Optimizing Peer Learning in Online Groups with Affinities
Optimizing Peer Learning in Online Groups with Affinities
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DOI：
10.1145/3292500.3330945
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发表时间：
2019-07
期刊：
影响因子：
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通讯作者：
M. Esfandiari;Dong Wei;S. Amer-Yahia;Senjuti Basu Roy
中科院分区：
文献类型：
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作者：
M. Esfandiari;Dong Wei;S. Amer-Yahia;Senjuti Basu Roy
我们调查在线小组的形成，成员寻求通过协作来提高他们的学习潜力。我们采用两种常见的学习模型：LpA（每个成员向所有技能较高的成员学习）和 LpD（其中技能最差的成员向技能最高的成员学习）。我们提出了形成群体的问题，目的是在不同亲和力结构下优化同伴学习：AffD，其中群体亲和力在所有成员之间最小，AffC，其中群体亲和力在指定成员（例如，最不熟练或最熟练）和所有其他成员之间最小。这产生了多目标优化问题的多种变体。我们提出这些问题的原则性建模，并研究理论和算法挑战。我们首先给出硬度结果，然后开发具有恒定近似因子的计算高效的算法。我们的真实数据实验具有统计显着性，表明考虑亲和力的分组可以改善学习。我们广泛的合成实验证明了我们解决方案的定性和可扩展性。
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 multiobjective 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.