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In computing classrooms, building an open-ended programming project engages students in the process of designing and implementing an idea of their own choice. An explicit planning process has been shown to help students build more complex and ambitious open-ended projects. However, novices encounter difficulties in exploring and creatively expressing ideas during planning. We present Idea Builder, a storyboarding-based planning system to help novices visually express their ideas. Idea Builder includes three features: 1) storyboards to help students express a variety of ideas that map easily to programming code, 2) animated example mechanics with example actors to help students explore the space of possible ideas supported by the programming environments, and 3) synthesized starter code to help students easily transition from planning to programming. Through two studies with high school coding workshops, we found that students self-reported as feeling creative and feeling easy to communicate ideas; having access to animated example mechanics of an actor help students to build those actors in their plans and projects; and that most students perceived the synthesized starter code from Idea Builder as helpful and time-saving. 

Software testing is a critical skill for computing students, but learning and practicing testing can be challenging, particularly for beginners. A recent study suggests that a lightweight testing checklist that contains testing strategies and tutorial information could assist students in writing quality tests. However, students expressed a desire for more support in knowing how to test the code/scenario. Moreover, the potential costs and benefits of the testing checklist are not yet examined in a classroom setting. To that end, we improved the checklist by integrating explicit testing strategies to it (ETS Checklist), which provide step-by-step guidance on how to transfer semantic information from instructions to the possible testing scenarios. In this paper, we report our experiences in designing explicit strategies in unit testing, as well as adapting the ETS Checklist as optional tool support in a CS1.5 course. With the quantitative and qualitative analysis of the survey responses and lab assignment submissions generated by students, we discuss students' engagement with the ETS Checklists. Our results suggest that students who used the checklist intervention had significantly higher quality in their student-authored test code, in terms of code coverage, compared to those who did not, especially for assignments earlier in the course. We also observed students' unawareness of their need for help in writing high-quality tests. 

Educational Data Mining in Computer Science Education (CSEDM) is an interdisciplinary research community that combines discipline-based computing education research (CER) with educational data-mining (EDM)  to advance knowledge in ways that go beyond what either research community could do on its own.

The JEDM Special Issue on CSEDM received a total of 12 submissions. Each submission was reviewed by at least three reviewers, who brought expertise from both the EDM and CER communities, as well as one of special issue editors. Ultimately, three papers were accepted, for an acceptance rate of 25%.

 

These three papers cover a variety of important topics in CSEDM research. Edwards et al. discuss the challenges of collecting, sharing and analyzing programming data, and contribute two high-quality CS datasets. Gitinabard et al. contribute new approaches for analyzing data from pairs of students working on programs together, and show how such data can inform classroom instruction. Finally, Zhang et al. contribute a novel model for predicting students' programming performance based on their past performance. Together, these papers showcase the complexities of data, analytics and modeling in the domain of CS, and contribute to our understanding of how students learn in CS classrooms.

 

Software testing is an essential skill for computer science students. Prior work reports that students desire support in determining what code to test and which scenarios should be tested. In response to this, we present a lightweight testing checklist that contains both tutorial information and testing strategies to guide students in what and how to test. To assess the impact of the testing checklist, we conducted an experimental, controlled A/B study with 32 undergraduate and graduate students. The study task was writing a test suite for an existing program. Students were given either the testing checklist (the experimental group) or a tutorial on a standard coverage tool with which they were already familiar (the control group). By analyzing the combination of student-written tests and survey responses, we found students with the checklist performed as well as or better than the coverage tool group, suggesting a potential positive impact of the checklist (or at minimum, a non-negative impact). This is particularly noteworthy given the control condition of the coverage tool is the state of the practice. These findings suggest that the testing tool support does not need to be sophisticated to be effective. 

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. 

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 open-ended 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. 

The goal of this workshop is to bring together the existing community of researchers working on Infrastructure Design for Data-Intensive Research in Computer Science Education and a community of Learning at Scale researchers focused on Computer Science Education. While both communities share many similar goals and could greatly benefit from each other work, the interaction between the communities is small. We hope that the proposed workshop will be instrumental in bringing together like-minded researchers from different communities, establishing collaboration, and expanding the scope of infrastructure project to address critical scaling issues. 

Project-based learning can encourage and motivate students to learn through exploring their own interests, but introduces special challenges for novice programmers. Recent research has shown that novice students perceive themselves to be "bad at programming, especially when they do not know how to start writing a program, or need to create a plan before getting started. In this paper, we present PlanIT, a guided planning tool integrated with the Snap! programming environment designed to help novices plan and program their open-ended projects. Within PlanIT, students can add a description for their project, use a to do list to help break down the steps of implementation, plan important elements of their program including actors, variables, and events, and view related example projects. We report findings from a pilot study of high school students using PlanIT, showing that students who used the tool learned to make more specific and actionable plans. Results from student interviews show they appreciate the guidance that PlanIT provides, as well as the affordances it offers to more quickly create program elements.