Predicting at-risk novice Java programmers through the analysis of online protocols
Predicting at-risk novice Java programmers through the analysis of online protocols
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通过分析在线协议来预测有风险的 Java 新手程序员
DOI：
10.1145/2016911.2016930
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
Matthew C. Jadud
中科院分区：
文献类型：
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作者：
Emily S. Tabanao;M. Rodrigo;Matthew C. Jadud
在这项研究中，我们试图量化新手程序员在编写程序的任务中的进展指标，我们评估了这些指标的使用，以确定学术风险的学生。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BlueJ，一个集成的Java开发环境的仪表化版本，我们收集新手编译，并探讨了新手遇到的错误，这些错误的位置，以及新手编译他们的程序的频率。我们确定了哪些经常遇到的错误，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，可以预测学生的期中考试成绩。但是，所得出的模型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，但我们已经获得了关于学生编译行为的见解，这可能有助于我们识别需要干预的学生。
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.