High staff turnover in higher education often burdens teachers with laborious handovers of teaching tasks every semester. To boost teachers' workflow efficiency, we present an innovative knowledge management tool that allows experienced teachers to seamlessly capture task steps (i.e., denoted as ShareFlow) that can be subsequently recommended to novices via proactive push, all happening during teachers' natural workflow to minimize disruptions. We conducted a controlled experiment with 30 participants and compared our tool against a state-of-the-art baseline knowledge management system powered by a large language model (Claude 3 Haiku). We found that our knowledge management tool reduced task completion time and improved task quality (with statistical significance). Feedback from the participants also indicated the high usability of our tool, suggesting its strong potential for practical adoption for improving teacher workflows.

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.

While the capacity to self-regulate has been found to be crucial for secondary school students, prior studies often rely on self-report surveys and think-aloud protocols that present notable limitations in capturing self-regulated learning (SRL) processes. This study advances the understanding of SRL in secondary education by using trace data to examine SRL processes during multi-source writing tasks, with higher education participants included for comparison. We collected fine-grained trace data from 66 secondary school students and 59 university students working on the same writing tasks within a shared SRL-oriented learning environment. The data were labelled using Bannert's validated SRL coding scheme to reflect specific SRL processes, and we examined the relationship between these processes, essay performance, and educational levels. Using epistemic network analysis (ENA) to model and visualise the interconnected SRL processes in Bannert's coding scheme, we found that: (a) secondary school students predominantly engaged in three SRL processes - Orientation, Re-reading, and Elaboration/Organisation; (b) high-performing secondary students engaged more in Re-reading, while low-performing students showed more Orientation process; and (c) higher education students exhibited more diverse SRL processes such as Monitoring and Evaluation than their secondary education counterparts, who heavily relied on following task instructions and rubrics to guide their writing. These findings highlight the necessity of designing scaffolding tools and developing teacher training programs to enhance awareness and development of SRL skills for secondary school learners.

Collaborative problem-solving (CPS) is defined as an inherently sociocognitive phenomena. Despite this, extant learning analytic techniques tend to focus on either the social or cognitive aspects without explicitly considering their interaction. Prior work developed Neural Epistemic Network Analysis (NENA), which used a combination of deep learning methods to simultaneously model the social and cognitive aspects of CPS; however, the method had several limitations. The refined version of NENA presented here addresses these limitations by (a) introducing a simplified autoencoder deep learning architecture; (b) using a combination of social and epistemic networks as input to preserve interpretability in terms of social and cognitive factors; and (c) introducing an isometry loss function to ensure downstream statistical tests are meaningful. We found that the refined version of NENA is able to achieve high performance on criteria we would expect from a network analytic technique in the context of learning analytics: interpretability, goodness of fit, orthogonality and isometry; and discriminatory power. We also demonstrated that this method was comparable in performance to a more traditional learning analytic technique, Epistemic Network Analysis (ENA), while providing information that ENA did not. The results suggest that NENA could be a useful method for exploring the cognitive interactions of a given individual's social network and thus the influences their network exerts upon them.

In computer-based learning environments (CBLEs), adopting effective self-regulated learning (SRL) strategies requires sophisticated coordination of multiple SRL processes. While various studies have proposed adaptive SRL scaffolds (i.e. real-time advice on adopting effective SRL processes) and embedded them in CBLEs to facilitate learners' effective use of SRL strategies, two key research gaps remain. First, there is a lack of research on SRL scaffolds that are based on continuous assessment of both learners' SRL processes and learning conditions (e.g., awareness of learning resources) to provide adaptive support. Second, current analytics-based scaffolding mechanisms lack the scalability needed to effectively address multiple learning conditions. Integration of analytics of SRL with generative artificial intelligence (GenAI) can provide scalable scaffolding for real-time SRL processes and evolving conditions. Yet, empirical studies implementing and evaluating effects of this integration remain scarce. To address these limitations, we conducted a randomized control trial, assigning participants to three groups (control, process only, and process with condition groups) to investigate the effects of using GenAI to turn insights from real-time analytics about students' SRL processes and conditions into adaptive scaffolds. The results demonstrate that integrating real-time analytics with GenAI in adaptive SRL scaffolds - addressing both SRL processes and dynamic conditions - promotes more metacognitive learning patterns compared to the control and process-only groups. In addition, the learners showed varying levels of compliance with analytics-based GenAI scaffolds, and this was also reflected in how the learners coordinated their SRL processes, particularly in the performance phase of SRL. This study contributes to the literature by designing, implementing, and evaluating the impact of adaptive scaffolds on learners' SRL processes using real-time analytics with GenAI.

Learning is a complex process that occurs over time. To represent this complex process, interests has been rising in conceptualizing and integrating temporality into model constructions. However, the construction of an event-based temporal model is challenging. Specifically, researchers struggle with translating qualitative heuristics and theoretical hypotheses into quantifiable temporal parameters. Existing methods of parameter derivation also suffer from issues of model transparency and oversimplification of learning contexts. Thus, we proposed a conceptual and methodological framework, Qualitative Parameter Triangulation (QPT), to center human interpretation in model construction. Based on human interpretations, QPT constructs a qualitative loss function and derives temporal parameters using an automatical optimization algorithm. The final step is to check consistency between a global representation with local qualitative evidence given specific learning moments. By presenting a worked example of QPT, we demonstrated the process of maintaining pairwise alignments across interpretation, systematization, and approxi-gation. As a proof of concept, QPT is a feasible framework for determining temporal parameters and constructing event-based temporal models.

