Open-ended programming increases students' motivation by allowing them to solve authentic problems and connect programming to their own interests. However, such open-ended projects are also challenging, as they often encourage students to explore new programming features and attempt tasks that they have not learned before. Code examples are effective learning materials for students and are well-suited to supporting open-ended programming. However, there is little work to understand how novices learn with examples during open-ended programming, and few real-world deployments of such tools. In this paper, we explore novices' learning barriers when interacting with code examples during open-ended programming. We deployed Example Helper, a tool that offers galleries of code examples to search and use, with 44 novice students in an introductory programming classroom, working on an openended project in Snap!. We found three high-level barriers that novices encountered when using examples: decision, search, and integration barriers. We discuss how these barriers arise and design opportunities to address them.
Understanding students' misconceptions is important for effective teaching and assessment. However, discovering such misconceptions manually can be time-consuming and laborious. Automated misconception discovery can address these challenges by highlighting patterns in student data, which domain experts can then inspect to identify misconceptions. In this work, we present a novel method for the semi-automated discovery of problem-specific misconceptions from students' program code in computing courses, using a state-of-the-art code classification model. We trained the model on a block-based programming dataset and used the learned embedding to cluster incorrect student submissions. We found these clusters correspond to specific misconceptions about the problem and would not have been easily discovered with existing approaches. We also discuss potential applications of our approach and how these misconceptions inform domain-specific insights into students' learning processes. CCS CONCEPTS• Computing methodologies → Learning latent representations; • Applied computing → Education.
Programming environments such as Snap!, Scratch, and Processing engage learners by allowing them to create programming artifacts such as apps and games, with visual and interactive output. Learning programming with such a media-focused context has been shown to increase retention and success rate. However, assessing these visual, interactive projects requires time and laborious manual effort, and it is therefore difficult to offer automated or real-time feedback to students as they work. In this paper, we introduce SnapCheck, a dynamic testing framework for Snap! that enables instructors to author test cases with Condition-Action templates. The goal of SnapCheck is to allow instructors or researchers to author property-based test cases that can automatically assess students' interactive programs with high accuracy. Our evaluation of SnapCheck on 162 code snapshots from a Pong game assignment in an introductory programming course shows that our automated testing framework achieves at least 98 % accuracy over all rubric items, showing potentials to use SnapCheck for auto-grading and providing formative feedback to students.
Block-based programming environments, such as Scratch and Snap!, engage users to create programming artifacts such as games and stories, and share them in an online community. Many Snap! users start programming by reusing and modifying an example project, but encounter many barriers when searching and identifying the relevant parts of the program to learn and reuse. We present Pinpoint, a system that helps Snap! programmers understand and reuse an existing program by isolating the code responsible for specific events during program execution. Specifically, a user can record an execution of the program (including user inputs and graphical output), replay the output, and select a specific time interval where the event of interest occurred, to view code that is relevant to this event. We conducted a small-scale user study to compare users' program comprehension experience with and without Pinpoint, and found suggestive evidence that Pinpoint helps users understand and reuse a complex program more efficiently.
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