Predictive student models are increasingly used in learning environments due to their ability to enhance educational outcomes and support stakeholders in making informed decisions. However, predictive models can be biased and produce unfair outcomes, leading to potential discrimination against some students and possible harmful long-term implications. This has prompted research on fairness metrics meant to capture and quantify such biases. Nonetheless, so far, existing fairness metrics used in education are predictive performance-oriented, focusing on assessing biased outcomes across groups of students, without considering the behaviors of the models nor the severity of the biases in the outcomes. Therefore, we propose a novel metric, the Model Absolute Density Distance (MADD), to analyze models' discriminatory behaviors independently from their predictive performance. We also provide a complementary visualization-based analysis to enable fine-grained human assessment of how the models discriminate between groups of students. We evaluate our approach on the common task of predicting student success in online courses, using several common predictive classification models on an open educational dataset. We also compare our metric to the only predictive performance-oriented fairness metric developed in education, ABROCA. Results on this dataset show that: (1) fair predictive performance does not guarantee fair models' behaviors and thus fair outcomes, (2) there is no direct relationship between data bias and predictive performance bias nor discriminatory behaviors bias, and (3) trained on the same data, models exhibit different discriminatory behaviors, according to different sensitive features too. We thus recommend using the MADD on models that show satisfying predictive performance, to gain a finer-grained understanding on how they behave and to refine models selection and their usage.

This paper presents a design-based research which focuses on the design and the evaluation of the Pyrates online application. This serious game aims to introduce Python programming language supporting the transition from block-based languages. The layout of Pyrates' learning environment is inspired from beneficial features of block-based programming editors. In order to evaluate this design, the application has been tested in eight classrooms with French 10-th grade students (n = 240). Self-generated activity traces have been collected (n = 69, 701) and supplemented by a qualitative online survey. The data analysis shows that some of the design choices conduct to the expected effects. The creation of a "programming memo" (synthesized documentation) allows the discovery of algorithmic notions while offering a reference support for the Python syntax. The ease of copy-paste from this memo limits keyboarding. The integration of a syntax analyzer designed for beginners gives students a high level of autonomy in handling errors. However, other choices have rather deleterious impacts. For instance, the creation of a control panel for program executions proves to be dedicated to a trialand-error programming approach or to "notional bypassing" strategies.

Learning Analytics Dashboards (LADs) are becoming a key element in enabling learners to monitor their learning, plan and actually learn. However, LADs are sometimes not completely adapted to students, who are rarely involved in their design. Moreover, even when they are, the implemented LADs are often the same for all students, whereas previous works have shown the value of adapted LADs. Here we investigate which adaptations are requested by students, and attempt to identify which data and visualizations are suitable depending on the student's profile. More specifically, we consider dynamic profiles as students' expectations can vary over the course duration. By using LADs co-design sessions both online and on-site, we collected needs from N=386 university students from different disciplines and degree level, split in 108 groups (2 to 4 students). After a manual annotation, we identified a total of 54 types of data and indicators, divided into 12 thematics. Our first analysis confirmed some previous results, particularly on the use of peer comparisons that do not fulfill every student's needs. And we noticed other expectations according to the student's learning context or the academic period. Future work will benefit from these results to define a model of adapted LADs.

Depuis le début de la pandémie de COVID-19, de nombreux pays ont adopté l’enseignement en ligne comme alternative aux cours en présentiel. Cette situation a accentué la prise de conscience de l’importance d’analyser les données d’apprentissage laissées par les élèves, pour améliorer et évaluer le processus d’apprentissage. Cet article expose les résultats d’une étude conduite sur une classe de 26 élèves de deuxième année du cycle ingénieur d’un établissement supérieur au Maroc, suivant tous un cours en modalité mixte sur la plateforme Moodle. Cette étude porte sur un outil destiné aux enseignants ainsi qu’aux élèves afin de faciliter le suivi et le contrôle du processus d'apprentissage, les résultats montrent que l’outil a permis d’améliorer l’engagement et le taux de réussite des élèves.

La réalité virtuelle est de plus en plus utilisée dans le domaine de l’éducation et de la formation. Les applications de réalité virtuelle fournissent aux apprenants l’occasion de vivre des expériences qu’ils ne peuvent pas trouver (totalement ou partiellement) dans le monde réel et qui favorisent les apprentissages. Dans le secteur de la formation a` distance, il ressort des travaux existants que la réalité virtuelle offre de nouveaux supports d’apprentissage et une nouvelle façon de collaborer et de se socialiser. Elle permet de simuler des situations d’apprentissage semblables à celles réalisées dans une formation pr ́esentielle (cours magistral, manipulation des objets dans un laboratoire, etc.) en offrant aux utilisateurs, une grande liberté d’interactions dans des systèmes immersifs. L’objectif de notre article consiste à étudier un nouvel apport de la réalité virtuelle sur l’expérience utilisateur des apprenants dans un dispositif de formation à distance. Nous montrons notamment comment la représentation des avatars peut influencer le sentiment de coprésence et rapprocher les apprenants dans un environnement virtuel collaboratif.