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Team cohesion is critical in driving successful outcomes for teams in collaborative learning settings. It shapes team behaviour, fostering shared perceptions, group synchrony and a common goal-oriented approach. This affinity becomes evident in dynamic interactions, offering insights into team behaviour through interaction data analysis. Interpreting interaction data proves complex, hampering our understanding and insights into shared team perceptions and task cohesion development. This paper used temporal motif analysis to examine the changes in team members' cohesive perceptions and behaviours, including task cohesion, performance outcomes, engagement and group synchrony. Trace data from an online work-integrated learning environment captured learning behaviours, while responses to a questionnaire at different stages of a study program captured task cohesion and cohesive perceptions. The findings reveal teams with strong task cohesion and high performance tend to share similar cohesive perceptions driven by interdependent interactions. Conversely, teams with different cohesion perceptions have lower interaction interdependence and poorer performance. Through analysing team interaction data, this study uncovered key insights to promote positive adjustments aligning team perceptions, enhancing collaborative learning and offering support for improved performance, engagement and synchrony among teams, ultimately benefiting learning outcomes and the cultivation of skills and competencies.

Expert knowledge elicitation (EKE) aims at obtaining individual representations of experts’ beliefs and render them in the form of probability distributions or functions. In many cases the elicited distributions differ and the challenge in Bayesian inference is then to find ways to reconcile discrepant elicited prior distributions. This paper proposes the parallel analysis of clusters of prior distributions through a hierarchical method for clustering distributions and that can be readily extended to functional data. The proposed method consists of (i) transforming the infinite-dimensional problem into a finite-dimensional one, (ii) using the Hellinger distance to compute the distances between curves and thus (iii) obtaining a hierarchical clustering structure. In a simulation study the proposed method was compared to k-means and agglomerative nesting algorithms and the results showed that the proposed method outperformed those algorithms. Finally, the proposed method is illustrated through an EKE experiment and other functional data sets.

The growth and uptake of educational technology has significantly reshaped the delivery of distance and online learning. With an unprecedented number of learners engaging with online modes of education, there is a growing need to understand the underlying student enrolment motivations, goals and learning behaviours evolving from a highly diverse student population. Research in learning analytics has advanced the use of digital data to understand student learning processes. However, there remains a limited understanding of how non-traditional learner characteristics, needs and motivational factors influence their learning behaviour and engagement strategies. Survey data from 232 students enrolled in fully online degree programs at a large public research university in Australia has been examined and used to represent 1687 students that have not completed the survey. To characterise the larger population of students, we combined their demographics, digital trace data, and course performance to provide richer insights of non-traditional learners in online learning. Data science approaches are applied, including an unsupervised machine learning technique that revealed the results of six unique learner profiles, clearly differentiated by their motivation, demographic, engagement and performance. While the findings show that each learner profile faces unique study challenges, there are also unique opportunities associated with each profile that could be utilised to improve their learning outcomes. The practical implications of the study on teaching practices are further discussed.