Advances in Quantitative Ethnography

Table of Contents

1. Gaming and Augmented Reality

   1. Frontmatter

   2. A Quantitative Ethnographic Analysis of Caregiver Competencies and Engagement in Augmented Reality Geriatric Simulation

      Behdokht Kiafar, Salam Daher, Asif Ahmmed, Roghayeh Leila Barmaki

      Abstract

      The need to improve geriatric care quality presents a challenge that requires insights from stakeholders. While simulation-based training can enhance caregiving competencies, extracting meaningful insights from these experiences to inform simulation design remains a challenge. In this study, we employed Epistemic Network Analysis (ENA), Ordered Network Analysis (ONA), and Chronologically-Ordered Representations of Discourse and Tool-Related Activity (CORDTRA) within an Augmented Reality simulation to analyze caregiver competencies and engagement. Twenty participants interacted with a virtual geriatric patient across two conditions: an unaware condition, where the virtual patient lacked contextual awareness, and an aware condition, where the patient offered personalized responses and reacted to environmental and conversational cues. Results showed that participants provided more supportive care and demonstrated stronger person-centered caregiving behaviors when interacting with an aware virtual patient. In addition, ONA during a reasoning task revealed a significant difference between the two conditions, suggesting more adaptive strategies in the aware condition. CORDTRA analysis further indicated higher participant engagement when the virtual patient expressed awareness. These findings have implications for Human-Computer Interaction and nursing education by demonstrating how Quantitative Ethnography can be applied to dynamic, multimodal simulations to evaluate and inform the design of effective training systems.

   3. Not All Who Wander Are Lost: Trailblazing Trajectories in a Minecraft-Based Learning Environment

      Xiner Liu, Yiqiu Zhou, Jaclyn Ocumpaugh, Amanda Barany, Andres Felipe Zambrano, Zhanlan Wei, Ryan S. Baker, Camille Giordano

      Abstract

      This study investigates how students explored a Minecraft-based science learning environment by analyzing their in-game movement trajectories. We use GPT-4o to identify recurring trajectory patterns from gameplay visualizations and to automatically label trajectory images, with some constructs labeled and reviewed by humans. These patterns are examined in relation to students’ self-reported survey measures. Epistemic Network Analysis is used to compare how different movement behaviors co-occurred across different learner profiles. The findings showed that students with high or improving outcomes engaged in more flexible exploration. They often wandered, changed directions, and alternated between looking around the environment before closely examining specific objects. In contrast, students with low or declining outcomes tended to concentrate on specific areas and frequently backtracked to previously visited locations. These findings highlight the importance of how, not just how much, students explore in open-ended environments as part of the learning process.

   4. Let Me Explain: Linking Situational Interest to Student Response in Interviews

      Zhanlan Wei, Amanda Barany, Jaclyn Ocumpaugh, Xiner Liu, Andres Felipe Zambrano, Ryan S. Baker, Camille Giordano

      Abstract

      This study investigates how situational interest (SI) influences student interview responses during game-based learning. Using real-time interviews conducted within the What-If Hypothetical Implementations in Minecraft (WHIMC) environment, we analyzed differences in discourse between high- and low-SI students. Interview transcripts were coded using a structured codebook through a hybrid approach combining humans and GPT-4o. Epistemic Networks of student reflections revealed that high-SI students were more likely to offer Brief and Enthusiastic responses across all question types. These students used excited language, reacted aloud to game events, and expressed interest in specific gameplay elements. Low-SI students provided more Explanatory and Neutral responses. They often paused to describe their plans, explain in-game decisions, or reason through moments of uncertainty. Ordered Networks of interviewer statements revealed that interviewers’ strategies remained consistent for low- and high-SI groups, ruling out interviewer behavior as the cause of differences in student responses. These results shed light on the ways student interest levels may impact interview responses during game-based learning experiences.

   5. Modeling Player Progression in an Educational Game Using Ordered Networks

      Xiner Liu, Jennifer Scianna, Shari J. Metcalf, Zhanlan Wei, Ryan S. Baker, Amanda Barany, Luke Swanson, David J. Gagnon

      Abstract

      Understanding the sequence of player decisions in open-ended educational games provides insight into how those decisions influence player persistence or readiness for later challenges. This study uses Ordered Network Analysis to examine how players move between jobs of varying difficulty in the educational game Wake. To scaffold players, Wake breaks down multi-phase scientific investigations into smaller “jobs”. Each job is manually coded based on its difficulty level in Experimentation, Modeling, or Argumentation. We use these difficulty ratings, along with whether the player completed or quit it, as codes to model player progression. We see that players who completed a job with a high quit rate on their first attempt more often followed paths with gradually increasing difficulty prior to accepting that job. In contrast, other players who quit the same job with a high quit rate on their first attempt were more likely to have failed prior jobs requiring basic skills in Experimentation or Modeling when moving from jobs that did not involve such components. They also tended to remain within jobs without such components across multiple transitions, which may reflect lower preparedness or content knowledge compared to those who completed the later difficult job. Findings also show that players who completed the difficult job on their second attempt spent the time between attempts completing jobs with lower difficulty, which may have helped strengthen foundational skills relevant to the target job or restore confidence. These findings point to opportunities for progression-aware intervention design based on how successful and unsuccessful players move through different types of jobs.

