Skip to main content
SpringerLink
Log in

‘SSEEN’: a networked approach to uncover connections between sentiment, social, and epistemic elements of student online forum discourse

  • Development Article
  • Published:
Educational technology research and development Aims and scope Submit manuscript

  • 2 Altmetric

Abstract

Educational researchers have pointed to socioemotional dimensions of learning as important in gaining a more nuanced description of student engagement and learning. However, to date, research focused on the analysis of emotions has been narrow in its focus, centering on affect and sentiment analysis in isolation while neglecting how emotions potentially interact with social elements and course objectives in learning environments. In this paper, we present a case study analysis of seven asynchronous online discussions delivered as part of a blended-learning bachelor level course; we demonstrate the utility of a novel analysis workflow and visualization method which we refer to as SSEEN (social sentiment embedded epistemic networks) to uncover insights into the connection between social networks, course content, and detected student sentiment. The findings show that negative sentiment was most often associated with course content, but contrary to insights from prior research, negative sentiment served as a marker of engagement as students connected content to their own personal experiences. By simultaneously considering the social network alongside the sentiment-colored edges, we note that negative sentiment is not consistent within the discourse of particular students or between pairs indicating that sentiment did not appear to be an indicator of peer conflict or breaches in social contracts. The findings demonstrate how the proposed approach (SSEEN) can support educational researchers to gain a more nuanced understanding of the social, emotional, and epistemic dimensions of learning in asynchronous online discussions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • An, P., Holstein, K., d’Anjou, B., Eggen, B., & Bakker, S. (2020). The TA framework: Designing real-time teaching augmentation for K-12 classrooms. In Proceedings of the 2020 CHI conference on human factors in computing systems (pp. 1–17). https://doi.org/10.1145/3313831.3376277

  • Arastoopour Irgens, G., Shaffer, D. W., Swiecki, Z., Ruis, A. R., & Chesler, N. C. (2016). Teaching and assessing engineering design thinking with virtual internships and epistemic network analysis. International Journal of Engineering Education, 32(2), 1492–1501.

    Google Scholar 

  • Arguel, A., Pachman, M., & Lockyer, L. (2018). Identifying epistemic emotions from activity analytics in interactive digital learning environments. In J. M. Lodge, J. C. Horvath, & L. Corrin (Eds.), Learning analytics in the classroom (pp. 56–68). Routledge.

    Chapter  Google Scholar 

  • Borchers, C., Rosenberg, J. M., & Gibbons, B. (2021). To scale or not to scale: Comparing popular sentiment analysis dictionaries on educational twitter data. Proceedings of the 14th International Conference on Educational Data Mining, 619–624. https://educationaldatamining.org/EDM2021/EDM2021Proceedings.pdf

  • Cai, Z., Siebert-Evenstone, A., Eagan, B., & Williamson Shaffer, D. (2021). Using topic modeling for code discovery in large scale text data. ICQE 2020 Advances in Quantitative Ethnography. https://doi.org/10.1007/978-3-030-67788-6_2

    Article  Google Scholar 

  • Chaplot, D. S., Rhim, E., & Kim, J. (2015). Predicting student attrition in MOOCs using sentiment analysis and neural networks. In AIED Workshops, 53, 54–57.

    Google Scholar 

  • Chen, B., Chang, Y. H., Ouyang, F., & Zhou, W. (2018). Fostering student engagement in online discussion through social learning analytics. The Internet and Higher Education, 37, 21–30.

