The influence of students’ epistemic orientations on their learning behavior and outcomes is well-documented. However, limited research explores students’ epistemic orientations in terms of conceptual engagement and learning outcomes. This study, set within the context of higher education, examined the patterns of conceptual engagement among two performance groups and identifies differences in their epistemic orientations. Both epistemic network analysis (ENA) and ordered network analysis (ONA) methods were used. The results from the ENA revealed distinct trajectories and patterns of conceptual engagement between high-performing and low-performing students during different periods in their learning journey. High-performing students were able to establish a more interconnected and distributed epistemic network earlier than their low-performing counterparts. ONA results revealed that (1) high-performing students were more inclined to employ abstract theoretical concepts to address empirical concerns, doing so more frequently and earlier; and (2) low-performing students benefitted from forum interactions with high-performing students to expand their knowledge resources and engagement with theoretical constructs over time. These discoveries contribute to our comprehension of epistemic orientations in different learners. The implications of this study could help generate learning analytics that monitor students’ conceptual engagement in forum discussion and provide feedback to guide the design of learning.

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.

Learning analytics research increasingly studies classroom learning with AI-based systems through rich contextual data from outside these systems, especially student-teacher interactions. One key challenge in leveraging such data is generating meaningful insights into effective teacher practices. Quantitative ethnography bears the potential to close this gap by combining multimodal data streams into networks of co-occurring behavior that drive insight into favorable learning conditions. The present study uses transmodal ordered network analysis to understand effective teacher practices in relationship to traditional metrics of in-system learning in a mathematics classroom working with AI tutors. Incorporating teacher practices captured by position tracking and human observation codes into modeling significantly improved the inference of how efficiently students improved in the AI tutor beyond a model with tutor log data features only. Comparing teacher practices by student learning rates, we find that students with low learning rates exhibited more hint use after monitoring. However, after an extended visit, students with low learning rates showed learning behavior similar to their high learning rate peers, achieving repeated correct attempts in the tutor. Observation notes suggest conceptual and procedural support differences can help explain visit effectiveness. Taken together, offering early conceptual support to students with low learning rates could make classroom practice with AI tutors more effective. This study advances the scientific understanding of effective teacher practice in classrooms learning with AI tutors and methodologies to make such practices visible.

The introduction of collaborative robots (cobots) into the workplace has presented both opportunities and chal-lenges for those seeking to utilize their functionality. Prior research has shown that despite the capabilities afforded by cobots, there is a disconnect between those capabilities and the applications that they currently are deployed in, partially due to a lack of effective cobot-focused instruction in the field. Experts who work successfully within this collaborative domain could offer insight into the considerations and process they use to more effectively capture this cobot capability. Using an analysis of expert insights in the collaborative interaction design space, we developed a set of Expert Frames based on these insights and integrated these Expert Frames into a new training and programming system that can be used to teach novice operators to think, program, and troubleshoot in ways that experts do. We present our system and case studies that demonstrate how Expert Frames provide novice users with the ability to analyze and learn from complex cobot application scenarios.

Collaborative robots, or cobots, represent a breakthrough technology designed for high-level (e.g. collaborative) interactions between workers and robots with capabilities for flexible deployment in industries such as manufacturing. Understanding how workers and companies use and integrate cobots is important to inform the future design of cobot systems and educational technologies that facilitate effective worker-cobot interaction. Yet, little is known about typical training for collaboration and the application of cobots in manufacturing. To close this gap, we interviewed nine experts in manufacturing about their experience with cobots. Our thematic analysis revealed that, contrary to the envisioned use, experts described most cobot applications as only low-level (e.g. pressing start/stop buttons) interactions with little flexible deployment, and experts felt traditional robotics skills were needed for collaborative and flexible interaction with cobots. We conclude with design recommendations for improved future robots, including programming and interface designs, and educational technologies to support collaborative use.

Analyses of learning often rely on coded data. One important aspect of coding is establishing reliability. Previous research has shown that the common approach for establishing coding reliability is seriously flawed in that it produces unacceptably high Type I error rates. This paper focuses on testing whether or not these error rates correspond to specific reliability metrics or a larger methodological problem. Our results show that the method for establishing reliability is not metric specific, and we suggest the adoption of new practices to control Type I error rates associated with establishing coding reliability.

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.