A Systematic Literature Review of Empirical Research on Epistemic Network Analysis in Education
Open accessAbstract
Over the past decade, epistemic network analysis (ENA) has emerged as a quantitative ethnography tool for modeling discourse in different types of human behaviors. This article offers a comprehensive systematic review of ENA educational applications in empirical studies ($\text{n}=76$) published between 2010 and 2021. We review the ENA methods that research has relied on, the use of educational theories, their method of application, comparisons across groups and the main findings. Our results show that ENA has helped visually model the coded interactions and illustrate the connection strength among elements of network models. The applications of ENA have expanded beyond discourse analysis to several new areas of inquiry such as modeling surveys, log files or game play. Most of the reviewed articles used ENA based on educational theories and frameworks ($\text{n}=53$, 69.7%), with one or more theories per article, while 23 articles (30.3%) did not report theoretical grounding. The implementation of ENA has enabled comparisons across groups and helped augment the insights of other methods such as process mining, however there is little evidence that studies have exploited the quantitative potential of ENA. Most of the reviewed studies used ENA on small sample size with manually coded interactions with few examples of large samples and automated coding.
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References 115 · Cited by 103
Research works printed but not linked: 13
- D. W. Shaffer, Quantitative Ethnography. Madison, WI, USA: Cathcart Press, 2017.
- E. Bagley, ''The epistemography of urban and regional planning 912: Appropriation in the face of resistance,'' in Proc. 9th Int. Conf. Learn. Sci., 2010, pp. 81-88.
- D. Hatfield and D. W. Shaffer, ''The epistemography of journalism 335: Complexity in developing journalistic expertise,'' in Proc. 9th Int. Conf. Learn. Sci. (ICLS), 2010, pp. 628-635.
- Z. Cai, B. Eagan, N. M. Dowell, J. W. Pennebaker, D. W. Shaffer, and A. C. Graesser, ''Epistemic network analysis and topic modeling for chat data from collaborative learning environment,'' in Proc. 10th Int. Conf. Educ. Data Mining, Jan. 2017, pp. 104-111.
- A. Csanadi, B. Eagan, D. W. Shaffer, I. Kollar, and F. Fischer, ''Collab- orative and individual scientific reasoning of pre-service teachers: New insights through epistemic network analysis (ENA),'' in Proc. Comput. Collab. Learn. Conf., 2017, pp. 215-222.
- G. Arastoopour, N. Chesler, and D. W. Shaffer, ''A simulation-based approach for increasing women in engineering,'' in Proc. Comput.- Supported Collaborative Learn. Conf. (CSCL), 2013, pp. 217-218.
- D. W. Shaffer, ''Epistemic frames and islands of expertise: Learning from infusion experiences,'' in Proc. 6th Int. Conf. Learn. Sci. (ICLS), 2004, pp. 473-480.
- D. W. Shaffer and A. R. Ruis, ''How We Code,'' in Advances in Quan- titative Ethnography (Communications in Computer and Information Science), vol. 1312, A. R. Ruis and S. B. Lee, Eds. Cham, Switzerland: Springer, 2021, pp. 62-77.
- A. R. Ruis, A. L. Siebert-Evenstone, R. Pozen, B. R. Eagan, and D. W. Shaffer, ''Finding common ground: A method for measuring recent temporal context in analyses of complex, collaborative thinking,'' in Proc. Comput.-Supported Collaborative Learn. Conf. (CSCL), 2019, pp. 136-143.
- H. Sung, C. Shengyang, A. R. Ruis, and D. W. Shaffer, ''Reading for breadth, reading for depth: Understanding the relationship between reading and complex thinking using epistemic network analysis,'' in Proc. 13th Comput.-Supported Collaborative Learn. Conf. (CSCL), 2019, pp. 376-383.
- G. Arastoopour and D. W. Shaffer, ''Epistemography and professional CSCL environment design,'' in Proc. Comput.-Supported Collaborative Learn. Conf., 2015, vol. 1, pp. 204-211.
- J. Oshima, R. Oshima, A. Ohsaki, and J. Michael Splichal, ''Col- lective knowledge advancement through shared epistemic agency: Socio-semantic network analyses,'' in Proc. 13th Comput.-Supported Collaborative Learn. Conf. (CSCL), 2019, pp. 57-64.
- H. Talafian, M. Shah, A. Barany, and A. Foster, ''Promoting and tracing high school students' identity change in an augmented virtual learning environment,'' in Proc. 13th Int. Conf. Comput. Supported Collaborative Learn. (CSCL), 2019, pp. 216-223.
- Authors
- Elmoazen, R., Saqr, M., Tedre, M., & Hirsto, L.
- Affiliations
- University of Eastern Finland (FI)
- Venue
- IEEE Access
- Published
- 2022
- Volume
- 10
- Pages
- 17330-17348
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W4210936346
- Cited by
- 103
- References
- 115
- Citations by year
- 2026: 29 · 2025: 32 · 2024: 27 · 2023: 14 · 2022: 1
- FWCI
- 3.03
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Education › Educational Research and Methods
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- epistemic network analysis, educational software, discourse modeling, network models, educational theory, process mining, automated coding, log analysis, group comparison
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