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Student Coding Styles as Predictors of Help-Seeking Behavior

机译：学生编码风格作为求助行为的预测指标

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摘要

Recent research in CS education has leveraged machine learning techniques to capture students' progressions through assignments in programming courses based on their code submissions. With this in mind, we present a methodology for creating a set of descriptors of the students' progression based on their coding styles as captured by different non-semantic and semantic features of their code submissions. Preliminary findings show that these descriptors extracted from a single assignment can be used to predict whether or not a student got help throughout the entire quarter. Based on these findings, we plan on developing a model of the impact of teacher intervention on a student's pathway through homework assignments.
机译：CS教育的最新研究已经利用机器学习技术，通过根据代码提交的编程课程作业来捕获学生的学习进度。考虑到这一点，我们提出了一种方法，用于根据学生的编码风格（由其提交的代码的不同非语义和语义特征捕获）来创建一组学生进度的描述符。初步发现表明，从单个作业中提取的这些描述符可用于预测学生在整个季度中是否获得了帮助。基于这些发现，我们计划开发一种教师干预通过作业分配对学生学习途径影响的模型。

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