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Numerous computer-based collaborative learning environments have been developed to support collaborative problem-solving. Yet, understanding the complexity and dynamic nature of the collaboration process remains a challenge. This is particularly true in open-ended immersive learning environments, where students navigate both physical and virtual spaces, pursuing diverse paths to solve problems. In response, we aimed to unpack these complex collaborative learning processes by investigating 16 groups of college students (n = 77) who utilized an immersive astronomy simulation in their introductory astronomy course. Our specific focus is on joint attention as a multi-level indicator to index collaboration. To examine the interplay between joint attention and other multimodal traces (conceptual discussions and gestures) in students’ interactions with peers and the simulation, we employed a multi-granular approach. This approach encompasses macro-level correlations, meso-level network trends, and micro-level qualitative insights from vignettes to capture nuances at different levels. Distinct multimodal engagement patterns emerged between low- and high-achieving groups, evolving over time across a series of tasks. Our findings contribute to the understanding of the notion of timely joint attention and emphasize the importance of individual exploration during the early stages of collaborative problem-solving, demonstrating its contribution to productive knowledge coconstruction. This research overall provides valuable insights into the complexities of collaboration dynamics within and beyond digital space. The empirical evidence we present in our study lays a strong foundation for developing instructional designs aimed at fostering productive collaboration in immersive learning environments. 

Self-regulated learning (SRL) is the ability to regulate cognitive, metacognitive, motivational, and emotional states while learning and is posited to be a strong predictor of academic success. It is therefore important to provide learners with effective instructions to promote more meaningful and effective SRL processes. One way to implement SRL instructions is through providing real-time SRL scaffolding while learners engage with a task. However, previous studies have tended to focus on fixed scaffolding rather than adaptive scaffolding that is tailored to student actions. Studies that have investigated adaptive scaffolding have not adequately distinguished between the effects of adaptive and fixed scaffolding compared to a control condition. Moreover, previous studies have tended to investigate the effects of scaffolding at the task level rather than shorter time segments—obscuring the impact of individual scaffolds on SRL processes. To address these gaps, we (a) collected trace data about student activities while working on a multi-source writing task and (b) analyzed these data using a cutting-edge learning analytic technique— ordered network analysis (ONA)—to model, visualize, and explain how learners' SRL processes changed in relation to the scaffolds. At the task level, our results suggest that learners who received adaptive scaffolding have significantly different patterns of SRL processes compared to the fixed scaffolding and control conditions. While not significantly different, our results at the task segment level suggest that adaptive scaffolding is associated with earlier engagement in SRL processes. At both the task level and task segment level, those who received adaptive scaffolding, compared to the other conditions, exhibited more task-guided learning processes such as referring to task instructions and rubrics in relation to their reading and writing. This study not only deepens our understanding of the effects of scaffolding at different levels of analysis but also demonstrates the use of a contemporary learning analytic technique for evaluating the effects of different kinds of scaffolding on learners' SRL processes. 

The introduction of collaborative robots (cobots) into the workplace has presented both opportunities and challenges 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. 

Studying interactions faces methodological challenges and existing methods, such as configural diagramming, have limitations. This work demonstrates Epistemic Network Analysis (ENA) as an analytical method to construct configural diagrams. We demonstrated ENA as an analytical tool by applying this method to study dementia caregiver work systems. We conducted 20 semistructured interviews with caregivers to collect caregiving experiences. Guided by the Patient Work System model, we conducted a directed content analysis to identify work system components and used ENA to study interactions between components. By using ENA to create configural diagrams, we identified five frequently occurring interactions, compared work system configurations of caregivers providing care at home and away from home. Although we were underpowered to determine statistically significant differences, we identified visual and qualitative differences. Our results demonstrate the capability of ENA as 

Coding data—defining concepts and identifying where they occur in data—is a critical aspect of qualitative data analysis, and especially so in quantitative ethnography. Coding is a central process for creating meaning from data, and while much has been written about coding methods and theory, relatively little has been written about what constitutes best practices for fair and valid coding, what justifies those practices, and how to implement them. In this paper, our goal is not to address these issues comprehensively, but to provide guidelines for good coding practice and to highlight some of the issues and key questions that quantitative ethnographers and other researchers should consider when coding data. 

A key goal of quantitative ethnographic (QE) models, and statistical models more generally, is to produce the most parsimonious model that adequately explains or predicts the phenomenon of interest. In epistemic network analysis (ENA), for example, this entails constructing network models with the fewest number of codes whose interaction structure provides sufficient explanatory power in a given context. Unlike most statistical models, however, modification of ENA models can affect not only the statistical properties but also the interpretive alignment between quantitative features and qualitative meaning that is a central goal in QE analyses. In this study, we propose a novel method, Parsimonious Removal with Interpretive Alignment, for systematically identifying more parsimonious ENA models that are likely to maintain interpretive alignment with an existing model. To test the efficacy of the method, we implemented it on a well-studied dataset for which there is a published, validated ENA model, and we show that the method successfully identifies reduced models likely to maintain explanatory power and interpretive alignment. 

Rapid advances in technology also come with increased training needs for people who engineer and interact with these technologies. One such technology is collaborative robots, cobots, which are designed to be safer and easier to use than their traditional robotic counterparts. However, there have been few studies of how people use cobots and even fewer identifying what a user must know to properly set up and effectively use cobots for their manufacturing processes. In this study, we interviewed nine experts in robots and automation in manufacturing settings. We employ a quantitative ethnographic approach to gain qualitative insights into the cultural practices of robotics experts and corroborate these stories with quantitative warrants. Both quantitative and qualitative analyses revealed that experts put safety first when designing and monitoring cobot applications. This study improves our understanding of expert problem-solving in collaborative robotics, defines an expert model that can serve as a basis for the development of an authentic learning technology, and illustrates a useful method for modeling expertise in vocational settings. 

While there has been much growth in the use of microblogging platforms (e.g., Twitter) to share information on a range of topics, researchers struggle to analyze the large volumes of data produced on such platforms. Established methods such as Sentiment Analysis (SA) have been criticized over their inaccuracy and limited analytical depth. In this exploratory methodological paper, we propose a combination of SA with Epistemic Network Analysis (ENA) as an alternative approach for providing richer qualitative and quantitative insights into Twitter discourse. We illustrate the application and potential use of these approaches by visualizing the differences between tweets directed or discussing Democrats and Republicans after the COVID-19 Stimulus Package announcement in the US. SA was integrated into ENA models in two ways: as a part of the blocking variable and as a set of codes. Our results suggest that incorporating SA into ENA allowed for a better understanding of how groups viewed the components of the stimulus issue by splitting them by sentiment and enabled a meaningful inclusion of data with singular subject focus into the ENA models. 

When text datasets are very large, manually coding line by line becomes impractical. As a result, researchers sometimes try to use machine learning algorithms to automatically code text data. One of the most popular algorithms is topic modeling. For a given text dataset, a topic model provides probability distributions of words for a set of “topics” in the data, which researchers then use to interpret meaning of the topics. A topic model also gives each document in the dataset a score for each topic, which can be used as a non-binary coding for what proportion of a topic is in the document. Unfortunately, it is often difficult to interpret what the topics mean in a defensible way, or to validate document topic proportion scores as meaningful codes. In this study, we examine how keywords from codes developed by human experts were distributed in topics generated from topic modeling. The results show that (1) top keywords of a single topic often contain words from multiple human-generated codes; and conversely, (2) words from human-generated codes appear as high-probability keywords in multiple topic. These results explain why directly using topics from topic models as codes is problematic. However, they also imply that topic modeling makes it possible for researchers to discover codes from short word lists.