   6. Better to Be Confused or Frustrated Than Bored: Analyzing Affect Dynamics Across Player Archetypes

      Andres Felipe Zambrano, Jaclyn Ocumpaugh, Ryan S. Baker, Jessica Vandenberg

      Abstract

      Ordered Network Analysis (ONA) helps us to understand the dynamics of specific events over time. Recent work has leveraged these benefits of ONA to study affect dynamics, the shifts in emotions over time, theorizing differences that might be driven by both motivational factors and even learning experiences. This study uses ONA to investigate these relationships within the context of the affective transitions made by students of four behavioral archetypes—Scanners, Worksheet Users, Conversers, and Roamers—in Crystal Island, a game designed to support inquiry-based learning in microbiology. Results indicate that while both Scanners and Worksheet Users exhibited more positively valenced affect dynamics, only Worksheet Users showed measurable learning gains—likely due to their greater engagement in systematic exploration and reflection, particularly while experiencing and regulating confusion and frustration. In contrast, Conversers and Roamers experienced persistent boredom and prolonged confusion, accompanied by low motivation and poor performance. These findings suggest that successful learning in game-based environments depends not only on positive affect but also on the presence of productive struggle and effective emotional regulation, demonstrating the potential of an analytical approach centered around ONA to elucidate the relationships between behavior and affect during learning.

   7. Analyzing Brain Activity and User Experience Across Input Modalities Using Quantitative Ethnography

      Shayla Sharmin, Behdokht Kiafar, Roghayeh Leila Barmaki

      Abstract

      This study investigates how brain activity and user experience differ when using a hand vs. a stylus for touch display interactions in a computer-based educational game. We used functional near-infrared spectroscopy to measure brain activity from the prefrontal cortex during the gameplay. We also collected self-reported user experience via questionnaires. Sixteen participants completed quiz tasks using both input methods. Epistemic network analysis showed differences in how brain activity and user experience patterns are linked together for each input method. Additionally, we conducted a delta-based correlation and network analysis, inspired by the principles of chronologically ordered representations of discourse and tool-related activity, to investigate how participant-level changes in brain and self-reported behavioral measures aligned across hand and stylus conditions. The result showed that changes in brain activity weakly align with subjective experience. Results also showed significantly higher brain activity with the stylus, which suggests increased cognitive demand, but users found the hand method easier to use, performed better, and felt less frustrated than with the stylus. These findings indicate that input modality has an impact on brain behavior and user experiences while using educational tools.

2. Education and Self Learning

   1. Frontmatter

   2. Modeling and Measuring Sociocritical AI Literacies with Epistemic Network Analysis

      Golnaz Arastoopour Irgens, Atefeh Behboudi, Alicia C. Lane

      Abstract

      As artificial intelligence (AI) increasingly shapes societal systems, there is a need for educational models that integrate sociopolitical critique with technical AI learning. This study uses Epistemic Network Analysis (ENA) to investigate how Black middle school girls interrogate and reimagine AI in service of justice. Guided by Critical Race Technology Theory and Black feminist thought, the curriculum engaged students in activities that connected AI concepts with discussions of bias, representation, and systemic oppression. Using ENA, we modeled students’ discourse across curricular phases, revealing distinct patterns in how learners connected technical knowledge, social critique, and their lived experiences. Results show that students transitioned from understanding algorithmic limitations to expressing commitments to social justice through their AI designs. ENA served as a powerful tool to measure and visualize these learning trajectories. Findings highlight the potential of ENA to empirically support how learners integrate a sociocritical lens into technical disciplinary content.

   3. Still Under the Hammer and Sickle: Race, Repression, and Violations of Academic Freedom in Cuban Universities

      Amalia Z. Daché, Jennifer Mesa, Danielle P. Espino

      Abstract

      Cuba’s higher education system is often lauded for its commitment to equity and social development, particularly in the fields of education and health. Yet, it remains one of the lowest-ranked countries globally in terms of academic freedom. This study applies epistemic network analysis (ENA), a technique within quantitative ethnography, to visualize and analyze how violations of academic freedom in Cuban universities are experienced along racial and gender lines. Drawing on 81 documented cases, our findings show that AfroCubans, compared to White Cubans, face more immediate and severe repercussions, such as expedited expulsions and terminations. Black men are disproportionately targeted with false accusations of sexual misconduct, while Black women experience sustained institutional censorship alongside acts of resistance. In contrast, White Cuban women are more likely to encounter prolonged administrative coercion. These patterns reveal deeply racialized and gendered dynamics in the enforcement of ideological conformity, suggesting that racism and sexism continue to structure academic repression in Cuba’s universities—despite the egalitarian rhetoric of the revolutionary state.

