TY - GEN
T1 - Optimizing peer learning in online groups with affinities
AU - Esfandiari, Mohammadreza
AU - Wei, Dong
AU - Amer-Yahia, Sihem
AU - Roy, Senjuti Basu
N1 - Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/7/25
Y1 - 2019/7/25
N2 - We investigate online group formation where members seek to increase their learning potential via collaboration. We capture two common learning models: LpA where each member learns from all higher skilled ones, and LpD where the least skilled member learns from the most skilled one. We formulate the problem of forming groups with the purpose of optimizing peer learning under different affinity structures: AffD where group affinity is the smallest between all members, and AffC where group affinity is the smallest between a designated member (e.g., the least skilled or the most skilled) and all others. This gives rise to multiple variants of a multi-objective optimization problem. We propose principled modeling of these problems and investigate theoretical and algorithmic challenges. We first present hardness results, and then develop computationally efficient algorithms with constant approximation factors. Our real-data experiments demonstrate with statistical significance that forming groups considering affinity improves learning. Our extensive synthetic experiments demonstrate the qualitative and scalability aspects of our solutions.
AB - We investigate online group formation where members seek to increase their learning potential via collaboration. We capture two common learning models: LpA where each member learns from all higher skilled ones, and LpD where the least skilled member learns from the most skilled one. We formulate the problem of forming groups with the purpose of optimizing peer learning under different affinity structures: AffD where group affinity is the smallest between all members, and AffC where group affinity is the smallest between a designated member (e.g., the least skilled or the most skilled) and all others. This gives rise to multiple variants of a multi-objective optimization problem. We propose principled modeling of these problems and investigate theoretical and algorithmic challenges. We first present hardness results, and then develop computationally efficient algorithms with constant approximation factors. Our real-data experiments demonstrate with statistical significance that forming groups considering affinity improves learning. Our extensive synthetic experiments demonstrate the qualitative and scalability aspects of our solutions.
UR - http://www.scopus.com/inward/record.url?scp=85071186181&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85071186181&partnerID=8YFLogxK
U2 - 10.1145/3292500.3330945
DO - 10.1145/3292500.3330945
M3 - Conference contribution
AN - SCOPUS:85071186181
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 1216
EP - 1226
BT - KDD 2019 - Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PB - Association for Computing Machinery
T2 - 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019
Y2 - 4 August 2019 through 8 August 2019
ER -