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Background Objective Methods Results and Conclusions Dashboards play a prominent role in learning analytics (LA) research. In collaboration activities, dashboards can show traces of team participation. They are often evaluated based on students' perceived satisfaction and engagement with the dashboard. However, there is a notable methodological gap in understanding how these dashboards support the nuanced process of student reflection.This paper presents empirical evidence on how students from high and low‐performing groups reflect individually on their performance while using a Learning Analytics Dashboard (LAD).We address this in the context of education in healthcare, wherein we captured actions and positioning data from a simulation‐based collaborative activity and generated a collaborative LAD. A total of 41 nursing students were invited to participate in a post‐hoc semi‐structured individual interview to use a collaborative LAD while answering a set of prompts to reflect on their individual and group performance. Students' reflections were coded and analysed using Bain's 5R reflection framework. We used epistemic network analysis to capture the dynamic reflection process and to understand the connections between the reflection stages (from low to high). We compared how different these connections were for students in high and low‐performing groups.Our results revealed that most students were only able to achieve low and middle stages of reflection. Yet, students in low‐performing groups predominantly followed low‐to‐middle stages of reflection. In contrast, students from high‐performing groups demonstrated the ability to transition between low‐to‐middle and low‐to‐high stages of reflection. Based on these findings, we discuss implications for both research and practice, particularly emphasising the necessity to scaffold reflection when using LADs. [ABSTRACT FROM AUTHOR]

Multimodal learning analytics (MMLA) offers the potential to provide evidence‐based insights into complex learning phenomena such as collaborative learning. Yet, few MMLA applications have closed the learning analytics loop by being evaluated in real‐world educational settings. This study evaluates the effectiveness of an MMLA solution in enhancing feedback and reflection within a complex and highly dynamic collaborative learning environment. A two‐year longitudinal study was conducted with 399 students and 17 teachers, utilising an MMLA system in reflective debriefings in the context of healthcare education. We analysed the survey data of 74 students and 11 teachers regarding their perceptions of the MMLA system. We applied the Evaluation Framework for Learning Analytics, augmented by complexity, accuracy and trust measures, to assess both teachers' and students' perspectives. The findings illustrated that teachers and students both had generally positive perceptions of the MMLA solution. Teachers found the MMLA solution helpful in facilitating feedback provision and reflection during debriefing sessions. Similarly, students found the MMLA solution effective in providing clarity on the data collected, stimulating reflection on their learning behaviours, and prompting considerations for adaptation in their learning behaviours. However, the complexity of the MMLA solution and the need for qualitative measures of communication emerged as areas for improvement. Additionally, the study highlighted the importance of data accuracy, transparency, and privacy protection to maintain user trust. The findings provide valuable contributions to advancing our understanding of the use of MMLA in supporting feedback and reflection practices in intricate collaborative learning while identifying avenues for further research and improvement. We also provided several insights and practical recommendations for successful MMLA implementation in authentic learning contexts. Practitioner notesWhat is currently known about this topic Multimodal learning analytics (MMLA) seeks to generate data‐informed insights about learners' metacognitive and emotional states as well as their learning behaviours, by utilising intricate physical and physiological signals.MMLA has not only pioneered novel data analytic methods but also aspired to complete the learning analytics loop by crafting innovative, tangible solutions that relay these insights to the concerned stakeholders.A prominent direction within MMLA research has been the formulation of tools to support feedback and reflection in collaborative learning scenarios, given MMLA's capacity to discern intricate and dynamic learning behaviours.What this paper adds Teachers' and students' positive perceptions of an MMLA implementation in stimulating considerations of adaptations in their pedagogical practices and learning behaviours, respectively.Empirical evidence supporting the potential of MMLA in assisting teachers to facilitate students' reflective practices during intricate collaborative learning scenarios.The importance of addressing issues related to design complexity, interpretability for users with disabilities, aggregated data representation, and concerns related to trust for building a practical MMLA solution in real learning settings.Implications for practice and/or policy The MMLA solution can provide teachers with a comprehensive view of student performance, illuminate areas for improvement, and confirm learning scenario outcomes.The MMLA solution can stimulate students' reflections on their learning behaviours and promote considerations of adaptation in their learning behaviours.Providing clear explanations and guidance on how to interpret analytics, as well as addressing concerns related to data completeness and representation, are essential to maximising utility. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) can enhance teachers' capabilities by sharing control over different parts of learning activities. This is especially true for complex learning activities, such as dynamic learning transitions where students move between individual and collaborative learning in un‐planned ways, as the need arises. Yet, few initiatives have emerged considering how shared responsibility between teachers and AI can support learning and how teachers' voices might be included to inform design decisions. The goal of our article is twofold. First, we describe a secondary analysis of our co‐design process comprising six design methods to understand how teachers conceptualise sharing control with an AI co‐orchestration tool, called Pair‐Up. We worked with 76 middle school math teachers, each taking part in one to three methods, to create a co‐orchestration tool that supports dynamic combinations of individual and collaborative learning using two AI‐based tutoring systems. We leveraged qualitative content analysis to examine teachers' views about sharing control with Pair‐Up, and we describe high‐level insights about the human‐AI interaction, including control, trust, responsibility, efficiency, and accuracy. Secondly, we use our results as an example showcasing how human‐centred learning analytics can be applied to the design of human‐AI technologies and share reflections for human‐AI technology designers regarding the methods that might be fruitful to elicit teacher feedback and ideas. Our findings illustrate the design of a novel co‐orchestration tool to facilitate the transitions between individual and collaborative learning and highlight considerations and reflections for designers of similar systems. Practitioner notesWhat is already known about this topic: Artificial Intelligence (AI) can help teachers facilitate complex classroom activities, such as having students move between individual and collaborative learning in unplanned ways.Designers should use human‐centred design approaches to give teachers a voice in deciding what AI might do in the classroom and if or how they want to share control with it.What this paper adds: Presents teacher views about how they want to share control with AI to support students moving between individual and collaborative learning.Describes how we adapted six design methods to design AI features.Illustrates a complete, iterative process to create human‐AI interactions to support teachers as they facilitate students moving from individual to collaborative learning.Implications for practice: We share five implications for designers that teachers highlighted as necessary when designing AI‐features, including control, trust, responsibility, efficiency and accuracy.Our work also includes a reflection on our design process and implications for future design processes. [ABSTRACT FROM AUTHOR]