Probabilistic Use Cases: Discovering Behavioral Patterns for Predicting Certification

Cody A. Coleman, Daniel T. Seaton, Isaac L. Chuang
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引用次数: 56

Abstract

Advances in open-online education have led to a dramatic increase in the size, diversity, and traceability of learner populations, offering tremendous opportunities to study detailed learning behavior of users around the world. This paper adapts the topic modeling approach of Latent Dirichlet Allocation (LDA) to uncover behavioral structure from student logs in a MITx Massive Open Online Course, 8.02x: Electricity and Magnetism. LDA is typically found in the field of natural language processing, where it identifies the latent topic structure within a collection of documents. However, this framework can be adapted for analysis of user-behavioral patterns by considering user interactions with courseware as a ``bag of interactions'' equivalent to the ``bag of words'' model found in topic modeling. By employing this representation, LDA forms probabilistic use cases that clusters students based on their behavior. Through the probability distributions associated with each use case, this approach provides an interpretable representation of user access patterns, while reducing the dimensionality of the data and improving accuracy. Using only the first week of logs, we can predict whether or not a student will earn a certificate with 0.81 ± 0.01 cross-validation accuracy. Thus, the method presented in this paper is a powerful tool in understanding user behavior and predicting outcomes.
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概率用例:发现用于预测认证的行为模式
开放在线教育的进步导致学习者群体的规模、多样性和可追溯性急剧增加,为研究世界各地用户的详细学习行为提供了巨大的机会。本文采用潜在狄利let分配(Latent Dirichlet Allocation, LDA)的主题建模方法,从MITx大规模开放在线课程8.02x:电与磁的学生日志中揭示行为结构。LDA通常用于自然语言处理领域,用于识别文档集合中的潜在主题结构。然而,通过将用户与课件的交互视为相当于主题建模中的“词包”模型的“交互包”,这个框架可以用于分析用户行为模式。通过使用这种表示,LDA形成概率用例,根据学生的行为对他们进行聚类。通过与每个用例相关联的概率分布,该方法提供了用户访问模式的可解释表示,同时降低了数据的维数并提高了准确性。仅使用第一周的日志,我们就可以以0.81±0.01的交叉验证精度预测学生是否会获得证书。因此,本文提出的方法是理解用户行为和预测结果的有力工具。
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