Predicting at-risk novice Java programmers through the analysis of online protocols

Emily S. Tabanao, M. Rodrigo, Matthew C. Jadud
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引用次数: 91

Abstract

In this study, we attempted to quantify indicators of novice programmer progress in the task of writing programs, and we evaluated the use of these indicators for identifying academically at-risk students. Over the course of nine weeks, students completed five different graded programming exercises in a computer lab. Using an instrumented version of BlueJ, an integrated development environment for Java, we collected novice compilations and explored the errors novices encountered, the locations of these errors, and the frequency with which novices compiled their programs. We identified which frequently encountered errors and which compilation behaviors were characteristic of at-risk students. Based on these findings, we developed linear regression models that allowed prediction of students' scores on a midterm exam. However, the models derived could not accurately predict the at-risk students. Although our goal of identifying at-risk students was not attained, we have gained insights regarding the compilation behavior of our students, which may help us identify students who are in need of intervention.
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通过对在线协议的分析来预测Java新手程序员的风险
在这项研究中,我们试图量化新手程序员在编写程序任务中的进度指标,并评估这些指标在识别学业上有风险的学生方面的使用。在九周的课程中,学生们在计算机实验室完成了五个不同等级的编程练习。使用Java集成开发环境BlueJ的仪器化版本,我们收集了初学者的编译,并探索了初学者遇到的错误、这些错误的位置以及初学者编译程序的频率。我们确定了哪些经常遇到错误,哪些编译行为是高危学生的特征。基于这些发现,我们开发了线性回归模型,可以预测学生在期中考试中的分数。然而,所建立的模型并不能准确地预测有风险的学生。虽然我们没有达到识别有风险学生的目标,但我们对学生的编译行为有了深入的了解,这可能有助于我们识别需要干预的学生。
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