Titlebook: Advances in Quantitative Ethnography; 6th International Co Yoon Jeon Kim,Zachari Swiecki Conference proceedings 2024 The Editor(s) (if appl

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Shifting Paradigms: Trends in Quantitative Ethnography Research at ICQE. This study analyzed conference paper abstracts from the International Conference on Quantitative Ethnography (ICQE) from 2022 and 2023 using epistemic network analysis (ENA). Research in 2022 emphasized foundational QE aspects and methodologies, while 2023 focused on using the research for social

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Analyzing Nursing Assistant Attitudes Towards Geriatric Caregiving Using Epistemic Network Analysis insights, but they can be biased and have limited generalizability, while quantitative methods may miss nuances. To address these limitations, network-based approaches such as Epistemic Network Analysis (ENA) can bridge the methodological gap. By leveraging the strengths of both methods, ENA provid

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Exploring Students’ Changing Conceptions About eLearning Leadershipin the literature to address such challenges, gauging the extent of change and evolution in students’ understanding remains difficult. This paper investigates the potential of Epistemic Network Analysis (ENA) in offering insights on changing understanding at class, group, and individual levels in a

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Studying the Interplay of Self-regulated Learning Cycles and Scaffolding Through Ordered Network AnaSRL, comparisons of SRL processes across learning platforms and domains are scarce. As most prior research has investigated SRL patterns across learner performance levels, methods are lacking to investigate if adaptive support adequately supports learners’ temporal SRL during problem solving. This s

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1865-0929 fully reviewed and selected from 82 submissions. They are organized in the following topical sections:. .Theory and Methods; Teaching and Learning; and Cultural Discourse..978-3-031-76334-2978-3-031-76335-9Series ISSN 1865-0929 Series E-ISSN 1865-0937

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https://doi.org/10.1007/978-3-211-49309-0es ENA’s visualization strengths with ARM’s ability to identify more complex patterns. Using human-human tutoring transcripts, we illustrate how ARM and EARN can complement ENA by offering insights on directional conditional relationships between groups and pairs of constructs, offering a more nuanced understanding of complex phenomena.