Recommendation of programming activities by multi-label classification for a formative assessment of students

Computer programming ability is a type of knowledge that is considered to be quite complex because it demands many cognitive skills and extensive practice to be mastered. However, formative assessment is a strategy that can improve learning. For this reason, we developed a recommender system to aid in making choices on programming practices by recommending classes of activities. This system provides instructors with a means of semi-automatic assessment, with more individualised and accurate activities tailored to the needs of their learners. To achieve this goal, the system of recommendations analyses multidimensional profiles of new students and seeks the best match for them among profiles in the logs of previous recommendations, which were made manually. Based on these matched profiles, the system can now recommend to new learners classes of activities that are indicated by similar profiles that have already received recommendations. The recommendation of activities is thus treated by our system as a multi-label classification task in which each student’s profile is associated with one or more classes of programming activities. The results obtained under different evaluation metrics confirm that the chosen algorithm, the ML-kNN, correctly mimics human decisions on the recommendations of classes of activities most of the time. Furthermore, these metrics provide relevant information for instructors to perform better actions with regard to formative assessments.