Forming beneficial teams of students in massive online classes

R. Agrawal, Behzad Golshan, Evimaria Terzi
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引用次数: 12

Abstract

Given a class of large number of students, each exhibiting a different ability level, how can we form teams of students so that the expected performance of team members improves due to team participation? We take a computational perspective and formally define two versions of such team-formation problem: the MAXTEAM and the MAXPARTITION problems. The first asks for the identification of a single team of students that improves the performance of most of the participating team members. The second asks for a partitioning of students into non-overlapping teams that also maximizes the benefit of the participating students. We show that the first problem can be solved optimally in polynomial time, while the second is NP-complete. For the MAXPARTITION problem, we also design an efficient approximate algorithm for solving it. Our experiments with generated data coming from different distributions demonstrate that our algorithm is significantly better than any of the popular strategies for dividing students in a class into sections.
在大规模在线课程中组建有益的学生团队
一个班级有很多学生,每个人的能力水平都不一样,我们如何组成学生团队,让团队成员的预期表现因为团队参与而提高?我们从计算的角度正式定义了这类团队形成问题的两个版本:MAXTEAM和MAXPARTITION问题。第一个问题要求确定一个学生团队,该团队可以提高大多数参与团队成员的表现。第二种方法要求将学生分成不重叠的小组,这样也能最大限度地提高参与学生的利益。我们证明了第一个问题可以在多项式时间内最优解决,而第二个问题是np完全的。对于MAXPARTITION问题,我们还设计了一个高效的近似算法来求解。我们对来自不同分布的生成数据进行的实验表明,我们的算法明显优于将班级中的学生划分为小组的任何流行策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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