{"title":"Comprehension First: Evaluating a Novel Pedagogy and Tutoring System for Program Tracing in CS1","authors":"Greg L. Nelson, Benjamin Xie, Amy J. Ko","doi":"10.1145/3105726.3106178","DOIUrl":null,"url":null,"abstract":"What knowledge does learning programming require? Prior work has focused on theorizing program writing and problem solving skills. We examine program comprehension and propose a formal theory of program tracing knowledge based on control flow paths through an interpreter program's source code. Because novices cannot understand the interpreter's programming language notation, we transform it into causal relationships from code tokens to instructions to machine state changes. To teach this knowledge, we propose a comprehension-first pedagogy based on causal inference, by showing, explaining, and assessing each path by stepping through concrete examples within many example programs. To assess this pedagogy, we built PLTutor, a tutorial system with a fixed curriculum of example programs. We evaluate learning gains among self-selected CS1 students using a block randomized lab study comparing PLTutor with Codecademy, a writing tutorial. In our small study, we find some evidence of improved learning gains on the SCS1, with average learning gains of PLTutor 60% higher than Codecademy (gain of 3.89 vs. 2.42 out of 27 questions). These gains strongly predicted midterms (R2=.64) only for PLTutor participants, whose grades showed less variation and no failures.","PeriodicalId":267640,"journal":{"name":"Proceedings of the 2017 ACM Conference on International Computing Education Research","volume":"83 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"84","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2017 ACM Conference on International Computing Education Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3105726.3106178","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 84
Abstract
What knowledge does learning programming require? Prior work has focused on theorizing program writing and problem solving skills. We examine program comprehension and propose a formal theory of program tracing knowledge based on control flow paths through an interpreter program's source code. Because novices cannot understand the interpreter's programming language notation, we transform it into causal relationships from code tokens to instructions to machine state changes. To teach this knowledge, we propose a comprehension-first pedagogy based on causal inference, by showing, explaining, and assessing each path by stepping through concrete examples within many example programs. To assess this pedagogy, we built PLTutor, a tutorial system with a fixed curriculum of example programs. We evaluate learning gains among self-selected CS1 students using a block randomized lab study comparing PLTutor with Codecademy, a writing tutorial. In our small study, we find some evidence of improved learning gains on the SCS1, with average learning gains of PLTutor 60% higher than Codecademy (gain of 3.89 vs. 2.42 out of 27 questions). These gains strongly predicted midterms (R2=.64) only for PLTutor participants, whose grades showed less variation and no failures.
学习编程需要哪些知识?先前的工作集中在理论化程序编写和解决问题的技能。我们研究了程序理解,并提出了一种基于控制流路径的程序跟踪知识的形式化理论,该理论通过解释器程序的源代码。由于新手无法理解解释器的编程语言符号,我们将其转换为从代码符号到指令再到机器状态变化的因果关系。为了教授这些知识,我们提出了一种基于因果推理的理解优先教学法,通过在许多示例程序中逐步通过具体示例来展示、解释和评估每条路径。为了评估这种教学方法,我们建立了PLTutor,这是一个带有固定课程示例程序的导师制系统。我们使用一项比较PLTutor和Codecademy写作教程的块随机实验室研究来评估自我选择的CS1学生的学习收益。在我们的小型研究中,我们发现了一些证据表明SCS1的学习收益有所提高,PLTutor的平均学习收益比Codecademy高60%(27个问题中的3.89 vs 2.42的收益)。这些收益强烈地预测了PLTutor参与者的期中考试(R2=.64),他们的成绩变化较小,没有不及格。