    Article  Google Scholar 

  • Chevrier, M., Muis, K. R., Trevors, G. J., Pekrun, R., & Sinatra, G. M. (2019). Exploring the antecedents and consequences of epistemic emotions. Learning and Instruction, 63, 101209. https://doi.org/10.1016/j.learninstruc.2019.05.006

    Article  Google Scholar 

  • D’Mello, S. (2017). Emotional learning analytics. Handbook of Learning Analytics. https://doi.org/10.18608/hla17.010

    Article  Google Scholar 

  • Dado, M., & Bodemer, D. (2017). A review of methodological applications of social network analysis in computer-supported collaborative learning. Educational Research Review, 22, 159–180. https://doi.org/10.1080/87567555.2018.1453472

    Article  Google Scholar 

  • Dowell, N. M., & Poquet, O. (2021). SCIP: Combining group communication and interpersonal positioning to identify emergent roles in scaled digital environments. Computers in Human Behavior, 119, 106709.

    Article  Google Scholar 

  • El Alaoui, I., Gahi, Y., & Messoussi, R. (2019, April). Full consideration of big data characteristics in sentiment analysis context. In 2019 IEEE 4th International Conference on Cloud Computing and Big Data Analysis (ICCCBDA) (pp. 126–130). IEEE.

  • Frenzel, A. C., Becker-Kurz, B., Pekrun, R., Goetz, T., & Lüdtke, O. (2018). Emotion transmission in the classroom revisited: A reciprocal effects model of teacher and student enjoyment. Journal of Educational Psychology, 110(5), 628–639. https://doi.org/10.1037/edu0000228

    Article  Google Scholar 

  • Gasevic, D. (2019, May 8). Using Learning Analytics to Measure 21st-Century Skills. Proceedings of the Australian Council for Educational Research Conference 2019. Australian Council for Educational Research Conference 2019, Melbourne, Australia. https://research.acer.edu.au/research_conference/RC2019/5august/3/

  • Gašević, D., Joksimović, S., Eagan, B. R., & Shaffer, D. W. (2019). SENS: Network analytics to combine social and cognitive perspectives of collaborative learning. Computers in Human Behavior, 92, 562–577.

    Article  Google Scholar 

  • Gkontzis, A. F., Karachristos, C. V., Panagiotakopoulos, C. T., Stavropoulos, E. C., & Verykios, V. S. (2017). Sentiment analysis to track emotion and polarity in student fora. Proceedings of the 21st Pan-Hellenic Conference on Informatics. https://doi.org/10.1145/3139367.3139389

    Article  Google Scholar 

  • Goodlad, K., Westengard, L., & Hillstrom, J. (2018). Comparing faculty and student perception of academic performance, classroom behavior, and social interactions in learning communities. College Teaching, 66(3), 130–139. https://doi.org/10.1080/87567555.2018.1453472

    Article  Google Scholar 

  • Hagberg, A., Swart, P., & Chult, S. (2008). Exploring network structure, dynamics, and function using NetworkX. Los Alamos National Lab.

    Google Scholar 

  • Han, Z. M., Huang, C. Q., Yu, J. H., & Tsai, C. C. (2021). Identifying patterns of epistemic emotions with respect to interactions in massive online open courses using deep learning and social network analysis. Computers in Human Behavior, 122, 106843.

    Article  Google Scholar 

  • Hernández-García, Á., González-González, I., Jiménez-Zarco, A. I., & Chaparro-Peláez, J. (2015). Applying social learning analytics to message boards in online distance learning: A case study. Computers in Human Behavior, 47, 68–80. https://doi.org/10.1016/j.chb.2014.10.038

    Article  Google Scholar 

  • Hod, Y., Katz, S., & Eagan, B. (2020). Refining qualitative ethnographies using epistemic network analysis: A study of socioemotional learning dimensions in a humanistic knowledge building community. Computers & Education, 156, 103943. https://doi.org/10.1016/j.compedu.2020.103943

    Article  Google Scholar 

  • Huang, C., Han, Z., Li, M., Wang, X., & Zhao, W. (2021). Sentiment evolution with interaction levels in blended learning environments: Using learning analytics and epistemic network analysis. Australasian Journal of Educational Technology, 37(2), 81–95. https://doi.org/10.14742/ajet.6749

    Article  Google Scholar 

  • Hutto, C., & Gilbert, E. (2014). Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media, 8(1), 216.

