Simulation-based learning is central to healthcare education, yet its effectiveness depends on high-quality debriefing. Traditional debriefs often overlook detailed team dialogue dynamics. Advances in large language models (LLMs) open new possibilities for learning analytics (LA) by automatically coding and visualising teamwork behaviours from dialogue data. This study investigates the effectiveness of different prompting strategies for LLM-based coding, comparing their performance and environmental impacts (CO2e) to identify approaches suitable for transfer into professional practice. Building on these results, we evaluate the generalisability of the optimised model from university student simulations to in-situ, hospital settings, and explore how healthcare professionals perceive the interpretability, usefulness, and trustworthiness of LLM-driven learning analytics in professional learning debriefs. Findings illustrate that responsible uses of AI can help extend LA beyond a controlled university environment into an authentic, in-hospital healthcare context, offering potentially scalable and sustainable support for reflective practice and professional development.

Team teaching in higher education can be challenging, especially for educators managing large classes with limited pedagogical training and few opportunities to reflect on their practices. Emerging sensing technologies and analytics can capture and analyse patterns of collaboration, communication, and movement of team teaching. Yet, few studies have presented these data to educators for reflection. To address this gap, we examine the benefits, challenges, and concerns of presenting multimodal teaching data (positional, audio, and spatial pedagogy observations) to educators via the TeamTeachingViz dashboard. We evaluated TeamTeachingViz in an authentic classroom context where educators explored their own data and team teaching strategies. Multimodal data was collected from 36 in-the-wild classroom sessions involving 12 educators grouped in various combinations over 4 weeks, followed by semi-structured interviews to reflect on their practices. Findings suggest that educators improved their self-awareness by using data-driven insights to understand their movements and interactions, enabling continuous improvement in team teaching. However, they noted the need for additional data, such as student behaviours and speech content, to better contextualise these insights.

Learning analytics dashboards (LADs) simplify complex learner data into accessible visualisations, providing actionable insights for educators and students. However, their educational effectiveness has not always matched the sophistication of the technology behind them. Explanatory and interactive LADs, enhanced by generative AI (GenAI) chatbots, hold promise by enabling dynamic, dialogue-based interactions with data visualisations and offering personalised feedback through text. Yet, the effectiveness of these tools may be limited by learners' varying levels of GenAI literacy, a factor that remains underexplored in current research. This study investigates the role of GenAI literacy in learner interactions with conventional (reactive) versus scaffolding (proactive) chatbot-assisted LADs. Through a comparative analysis of 81 participants, we examine how GenAI literacy is associated with learners' ability to interpret complex visualisations and their cognitive processes during interactions with chatbot-assisted LADs. Results show that while both chatbots significantly improved learner comprehension, those with higher GenAI literacy benefited the most, particularly with conventional chatbots, demonstrating diverse prompting strategies. Findings highlight the importance of considering learners' GenAI literacy when integrating GenAI chatbots in LADs and educational technologies. Incorporating scaffolding techniques within GenAI chatbots can be an effective strategy, offering a more guided experience that reduces reliance on learners' GenAI literacy.

There is a growing interest in creating Learning Analytics (LA) systems that incorporate student perspectives. Yet, many LA systems still lean towards a technology-centric approach, potentially overlooking human values and the necessity of human oversight in automation. Although some recent LA studies have adopted a human-centred design stance, there is still limited research on establishing safe, reliable, and trustworthy systems during the early stages of LA design. Drawing from a newly proposed framework for human-centred artificial intelligence, we introduce SLADE, a method for ideating and identifying features of human-centred LA systems that balance human control and computer automation. We illustrate SLADE’s application in designing LA systems to support collaborative learning in healthcare. Twenty-one third-year students participated in design sessions through SLADE’s four steps: i) identifying challenges and corresponding LA systems; ii) prioritising these LA systems; iii) ideating human control and automation features; and iv) refining features emphasising safety, reliability, and trustworthiness. Our results demonstrate SLADE’s potential to assist researchers and designers in: 1) aligning authentic student challenges with LA systems through both divergent ideation and convergent prioritisation; 2) understanding students’ perspectives on personal agency and delegation to teachers; and 3) fostering discussions about the safety, reliability, and trustworthiness of LA solutions.

Advancements in Multimodal Learning Analytics (MMLA) have the potential to enhance the development of effective teamwork skills and foster reflection on collaboration dynamics in physical learning environments. Yet, only a few MMLA studies have closed the learning analytics loop by making MMLA solutions immediately accessible to educators to support reflective practices, especially in authentic settings. Moreover, deploying MMLA solutions in authentic settings can bring new challenges beyond logistic and privacy issues. This paper reports the design and use of TeamSlides, a multimodal teamwork analytics dashboard to support teacher-guided reflection. We conducted an in-the-wild classroom study involving 11 teachers and 138 students. Multimodal data were collected from students working in team healthcare simulations. We examined how teachers used the dashboard in 22 debrief sessions to aid their reflective practices. We also interviewed teachers to discuss their perceptions of the dashboard’s value and the challenges faced during its use. Our results suggest that the dashboard effectively reinforced discussions and augmented teacher-guided reflection practices. However, teachers encountered interpretation conflicts, sometimes leading to mistrust or misrepresenting the information. We discuss the considerations needed to overcome these challenges in MMLA research.

