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Tools for automatic grading programming assignments, also known as Online Judges, have been widely used to support computer science (CS) courses. Nevertheless, few studies have used these tools to acquire and analyse interaction data to better understand the students’ performance and behaviours, often due to data availability or inadequate granularity. To address this problem, we propose an Online Judge called CodeBench, which allows for fine‐grained data collection of student interactions, at the level of, eg, keystrokes, number of submissions, and grades. We deployed CodeBench for 3 years (2016–18) and collected data from 2058 students from 16 introductory computer science (CS1) courses, on which we have carried out fine‐grained learning analytics, towards early detection of effective/ineffective behaviours regarding learning CS concepts. Results extract clear behavioural classes of CS1 students, significantly differentiated both semantically and statistically, enabling us to better explain how student behaviours during programming have influenced learning outcomes. Finally, we also identify behaviours that can guide novice students to improve their learning performance, which can be used for interventions. We believe this work is a step forward towards enhancing Online Judges and helping teachers and students improve their CS1 teaching/learning practices.

Gamification has been widely employed in the educational domain over the past eight years when the term became a trend. However, the literature states that gamification still lacks formal definitions to support the design and analysis of gamified strategies. This paper analysed the game elements employed in gamified learning environments through a previously proposed and evaluated taxonomy while detailing and expanding this taxonomy. In the current paper, we describe our taxonomy in-depth as well as expand it. Our new structured results demonstrate an extension of the proposed taxonomy which results from this process, is divided into five dimensions, related to the learner and the learning environment. Our main contribution is the detailed taxonomy that can be used to design and evaluate gamification design in learning environments.

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The impact of gamification has been typically evaluated via self-report assessments (questionnaires, surveys, etc.). In this work, we evaluate the use of gamification elements as parameters to predict whether students are going to fail or not in a programming course. Additionally, unlike prior research, we verify how usage of gamification features can predict student performance not only as a discrete, but as a continuous measure as well, via classification and regression, respectively. Moreover, we apply our approach on two programming courses from two different universities and involve three gamification features, i.e., ranking, score, and attempts. Our results for both predictions are notable: by using data from only the first quarter of the course, we obtain 89% accuracy for the binary classification task, and explain 78% of the students' final grade variance, with a mean absolute error of 1.05, for regression. Additionally and interestingly, initial observations point also to gamification elements used in the online judge encouraging competition and collaboration. For the former, students that solved more problems, with fewer attempts, achieved higher scores and ranking. For the latter, students formed groups to generate ideas, then implemented their own solution.

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