Collaboration skills are fundamental to effective collaborative learning, career success, and responsible citizenship. Collaborative learning analytics (CLA) systems hold significant potential in helping students develop these skills by automatically collecting group interaction data, analyzing skill levels, and providing actionable feedback so…

The theory of socially shared regulation of learning (SSRL) suggests that successful collaborative groups can identify and respond to trigger events stemming from cognitive or emotional obstacles in learning. Thus, to develop real-time support for SSRL, novel metrics are needed to identify different types of trigger events that invite SSRL. Our…

The advent of advanced technology has opened new horizons for studying collaborative learning, although ambiguity remains in the classification and rationale for combining modalities in multimodal collaboration analytics (MMCA). Addressing this gap is crucial for the progression of collaborative learning practices and research. This review…

Even before the inception of the term "learning analytics," researchers globally had been investigating the use of various feedback systems to support the self-regulation of participation and promote equitable contributions during collaborative learning activities. While some studies indicate positive effects for distinct subgroups of…

Socially shared regulation in learning (SSRL) contributes to successful collaborative learning (CL). Empirical research into SSRL has received considerable attention recently, with increasingly available multimodal data, advanced learning analytics (LA), and artificial intelligence (AI) providing promising research avenues. Yet, integrating these…

Incorporating non-verbal data streams is essential to understanding the dynamics of interaction within collaborative learning environments in which a variety of verbal and non-verbal modes of communication intersect. However, the complexity of non-verbal data -- especially gathered in the wild from collaborative learning contexts -- demands…

Multimodal learning analytics (MMLA) research has shown the feasibility of building automated models of collaboration quality using artificial intelligence (AI) techniques (e.g., supervised machine learning (ML)), thus enabling the development of monitoring and guiding tools for computer-supported collaborative learning (CSCL). However, the…

Computational thinking (CT) is a critical 21st-century skill that equips undergraduate students to solve problems systematically and think algorithmically. A key component of CT is computational creativity, which enables students to generate novel solutions within programming constraints. Humanoid robots are increasingly explored as promising…

This study investigates the integration of artificial intelligence into the Think-Pair-Share (TPS) methodology through a learning analytics lens. Using a mixed-methods quasi-experimental design (N=140), we examined how an AI-enhanced collaborative platform influences creative thinking among computer science undergraduates. The experimental group…

Understanding students' emerging roles in computer-supported collaborative learning (CSCL) is critical for promoting regulated learning processes and supporting learning at both individual and group levels. However, it has been challenging to disentangle individual performance from group-based deliverables. This study introduces new learning…

Multimodal learning analytics (MMLA) integrates novel sensing technologies and artificial intelligence algorithms, providing opportunities to enhance student reflection during complex, collaborative learning experiences. Although recent advancements in MMLA have shown its capability to generate insights into diverse learning behaviours across…

As technology advances, learning analytics is expanding to include students' collaboration settings. Despite their increasing application in practice, some types of analytics might not fully capture the comprehensive educational contexts in which students' collaboration takes place (e.g., when data is collected and processed without predefined…

Socio-spatial learning analytics (SSLA) is an emerging area within learning analytics research that seeks to uncover valuable educational insights from individuals' social and spatial data traces. These traces are captured automatically through sensing technologies in physical learning spaces, and the research is commonly based on the theoretical…

Learning activities are at the core of every educational design effort. Designing learning activities is a process that benefits from reflecting on previous runs of those activities. One way to measure the behaviour and effects of design choices is to use learning analytics (LA). The challenge, however, lies in the unavailability of an…

Using data to generate a deeper understanding of collaborative learning is not new, but automatically analyzing log data has enabled new means of identifying key indicators of effective collaboration and teamwork that can be used to predict outcomes and personalize feedback. Collaboration analytics is emerging as a new term to refer to…