Utilizing programming traces to explore and model the dimensions of novices' code‐writing skill
Utilizing programming traces to explore and model the dimensions of novices' code‐writing skill
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利用编程痕迹探索和建模新手代码维度——写作能力
DOI：
10.1002/cae.22622
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发表时间：
2023
影响因子：
2.9
通讯作者：
Fan, Aysa Xuemo
中科院分区：
文献类型：
--
作者：
Zhang, Yingbin;Paquette, Luc;Pinto, Juan D.;Fan, Aysa Xuemo
研究发现，大多数新手程序员编写代码的熟练程度很低。然而，目前还不清楚哪些子技能构成了代码编写，哪些子技能是新手程序员所努力的。本研究利用程式设计的痕迹，以确定潜在的子技能，构成代码编写，使教师可以提供具体的指导，对薄弱的子技能。数据收集从本科课程教学介绍计算机科学的Java。614名学生在基于网络的学习系统中提交了家庭作业编程问题。根据提交的痕迹，我们计算了11个与正确性和学生在提交上花费的时间相关的特征。我们对随机选择的三分之二的学生进行了探索性因素分析，并确定了四个因素。第一个因素，代码风格熟练程度，主要与代码风格错误。第二，句法熟练度，涉及编译错误。第三个是语义熟练度，涉及运行时和逻辑错误。第四，语法调试熟练度，涉及修复编译器和代码风格错误所需的成功率和时间。对其余三分之一的数据进行的验证性因素分析支持四因素结构。因子模型显示了开发模型的数据集与两个新数据集之间的测量不变性，一个来自相同的样本，但在不同的时间点收集，另一个来自不同的样本和背景（现场课程与在线课程）。这些因素与以前的编程能力，编程语言熟悉程度和未来的考试表现有关。这些关联为因子模型提供了有效性证据。
Studies have found that most novice programmers have low proficiency in writing code. However, it is unclear what subskills compose code writing and which subskills novice programmers struggle with. This study utilizes programming traces to identify latent subskills that constitute code writing so that teachers can offer specific instruction on the weak subskills. Data were collected from an undergraduate course teaching introductory computer science in Java. Six hundred and fourteen students made submissions to homework programming questions in a web‐based learning system. Based on the submission traces, we computed 11 features related to correctness and time students spent on their submissions. We conducted an exploratory factor analysis on two‐thirds of students selected randomly and identified four factors. The first factor, code style proficiency, was mainly related to code style errors. The second, syntactic proficiency, concerned compiler errors. The third is semantic proficiency, which concerns runtime and logic errors. The fourth, syntactic debugging proficiency, concerned the success rate and time required for fixing compiler and code style errors. A confirmatory factor analysis conducted on the remaining one‐third of the data supported the four‐factor structure. The factor model showed measurement invariance between the data set where the model was developed and two new datasets, one from the same sample but collected at a different time point and another from a different sample and context (onsite course vs. online course). The factors were related to prior programming abilities, programming language familiarity, and future exam performance. These associations provided validity evidence for the factor model.
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