   4. A Quantitative Ethnography on Leadership Values and Principles of Asian American Women Senior Leaders in Higher Education

      Catherine L. Zhang, Julie E. Wollman, Xiaotong Jenna Lu

      Abstract

      This study explores the leadership narratives of 11 Asian American women serving as deans, vice presidents, provosts, and presidents at four-year institutions across the United States. Drawing on interview data, we analyze these leaders’ narratives to identify expressed values and leadership principles. Using quantitative ethnography, we identify connections between these values and principles, finding that while individual participants are not monolithic and their expressions vary, there are common themes and connections among their values and principles. These findings deepen our understanding of the values of Asian American women senior leaders in higher education and how these values take shape in leadership principles.

   5. Demands-Resources in Doctoral Education: Mapping Pathways to Dropout Intention and Careers in Further Research

      Jae Young Han, Shane David Iveson, Zachari Swiecki

      Abstract

      Doctoral attrition and the post-PhD “brain drain” are persistent shocks to the research pipeline. Drawing on the Doctoral Demands-Resources (DD-R) framework, we used quantitative ethnography to examine how 25 Australian PhD candidates narrate (a) persistence versus withdrawal; and (b) ambitions for a career in further research (CFR). Semi-structured interviews were thematically coded into six demands and four resources categories, then analyzed with Epistemic Network Analysis (ENA). Means-related ENA scores were regressed on dropout and CFR intentions while controlling for study mode, motivation profile, and candidature stage. Resources-weighted triads, such as Social Support, Informational Support and Support Network, characterized both persisters and CFR-aspirants. Conversely, demand-weighted triads, i.e., Workload, Exhaustion, and Lack of supervisory support, dominated the discourse among students considering attrition or apathetic about further research. Outcomes variables remained the strongest predictors of ENA structure after covariate adjustment. Findings extend DD-R by showing that the same motivational ecology underpins both near-team persistence and longer-term research-career identity. We outline practical recommendations: formalized peer hubs, supervisor feedback loops, and flexible policies for part-time or care-burdened candidates.

   6. A Quantitative Ethnography Study Guided by Community Cultural Wealth and Physics Identity

      Geraldine L. Cochran, Stella Nelson, Sabrina Henige, Kazi Aatish Imroz

      Abstract

      Physics bridge programs are designed to widen doctoral pathways for students, yet little is known about how participants’ community-rooted resources intersect with their emerging physics identities. Guided by Yosso’s conceptualization of community cultural wealth and critical adaptations of the physics identity construct, we are conducting a secondary analysis of eight semi-structured interviews with graduate students drawn from four bridge programs for the purposes of completing a quantitative ethnography study. In this paper, we discuss with transparency our decision-making and activities in the collection, segmentation, and codification stages of this study. In particular, our aim is to elucidate the initial steps of the quantitative ethnography process for new comers to the field who—like us—are trying to design a study that is guided by framework.

   7. Task Negotiation in Socially Shared Regulation of Learning: Ordered Network Analysis of Collaborative Groups in a Global STEM Learning Community

      Seung B. Lee, Danielle P. Espino, Eric R. Hamilton

      Abstract

      Successful collaborative learning requires participants to collectively negotiate and align their cognitive, behavioral and motivational processes towards a shared outcome. This group-level phenomenon, referred to as socially shared regulation of learning (SSRL), relies on an iterative process of coordination and regulation of task-related perceptions, activities and goals. Negotiation of tasks is a key part of SSRL within a collaborative learning setting. Using ordered network analysis (ONA), this paper examines the directional connections of student group discourse during collaborative learning, focusing on task negotiation behavior within SSRL processes. In particular, the analysis identifies shifts in task negotiation behavior during online group meetings for two collaborative STEM project teams involving middle and high school students from three countries. Findings show that early collaboration emphasizes shared task understanding and later shifts toward task planning and coordination. Task monitoring also increases as students more frequently share challenges, prompting discussions that renegotiate and refine task perceptions.

   8. Guide on the Side or Sage on the Stage?: Exploring the Relationship Between Teachers’ Spatial and Verbal Discursive Strategies

      Daniel J. Noh

      Abstract

      How teachers move through and position themselves in classroom spaces, relative to students, plays a critical role in structuring classroom interactions. While research in Multimodal Learning Analytics (MMLA) has explored these socio-spatial behaviors, less work has examined their intersection with verbal discursive strategies. This study investigates how teachers’ movement patterns and classroom design could be used to contextually interpret dialogic teaching strategies. Using data from the TIMSS 1999 Video Study, this paper presents an analysis of four U.S. mathematics and science lessons featuring distinct classroom layouts (row-desks and grouped-desks). Using Ordered Network Analysis (ONA), the study explores (1) how teachers’ spatial proximity to students relates to patterns of verbal teaching moves and (2) how classroom design influences these relationships. The findings contribute to a deeper understanding of teaching strategies, showcasing how ONA could be used to interpret how spatial and dialogic pedagogical strategies interact to shape classroom discourse. By comparing lessons across different subjects and configurations, this study aims for a richer understanding of how teacher movement and dialogic discourse relate and highlights the need for more systematic studies of multimodal teaching.