Max Fowler, David H. Smith IV, Mohammed Hassan, Seth Poulsen, Matthew West, C. Zilles
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引用次数: 8
摘要
Lopez和Lister首先为编程入门学生提供了代码阅读、跟踪和编写技能层次的证据。对这种层次结构的进一步支持可以帮助计算机科学教育者对课程内容进行排序,以最好地培养学生的编程技能。本研究旨在复制CS1中稍微简化的技能层次,使用更多的学生(600+ vs. 38)在非主要的Python入门课程中进行计算机考试。我们还探讨了其他可能的层次结构的有效性。方法收集4种考试题目的学生成绩资料。使用结构方程建模来推导每个考试的层次结构。我们发现了多个最适合的结构模型。最初的等级制度不会出现在“最佳”候选者中,但类似的模型会出现。我们还确定,我们的方法为我们提供了技能之间的相关性,但没有回答一个更基本的问题:这些技能的理想教学顺序是什么?这个建模工作对于理解与代码相关的基本技能之间可能的相关性是有价值的。然而,在某一时刻分析学生在这些技能上的表现并不足以决定教学顺序。我们提出可能的研究设计来探索这个更具可操作性的研究问题。
Reevaluating the relationship between explaining, tracing, and writing skills in CS1 in a replication study
ABSTRACT Background and Context Lopez and Lister first presented evidence for a skill hierarchy of code reading, tracing, and writing for introductory programming students. Further support for this hierarchy could help computer science educators sequence course content to best build student programming skill. Objective This study aims to replicate a slightly simplified hierarchy of skills in CS1 using a larger body of students (600+ vs. 38) in a non-major introductory Python course with computer-based exams. We also explore the validity of other possible hierarchies. Method We collected student score data on 4 kinds of exam questions. Structural equation modeling was used to derive the hierarchy for each exam. Findings We find multiple best-fitting structural models. The original hierarchy does not appear among the “best” candidates, but similar models do. We also determined that our methods provide us with correlations between skills and do not answer a more fundamental question: what is the ideal teaching order for these skills? Implications This modeling work is valuable for understanding the possible correlations between fundamental code-related skills. However, analyzing student performance on these skills at a moment in time is not sufficient to determine teaching order. We present possible study designs for exploring this more actionable research question.
期刊介绍:
Computer Science Education publishes high-quality papers with a specific focus on teaching and learning within the computing discipline. The journal seeks novel contributions that are accessible and of interest to researchers and practitioners alike. We invite work with learners of all ages and across both classroom and out-of-classroom learning contexts.