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.

The increasing availability of AI-based tools such as ChatGPT has transformed how students conduct online searches. This study investigates how students search for information with and without generative AI assistance, how distinct behavioural patterns emerge, and how these patterns relate to performance outcomes. We analyse 726 search stories, i.e., their navigation actions, from 305 students completing up to three search tasks. We first examine performance differences across search modalities, comparing AI-assisted and traditional web search approaches. We then analyse students’ search behaviours and examining their association with performance outcomes. Preliminary results show that AI-based searching tends to cluster around specific behavioural combinations and reduces extreme errors, but does not systematically improve correctness compared to traditional web searches. Furthermore, while performance outcomes vary across different search behaviours, no consistent patterns emerge linking specific behaviours to stable performance across tasks.

Procrastination and other forms of irrational delay are widespread among university students, leading to an array of potential negative consequences. While the reasons for this type of behavior are manifold and many facilitating factors have been identified, which of these factors are able to predict dilatory behavior in online/distance education has received comparatively little attention in the literature so far. In this study, we intended to compare the performance of two sets of objective predictors of delay, namely contextual variables based on characteristics of the assignment, and behavioral variables based on log data. Using historical data drawn from our university's learning management system, we calculated Bayesian multilevel models. The strongest and most consistent predictors of dilatory behavior turned out to be interval between the first click on the assignment and its deadline, the interval between the start of a block and the first click on the assignment, the number of clicks on the assignment, and the deadline type. The combination of both sets of predictors slightly improved the model's performance.

Predictive learning analytics (PLA) dashboards have been used by teachers to identify students at risk of failing their studies and provide proactive support. Yet, very few of them have been deployed at a large scale or had their use studied at a mature level of implementation. In this study, we surveyed 366 distance learning university teachers across four faculties three years after PLA has been made available across university as business as usual. Informed by the Unified Theory of Acceptance and Use of Technology (UTAUT), we present a context-specific version of UTAUT that reflects teachers’ perceptions of PLA in distance learning higher education. The adoption and use of PLA was shown to be positively influenced by less experience in teaching, performance expectancy, self-efficacy, positive attitudes, and low anxiety, while negatively influenced by a lack of facilitating conditions and low effort expectancy, indicating that the type of technology and context within which it is used are significant factors determining our understanding of technology usage and adoption. This study provides significant insights as to how to design, apply and implement PLA with teachers in higher education.

In this paper, we describe a large-scale study about the use of predictive learning analytics data with 240 teachers in 10 modules at a distance learning higher education institution. The aim of the study was to illuminate teachers' uses and practices of predictive data, in particular identify how predictive data was used to support students at risk of not completing or failing a module. Data were collected from statistical analysis of 17,033 students' performance by the end of the intervention, teacher usage statistics, and five individual semi-structured interviews with teachers. Findings revealed that teachers endorse the use of predictive data to support their practice yet in diverse ways and raised the need for devising appropriate intervention strategies to support students at risk.

This paper focuses on the problem of identifying students, who are at risk of failing their course. The presented method proposes a solution in the absence of data from previous courses, which are usually used for training machine learning models. This situation typically occurs in new courses. We present the concept of a "self-learner" that builds the machine learning models from the data generated during the current course. The approach utilises information about already submitted assessments, which introduces the problem of imbalanced data for training and testing the classification models.

This paper introduces two measures for the recommendation of study materials based on students' past study activity. We use records from the Virtual Learning Environment (VLE) and analyse the activity of previous students. We assume that the activity of past students represents patterns, which can be used as a basis for recommendations to current students.

This workshop explores how data literacy impacts on learning analytics both for practitioners and for end users. The term data literacy is used to broadly describe the set of abilities around the use of data as part of everyday thinking and reasoning for solving real-world problems. It is a skill required both by learning analytics practitioners to derive actionable insights from data and by the intended end users, such that it affects their ability to accurately interpret and critique presented analysis of data. The latter is particularly important, since learning analytics outcomes can be targeted at a wide range of end users, some of whom will be young students and many of whom are not data specialists.