Identifying Beneficial Learning Behaviors from Large-Scale Interaction Data

被引：0

作者：

Cristus, Miruna ^{[1
]}

Tackstrom, Oscar ^{[1
]}

Tan, Lingyi ^{[1
]}

Pacifici, Valentino ^{[1
]}

机构：

[1] Sana Labs, Stockholm, Sweden

来源：

ARTIFICIAL INTELLIGENCE IN EDUCATION (AIED 2020), PT II | 2020年 / 12164卷

关键词：

Learning behavior; Test preparation; Educational data mining;

D O I：

10.1007/978-3-030-52240-7_67

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Understanding the effect of learning behavior is fundamental to improving learning outcomes. In this paper, we perform a behavioral analysis based on data from a large high-stakes exam preparation platform. By measuring the importance of a set of candidate learning behaviors in predicting final exam outcomes, we identify a suite of beneficial behaviors. In particular, we find that breadth (wide coverage of content per week) and intensity together with consistency (frequent and equal-length practice for a limited period) are most predictive of final exam success rate, among eleven studied behaviors.

引用

页码：371 / 375

页数：5

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