Pass/Fail Prediction in Programming Courses

Abstract

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.

Could the Use of AI in Higher Education Hinder Students With Disabilities? A Scoping Review

“…The output could be a probability to fail [49], [54], [58], [60], [63] or a categorical variable indicating that the student was at risk or not [52], [55], [59], [61], [20]. Additionally, several researchers used combined categorical variables with probabilities to present their outputs or added additional contextual information [42], [46], [47], [51], [56], [57], [59], [62]. This categorization of students is not trivial as supplementary information to understand the predictions was seldom provided.…”

“…the decision-makers did not have access to the information [47], [61]. Van Petegem et al [62] explicitly stated that teachers could use the feature weights to better understand why a student was predicted to pass or fail in a class. Comparatively, Figueiredo and García-Peñalvo [57], and Eagle et al [56] provided teachers with contextual information such as a dashboard visualising the online activities of students in the course or profile descriptions of students.…”

“…One solution is to provide results that may explain why a student is at risk and what could be improved. For instance, Van Petegem et al [62] specifically chose an algorithmic model that allowed for interpretability of the at-risk predictions. Additionally, intervention systems can also be centred on talking and listening to students to understand them better, as was the case in [49] and in [42].…”

University learning style model: Bibliometrics and systematic literature review

“…Although it is true that the cognitive part is very important for the development of the student; but it would have a comprehensive panorama of being developed in values, ethics; in development as a person, who is aware of how to be a collaborative person, who contributes to his society by developing activities that respond to social problems, social pollution and the objectives of sustainable development. Competences are acquired in students according to what has been established in the curricular plan of each university; what comes to do the north of the graduate profile [63], [64]. The skills acquired by the students will depend on the different learning styles that are applied in the class sessions of the different subjects [69], [70].…”

Dodona: Learn to Code with a Virtual Co-teacher that Supports Active Learning

“…Rich metadata is available, allowing for a broad spectrum of research opportunities. This includes pass/fail prediction [24], plagiarism detection [25], recommendations of manual feedback, exercise recommendations [10], and user knowledge modeling [26,27].…”