    Article  Google Scholar 

  • Kagklis, V., Karatrantou, A., Tantoula, M., Panagiotakopoulos, C. T., & Verykios, V. S. (2015). A learning analytics methodology for detecting sentiment in student fora: A case study in distance education. European Journal of Open, Distance and E-Learning, 18(2), 74–94.

    Article  Google Scholar 

  • Kaliisa, R., & Dolonen, J. A. (2022). CADA: a teacher-facing learning analytics dashboard to foster teachers’ awareness of students’ participation and discourse patterns in online discussions. Technology, Knowledge and Learning, 28, 1–22.

    Google Scholar 

  • Knowles, M. (2021). EpistemicNetworkAnalysis.jl (0.1.0) [Julia]. https://github.com/snotskie/ EpistemicNetworkAnalysis.jl

  • Kumar, A., & Sebastian, T. M. (2012). Sentiment analysis on twitter. International Journal of Computer Science Issues (IJCSI), 9(4), 372.

    Google Scholar 

  • Mänty, K., Järvenoja, H., & Törmänen, T. (2022). The sequential composition of collaborative groups’ emotion regulation in negative socio-emotional interactions. European Journal of Psychology of Education, 38, 203.

    Article  Google Scholar 

  • Marquart, C., Hinojosa, C., Swiecki, Z., Eagan, B., & Williamson Shaffer, D. W. (2019). Epistemic Network Analysis [Online software] (1.5.2) [Computer software]. Epistemic Analytics. http://app.epistemicnetwork.org

  • Meyer, D. K., & Turner, J. C. (2002). Discovering emotion in classroom motivation research. Educational Psychologist, 37(2), 107–114. https://doi.org/10.1207/S15326985EP3702_5

    Article  Google Scholar 

  • Misiejuk, K., Scianna, J., Kaliisa, R., Vachuska, K., & Williamson Shaffer, D. (2021). Incorporating sentiment analysis with epistemic network analysis to enhance discourse analysis of twitter data. ICQE 2020: Advances in Quantitative Ethnography Science, 22, 345.

    Google Scholar 

  • Mohammad, S. M. (2016). Sentiment analysis: Detecting valence, emotions, and other affectual states from text. In H. L. Meiselman (Ed.), Emotion measurement (pp. 201–237). Woodhead Publishing.

    Chapter  Google Scholar 

  • Mohammad, S. M., Salameh, M., & Kiritchenko, S. (2016). How translation alters sentiment. Journal of Artificial Intelligence Research, 55, 95–130.

    Article  Google Scholar 

  • Muis, K. R., Psaradellis, C., Lajoie, S. P., Leo, I. D., & Chevrier, M. (2015). The role of epistemic emotions in mathematics problem solving. Contemporary Educational Psychology, 42, 172–185.

    Article  Google Scholar 

  • Näykki, P., Järvelä, S., Kirschner, P. A., & Järvenoja, H. (2014). Socio-emotional conflict in collaborative learning—A process-oriented case study in a higher education context. International Journal of Educational Research, 68, 1–14. https://doi.org/10.1016/j.ijer.2014.07.001

    Article  Google Scholar 

  • Newman, H., & Joyner, D. (2018). Sentiment analysis of student evaluations of teaching. In Artificial Intelligence in Education: 19th International Conference, AIED 2018, London, UK, June 27–30, 2018, Proceedings, Part II 19 (pp. 246–250). Springer.

  • Ocumpaugh, J. (2015). Baker Rodrigo Ocumpaugh monitoring protocol (BROMP) 2.0 technical and training manual. Teachers College, Columbia University and Ateneo Laboratory for the Learning Sciences.

    Google Scholar 

  • Oshima, J., Oshima, R., & Matsuzawa, Y. (2012). Knowledge building discourse explorer: A social network analysis application for knowledge building discourse. Educational Technology Research and Development, 60(5), 903–921.

