Benedict
A2 - Mavrikis, Manolis
A2 - Penstein Rosé, Carolyn
A2 - McLaren, Bruce
A2 - Martinez-Maldonado, Roberto
A2 - Hoppe, H. Ulrich
PB - Springer Verlag
T2 - 19th International Conference on Artificial Intelligence in Education, AIED 2018
Y2 - 27 June 2018 through 30 June 2018
ER -