Knowledge management involves finding, expanding, and using knowledge in an organisation to achieve goals. Its role is crucial in higher education to improve problem-solving, research, and teaching by acquiring, sharing, and applying knowledge. Higher education institutions can promote knowledge management through Communities of Practice, but doing so remains challenging due to cultural, organisational, and technological reasons. We present findings of the first step of co-design workshops with authentic higher education teaching teams that sought to understand (a) their practices as a community and any motivators and impediments to their community development; (b) how they perceived the tools they use for knowledge management; and (c) the kinds of tools they believed could help them better conduct knowledge management and develop as Communities of Practice. Our findings suggested four essential design requirements and informed our development of a new tool to support the knowledge management needs of higher education teaching teams.

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.

We propose a learning analytics-based methodology for assessing the collaborative writing of humans and generative artificial intelligence. Framed by the evidence-centered design, we used elements of knowledge-telling, knowledge transformation, and cognitive presence to identify assessment claims; we used data collected from the CoAuthor writing tool as potential evidence for these claims; and we used epistemic network analysis to make inferences from the data about the claims. Our findings revealed significant differences in the writing processes of different groups of CoAuthor users, suggesting that our method is a plausible approach to assessing human-AI collaborative writing.

We report on the design and evaluation of a novel technique for analysing the sociocognitive nature of collaborative problem-solving—neural epistemic network analysis (NENA). NENA combines the computational power and representational ability of graph neural networks (GNNs) to naturally incorporate social and cognitive features in the analysis with the interpretative advantages of epistemic network analysis (ENA). Comparing NENA and ENA on two datasets from collaborative problem-solving contexts, we found that NENA improves upon ENA’s ability to distinguish between known subgroups in CPS data, while also improving the interpretability and explainability of GNN results.

Coding discourse data is critical to many learning analytics studies. To code their data, researchers may use manual techniques, automated techniques, or a combination thereof. Manual coding can be time-consuming and error prone; automated coding can be difficult to implement for non-technical users. Generative artificial intelligence (GAI) offers a user friendly alternative to automated discourse coding via prompting and APIs. We assessed the ability of GAI, specifically the GPT class of models, at automatically coding discourse in the context of a learning analytics study using a variety of prompting and training strategies. We found that fine-tuning approaches produced the best results; however, no results achieved standard thresholds for reliability in our field.

Even though the engagement in self-regulated learning (SRL) has been shown to boost academic performance, SRL skills of many learners remain underdeveloped. They often struggle to productively navigate multiple cognitive, affective, metacognitive and motivational (CAMM) processes in SRL. To provide learners with the required SRL support, it is essential to understand how learners enact CAMM processes as they study. More research is needed to advance the measurement of affective and motivational processes within SRL, and investigate how these processes influence learners’ cognition and metacognition. With this in mind, we conducted a lab study involving 22 university students who worked on a 45-minute reading and writing task in digital learning environment. We used a wearable electroencephalogram device to record learner academic emotional and motivational states, and digital trace data to record learner cognitive and metacognitive processes. We harnessed time series prediction and explainable artificial intelligence methods to examine how learner’s emotional and motivational states influence their choice of cognitive and metacognitive processes. Our results indicate that emotional and motivational states can predict learners’ use of low cognitive, high cognitive and metacognitive processes with considerable classification accuracy (F1 > 0.73), and that higher values of interest, engagement and excitement promote cognitive processing.

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.

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.

This research aimed to explore the relationship between different indicators of the depth and quality of participation in computer-mediated learning environments. By using network analyses and statistical tests, we discovered significant associations between the cognitive presence phases of the Community of Inquiry framework and speech acts, and examined the impact of two different instructional interventions on these associations. We found that there are strong associations between some speech acts and cognitive presence phases. In addition, the study revealed that the association between speech acts and cognitive presence is moderated by external facilitation, but not affected by user role assignment. The results suggest that speech acts can plausibly be used to provide feedback in relation to cognitive presence and can potentially be used to increase the generalizability of cognitive presence classification.

Collaborative problem solving is defined as having cognitive and social dimensions. While network analytic techniques such as epistemic network analysis (ENA) and social network analysis (SNA) have been successfully used to investigate the patterns of cognitive and social connections that describe CPS, few attempts have been made to combine the two approaches. Building on prior work that used ENA and SNA metrics as independent predictors of collaborative learning, we propose and test the integrated social-epistemic network signature (iSENS), an approach that affords the simultaneous investigation of cognitive and social connections. We tested iSENS on data collected from military teams participating in training scenarios. Our results suggest that (1) these teams are defined by specific patterns of cognitive and social connections, (2) iSENS networks are able to capture these patterns, and (3) iSENS is a better predictor of team outcomes compared to ENA alone, SNA alone, and a non-integrated SENS approach.

This paper reports a Design-Based Research project developing a tool (the Process Tab) that supports teachers' interventions with students in virtual internships. The tool uses a networked approach and allows insights into the discourse of groups and individuals based on contributions in chat fora and assignments.