Self-regulated learning (SRL) skills are critical for effective learning and academic success. With the growing availability of trace data from students' online learning activities, researchers are increasingly leveraging this data to infer SRL processes. However, challenges remain regarding the validity of these inferences and their generalisability across diverse learning contexts. This study presents a structured approach to investigate these challenges by examining SRL behaviours in a multidisciplinary university cohort. The dataset includes 76 baseline survey responses, over 300 daily SRL survey submissions, and more than 6,000 sequences of recorded learning actions as trace data. Using mixed linear models and sequence mining, the analysis is grounded in SRL theory and evaluated through machine learning performance metrics. Our findings indicate consistent within-person patterns of SRL and online learning behaviours, supporting the concept of transferable, holistic skill development. Additionally, the results validate the trace-based detection of SRL engagement but highlight limitations in accurately detecting planning and reflection phases. These findings underscore the potential of automating SRL engagement detection while emphasising the need for multi-modal approaches to capture the full spectrum of SRL processes comprehensively.

This case study explores the online learning patterns of a cohort of first-year university students in two subjects: a compulsory science subject and an introductory programming subject, by analysing trace data from the Learning Management Systems (LMS). The methodology extends existing learning analytics techniques to incorporate temporal aspects of students’ learning, such as session duration and weekly online behaviours. By examining over 82,000 learning actions, the research unveils significant variations in students’ online learning strategies between subjects, offering deeper insights into these differences and their associated challenges. The study seeks to initiate broader discussions in learning analytics, emphasising the need to comprehend students’ diverse online learning experiences and encouraging further exploration in future research.

ccheck, a lenient automatic grader and C style-checker, to guide students to improve their coding practices. Many computing classes rely heavily on autograders--software that automates grading and alleviates staff workload in classes with large enrollments. At best, autograders offer timely and consistent feedback to students. However, existing autograders primarily judge on functional correctness---they are generally strict and inflexible in marking beginner programming assignments. They tend not to provide feedback on programming style and structure, which instead requires delayed, tedious manual assessment. ccheck, the tool we introduce, aims to address this gap and provide more meaningful, real-time feedback with a pedagogical focus.

In HCI, the honeypot effect describes how people interacting with a system passively stimulate passers-by to observe, approach and engage in an interaction. Previous research has revealed the successive engagement phases and zones of the honeypot effect. However, there is little insight into: 1) how people are stimulated to transition between phases; 2) what aspects drive the honeypot effect apart from watching others; and 3) what constraints affect its self-reinforcing performance. In this paper, we discuss the honeypot effect as a spatiotemporal model of trajectories and influences. We introduce the Honeypot Model based on the analysis of observations and interaction logs from Encounters, a public installation that interactively translated bodily movements into a dynamic visual and sonic output. In providing a model that describes trajectories and influences of audience engagement in public interactive systems, our paper seeks to inform researchers and designers to consider contextual, spatial and social factors that influence audience engagement.