research-article

Grouping students in educational settings

Authors:

Rakesh Agrawal,

Behzad Golshan,

Evimaria TerziAuthors Info & Claims

KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 1017 - 1026

https://doi.org/10.1145/2623330.2623748

Published: 24 August 2014 Publication History

Abstract

Given a class of large number of students, each exhibiting a different ability level, how can we group them into sections so that the overall gain for students is maximized? This question has been a topic of central concern and debate amongst social scientists and policy makers for a long time. We propose a framework for rigorously studying this question, taking a computational perspective. We present a formal definition of the grouping problem and investigate some of its variants. Such variants are determined by the desired number of groups as well as the definition of the gain for each student in the group. We focus on two natural instantiations of the gain function and we show that for both of them the problem of identifying a single group of students that maximizes the gain among its members can be solved in polynomial time. The corresponding partitioning problem, where the goal is to partition the students into non-overlapping groups appear to be much harder. However, the algorithms for the single-group version can be leveraged for solving the more complex partitioning problem. Our experiments with generated data coming from different distributions demonstrate that our algorithm is significantly better than the current strategies in vogue for dividing students in a class into sections.

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Ma HLi JZhu HTang WHuang ZTang Y(2024)Collaborative Optimization of Learning Team Formation Based on Multidimensional Characteristics and Constraints Modeling: A Team Leader-Centered Approach via E-CARGOIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.322476211:1(184-196)Online publication date: Feb-2024
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Index Terms

Grouping students in educational settings
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2014

2028 pages

ISBN:9781450329569

DOI:10.1145/2623330

General Chairs:
Sofus Macskassy
Facebook
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Claudia Perlich
Dstillery
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Program Chairs:
Jure Leskovec
Stanford University
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Wei Wang
UCLA
,
Rayid Ghani
University of Chicago

Copyright © 2014 ACM.

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Published: 24 August 2014

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KDD '14: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 24 - 27, 2014

New York, New York, USA

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KDD '14 Paper Acceptance Rate 151 of 1,036 submissions, 15%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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https://dl.acm.org/doi/10.24963/ijcai.2024/250
Ma HLi JZhu HTang WHuang ZTang Y(2024)Collaborative Optimization of Learning Team Formation Based on Multidimensional Characteristics and Constraints Modeling: A Team Leader-Centered Approach via E-CARGOIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.322476211:1(184-196)Online publication date: Feb-2024
https://doi.org/10.1109/TCSS.2022.3224762
Anaya ALetón ELuque M(2024)Team assembly approach based in social modellingSocial Network Analysis and Mining10.1007/s13278-024-01307-914:1Online publication date: 25-Jul-2024
https://doi.org/10.1007/s13278-024-01307-9
Li PDong SZhang YZhang B(2023)Predicting Task Planning Ability for Learners Engaged in Searching as Learning Based on Tree-Structured Long Short-Term Memory NetworksApplied Sciences10.3390/app13231284013:23(12840)Online publication date: 30-Nov-2023
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Ma HLi JTang YZhu HHuang ZTang W(2023)Universal Optimization Framework: Leader-Centered Learning Team Formation Based on Fuzzy Evaluations of Learners and E-CARGOIEEE Systems, Man, and Cybernetics Magazine10.1109/MSMC.2022.32316989:2(6-17)Online publication date: Apr-2023
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Le Quy TFriege GNtoutsi E(2023)Multi-fair Capacitated Students-Topics Grouping ProblemAdvances in Knowledge Discovery and Data Mining10.1007/978-3-031-33374-3_40(507-519)Online publication date: 27-May-2023
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