Charlotte Van Petegem's research while affiliated with Ghent University and other places

Predictive learning analytics has been widely explored in educational research to improve student retention and academic success in an introductory programming course in computer science (CS1). General-purpose and interpretable dropout predictions still pose a challenge. Our study aims to reproduce and extend the data analysis of a privacy-first student pass–fail prediction approach proposed by Van Petegem and colleagues (2022) in a different CS1 course. Using student submission and self-report data, we investigated the reproducibility of the original approach, the effect of adding self-reports to the model, and the interpretability of the model features. The results showed that the original approach for student dropout prediction could be successfully reproduced in a different course context and that adding self-report data to the prediction model improved accuracy for the first four weeks. We also identified relevant features associated with dropout in the CS1 course, such as timely submission of tasks and iterative problem solving. When analyzing student behaviour, submission data and self-report data were found to complement each other. The results highlight the importance of transparency and generalizability in learning analytics and the need for future research to identify other factors beyond self-reported aptitude measures and student behaviour that can enhance dropout prediction.

In educational contexts, automated assessment tools (AAT) are commonly used to provide formative feedback on programming exercises. However, designing exercises for AAT remains a laborious task or imposes limitations on the exercises. Most AAT use either output comparison, where the generated output is compared against an expected output, or unit testing, where the tool has access to the code of the submission under test. While output comparison has the advantage of being programming language independent, the testing capabilities are limited to the output. Conversely, unit testing can generate more granular feedback, but is tightly coupled with the programming language of the submission. In this paper, we introduce TESTed, which enables the best of both worlds: combining the granular feedback of unit testing with the programming language independence of output comparison. Educators can save time by designing exercises that can be used across programming languages. Finally, we report on using TESTed in educational practice.

Dodona (dodona.ugent.be) is an intelligent tutoring system for computer programming. It bridges the gap between assessment and learning by providing real-time data and feedback to help students learn better, teachers teach better and educational technology become more effective. We demonstrate how Dodona can be used as a virtual co-teacher to stimulate active learning and support challenge-based education in open and collaborative learning environments. We also highlight some of the opportunities (automated feedback, learning analytics, educational data mining) and challenges (scalable feedback, open internet exams, plagiarism) we faced in practice. Dodona is free for use and has more than 36 thousand registered users across many educational and research institutes, of which 15 thousand new users registered last year. Lowering the barriers for such a broad adoption was achieved by following best practices and extensible approaches for software development, authentication, content management, assessment, security and interoperability, and by adopting a holistic view on computer-assisted learning and teaching that spans all aspects of managing courses that involve programming assignments. The source code of Dodona is available on GitHub under the permissive MIT open-source license.

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.

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.