Effective collaboration and team communication are critical across many sectors. However, the complex dynamics of collaboration in physical learning spaces, with overlapping dialogue segments and varying participant interactions, pose assessment challenges for educators and self-reflection difficulties for students. Epistemic network analysis (ENA) is a relatively novel technique that has been used in learning analytics (LA) to unpack salient aspects of group communication. Yet, most LA works based on ENA have primarily sought to advance research knowledge rather than directly aid teachers and students by closing the LA loop. We address this gap by conducting a study in which we i) engaged teachers in designing human-centred versions of epistemic networks; ii) formulated an NLP methodology to code physically distributed dialogue segments of students based on multimodal (audio and positioning) data, enabling automatic generation of epistemic networks; and iii) deployed the automatically generated epistemic networks in 28 authentic learning sessions and investigated how they can support teaching. The results indicate the viability of completing the analytics loop through the design of streamlined epistemic network representations that enable teachers to support students’ reflections.

Multimodal Learning Analytics (MMLA) innovations make use of rapidly evolving sensing and artificial intelligence algorithms to collect rich data about learning activities that unfold in physical spaces. The analysis of these data is opening exciting new avenues for both studying and supporting learning. Yet, practical and logistical challenges commonly appear while deploying MMLA innovations “in-the-wild”. These can span from technical issues related to enhancing the learning space with sensing capabilities, to the increased complexity of teachers’ tasks. These practicalities have been rarely investigated. This article addresses this gap by presenting a set of lessons learnt from a 2-year human-centred MMLA in-the-wild study conducted with 399 students and 17 educators in the context of nursing education. The lessons learnt were synthesised into topics related to (i) technological/physical aspects of the deployment; (ii) multimodal data and interfaces; (iii) the design process; (iv) participation, ethics and privacy; and (v) sustainability of the deployment.

In recent years, there has been a growing interest in creating multimodal learning analytics (LA) systems that automatically analyse students’ states that are hard to see with the "naked eye", such as cognitive load and stress levels, but that can considerably shape their learning experience. A rich body of research has focused on detecting such aspects by capturing bodily signals from students using wearables and computer vision. Yet, little work has aimed at designing end-user interfaces that visualise physiological data to support tasks deliberately designed for students to learn from stressful situations. This paper addresses this gap by designing a stress analytics dashboard that encodes students’ physiological data into stress levels during different phases of an authentic team simulation in the context of nursing education. We conducted a qualitative study with teachers to understand (i) how they made sense of the stress analytics dashboard; (ii) the extent to which they trusted the dashboard in relation to students’ cortisol data; and (iii) the potential adoption of this tool to communicate insights and aid teaching practices.

Embodied team learning is a form of group learning that occurs in co-located settings where students need to interact with others while actively using resources in the physical learning space to achieve a common goal. In such situations, communication dynamics can be complex as team discourse segments can happen in parallel at different locations of the physical space with varied team member configurations. This can make it hard for teachers to assess the effectiveness of teamwork and for students to reflect on their own experiences. To address this problem, we propose METS (Multimodal Embodied Teamwork Signature), a method to model team dialogue content in combination with spatial and temporal data to generate a signature of embodied teamwork. We present a study in the context of a highly dynamic healthcare team simulation space where students can freely move. We illustrate how signatures of embodied teamwork can help to identify key differences between high and low performing teams: i) across the whole learning session; ii) at different phases of learning sessions; and iii) at particular spaces of interest in the learning space.

In co-located situations, team members use a combination of verbal and visual signals to communicate effectively, among which positional forms play a key role. The spatial patterns adopted by team members in terms of where in the physical space they are standing, and who their body is oriented to, can be key in analysing and increasing the quality of interaction during such face-to-face situations. In this paper, we model the students’ communication based on spatial (positioning) and audio (voice detection) data captured from 92 students working in teams of four in the context of healthcare simulation. We extract non-verbal events (i.e., total speaking time, overlapped speech,and speech responses to team members and teachers) and investigate to what extent they can serve as meaningful indicators of students’ performance according to teachers’ learning intentions. The contribution of this paper to multimodal learning analytics includes: i) a generic method to semi-automatically model communication in a setting where students can freely move in the learning space; and ii) results from a mixed-methods analysis of non-verbal indicators of team communication with respect to teachers’ learning design.