Equality in learning outcomes between students from different socio-economic status (SES) backgrounds is a long-standing issue. Although the problem is widespread, not all factors contributing to the gap are known. The compensatory advantage hypothesis posits that benefits of a higher-SES background are greater for students who face early academic difficulties. In other words, the effect of SES is amplified when encountering a challenge. This study examines how poor first assignment outcomes interact with SES to affect success in 22 online university courses. The results show that the relationship between coming from a higher-SES neighbourhood on course success is greater for students who do not submit the first assignment than for those who perform well on it, in line with the compensatory advantage hypothesis. More precisely, students from mid-ranked and high-SES areas were better able to compensate for missing their first assignment than their low-SES counterparts. Despite the robust results, the interaction effect varies considerably across courses, suggesting that contextual course factors might influence the strength, though the factors we analysed did not account for this variation. The implications of these findings for both research and potential strategies to reduce educational inequalities are discussed, emphasizing their significance for theoretical understanding and practical applications.

In this paper, we rely on the Cognitive Load Theory and explore how multimodal data can be used to measure students' effort at the task level. Different from what we expected, the subjective effort ratings have a higher correlation with the students' scores, while the behavioral and physiological data have higher correlations with the scores than with the effort ratings. Moreover, we found that, in the context of our study, ability had a stronger influence on students' success than the prior knowledge, while none of these variables had an influence on the effort ratings. Finally, we propose a new effort model based on students' activity.

The digitization of music, the emergence of online streaming platforms and mobile apps have dramatically changed the ways we consume music. Today, much of the music that we listen to is organized in some form of a playlist, and many users of modern music platforms create playlists for themselves or to share them with others. The manual creation of such playlists can however be demanding, in particular due to the huge amount of possible tracks that are available online. To help users in this task, music platforms like Spotify provide users with interactive tools for playlist creation. These tools usually recommend additional songs to include given a playlist title or some initial tracks. Interestingly, little is known so far about the effects of providing such a recommendation functionality. We therefore conducted a user study involving 270 subjects, where one half of the participants—the treatment group—were provided with automated recommendations when performing a playlist construction task. We then analyzed to what extent such recommendations are adopted by users and how they influence their choices. Our results, among other aspects, show that about two thirds of the treatment group made active use of the recommendations. Further analyses provide additional insights about the underlying reasons why users selected certain recommendations. Finally, our study also reveals that the mere presence of the recommendations impacts the choices of the participants, even in cases when none of the recommendations was actually chosen.

Playlist generation is a special form of music recommendation where the problem is to create a sequence of tracks to be played next, given a number of seed tracks. In academia, the evaluation of playlisting techniques is often done by assessing with the help of information retrieval measures if an algorithm is capable of selecting those tracks that also a human would pick next. Such approaches however cannot capture other factors, e.g., the homogeneity of the tracks that can determine the quality perception of playlists. In this work, we report the results of a multi-metric comparison of different academic approaches and a commercial playlisting service. Our results show that all tested techniques generate playlists with certain biases, e.g., towards very popular tracks, and often create playlists continuations that are quite different from those that are created by real users.

Recommender systems provide users with pertinent resources according to their context and their profiles, by applying statistical and knowledge discovery techniques. This paper describes a new approach of generating suitable recommendations based on the active user's navigation stream, by considering long and short-distance resources in the history with a tractable model.

Recommender systems filter resources for a given user by predicting the most pertinent resource given a specific context. This paper describes a new approach of generating suitable recommendations based on the active user's navigation stream. The underlying hypothesis is that the resources order in the stream results from the intrinsic logic of the user's behavior. The sequence based recommender we propose is inspired from language modeling and integrates skipping techniques. It has been tested on a browsing dataset extracted from Intranet logs provided by a French bank. Results show that the use of exponential decay weighting schemes when taking into account non contiguous sequences to compute recommendations enhances the accuracy. Moreover, we propose a skipping variant that provides a high accuracy while being less complex.