Background Learning to code is increasingly embedded in secondary and higher education curricula, where solving programming exercises plays an important role in the learning process and in formative and summative assessment. Unfortunately, students admit that copying code from each other is a common practice and teachers indicate they rarely use plagiarism detection tools. Objectives We want to lower the barrier for teachers to detect plagiarism by introducing a new source code plagiarism detection tool (Dolos) that is powered by state‐of‐the art similarity detection algorithms, offers interactive visualizations, and uses generic parser models to support a broad range of programming languages. Methods Dolos is compared with state‐of‐the‐art plagiarism detection tools in a benchmark based on a standardized dataset. We describe our experience with integrating Dolos in a programming course with a strong focus on online learning and the impact of transitioning to remote assessment during the COVID‐19 pandemic. Results and Conclusions Dolos outperforms other plagiarism detection tools in detecting potential cases of plagiarism and is a valuable tool for preventing and detecting plagiarism in online learning environments. It is available under the permissive MIT open‐source license at https://dolos.ugent.be. Implications Dolos lowers barriers for teachers to discover, prove and prevent plagiarism in programming courses. This helps to enable a shift towards open and online learning and assessment environments, and opens up interesting avenues for more effective learning and better assessment.
We present a privacy-friendly early-detection framework to identify students at risk of failing in introductory programming courses at university. The framework was validated for two different courses with annual editions taken by higher education students ( N = 2 080) and was found to be highly accurate and robust against variation in course structures, teaching and learning styles, programming exercises and classification algorithms. By using interpretable machine learning techniques, the framework also provides insight into what aspects of practising programming skills promote or inhibit learning or have no or minor effect on the learning process. Findings showed that the framework was capable of predicting students’ future success already early on in the semester.
The process of teaching children to code is often slowed down by the delay in providing feedback on each student's code. Especially in larger classrooms, teachers often lack the time to give individual feedback to each student. That is why it is important to equip children with tools that can provide immediate feedback and thus enhance their independent learning skills. This article presents Blink, a debugging tool specifically designed for Scratch, the most commonly taught programming language for children. Blink comes with basic debugging features such as 'step' and 'pause', allowing precise monitoring of the execution of Scratch programs. It also provides users with more advanced debugging options, such as back-in-time debugging and programmable pause. A group of children attending an extracurricular coding class have been testing the usefulness of Blink. Feedback from these young users indicates that Blink helps them pinpoint programming errors more accurately, and they have expressed an overall positive view of the tool.
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