A Recommender System Based on Effort: Towards Minimising Negative Affects and Maximising Achievement in CS1 Learning

Abstract

Programming online judges (POJs) are autograders that have been increasingly used in introductory programming courses (also known as CS1) since these systems provide instantaneous and accurate feedback for learners’ codes solutions and reduce instructors’ workload in evaluating the assignments. Nonetheless, learners typically struggle to find problems in POJs that are adequate for their programming skills. A potential reason is that POJs present problems with varied categories and difficulty levels, which may cause a cognitive overload, due to the large amount of information (and choice) presented to the student. Thus, students can often feel less capable, which may result in undesirable affective states, such as frustration and demotivation, decreasing their performance and potentially leading to increasing dropout rates. Recently, new research emerged on systems to recommend problems in POJs; however, the data collection for these approaches was not fine-grained; importantly, they did not take into consideration the students’ previous effort and achievement. Thus, this study proposes for the first time a prescriptive analytics solution for students’ programming behaviour by constructing and evaluating an automatic recommender module based on students’ effort, to personalise the problems presented to the learner in POJs. The aim is to improve the learners achievement, whilst minimising negative affective states in CS1 courses. Results in a within-subject double-blind controlled experiment showed that our method significantly improved positive affective states, whilst minimising the negatives ones. Moreover, our recommender significantly increased students’ achievement (correct solutions) and reduced dropout and failure in problem-solving.