    Article  Google Scholar 

  • Pekrun, R., Vogl, E., Muis, K., & Sinatra, G. (2016). Measuring emotions during epistemic activities: The epistemically-related emotion scales. Cognition and Emotion. https://doi.org/10.1080/02699931.2016.1204989

    Article  Google Scholar 

  • Pozzi, F., Fersini, E., Messina, E., & Liu, B. (2016). Sentiment analysis in social networks. Morgan Kaufmann.

    Google Scholar 

  • Rani, S., & Kumar, P. (2017). A sentiment analysis system to improve teaching and learning. Computer, 50(5), 36–43.

    Article  Google Scholar 

  • Reeves, T. C., & Oh, E. G. (2017). The goals and methods of educational technology research over a quarter century (1989–2014). Educational Technology Research and Development, 65, 325–339.

    Article  Google Scholar 

  • Rivers, S. E., Brackett, M. A., Reyes, M. R., Elbertson, N. A., & Salovey, P. (2013). Improving the social and emotional climate of classrooms: A clustered randomized controlled trial testing the RULER approach. Prevention Science, 14(1), 77–87. https://doi.org/10.1007/s11121-012-0305-2

    Article  Google Scholar 

  • Rosé, C. P., & Ferschke, O. (2016). Technology support for discussion-based learning: From computer supported collaborative learning to the future of massive open online courses. International Journal of Artificial Intelligence in Education, 26(2), 660–678. https://doi.org/10.1007/s40593-016-0107-y

    Article  Google Scholar 

  • Säljö, R. (2009). Learning, theories of learning and units of analysis in research. Educational Psychologist, 44(3), 202–208. https://doi.org/10.1080/00461520903029030

    Article  Google Scholar 

  • Saqr, M., Viberg, O., & Vartiainen, H. (2020). Capturing the participation and social dimensions of computer-supported collaborative learning through social network analysis: Which method and measures matter? International Journal of Computer-Supported Collaborative Learning, 15(2), 227–248. https://doi.org/10.1007/s11412-020-09322-6

    Article  Google Scholar 

  • Schutz, P. A., & Pekrun, R. (Eds.). (2007). Emotion in education. Elsevier Academic Press.

    Google Scholar 

  • Scott, J. (2011). Social network analysis: Developments, advances, and prospects. Social Network Analysis and Mining, 1(1), 21–26. https://doi.org/10.1145/3375462.3375505

    Article  Google Scholar 

  • Shaffer, D. W. (2017). Quantitative ethnography. Cathcart Press.

  • Shaffer, D. W., Collier, W., & Ruis, A. R. (2016). A tutorial on epistemic network analysis: Analyzing the structure of connections in cognitive, social, and interaction data. Journal of Learning Analytics, 3(3), 9–45.

    Article  Google Scholar 

  • Siebert-Evenstone, A. L., Irgens, G. A., Collier, W., Swiecki, Z., Ruis, A. R., & Shaffer, D. W. (2017). In search of conversational grain size: Modeling semantic structure using moving stanza windows. Journal of Learning Analytics, 4(3), 123–139.

    Article  Google Scholar 

  • Suero Montero, C., & Suhonen, J. (2014, November). Emotion analysis meets learning analytics: online learner profiling beyond numerical data. In Proceedings of the 14th Koli calling international conference on computing education research (pp. 165–169).

  • Swiecki, Z., & Williamson Shaffer, D. (2020). ISENS: An Integrated Approach to Combining Epistemic and Social Network Analyses. 10th International Conference on Learning Analytics & Knowledge, 305–313. https://doi.org/10.1145/3375462.3375505

  • Wen, M., Yang, D., & Rose, C. (2014, July). Sentiment Analysis in MOOC Discussion Forums: What does it tell us?. In Proceedings of the 7th International Conference on Educational Data Mining (EDM 2014).

  • Wise, A. F., & Vytasek, J. (2017). Learning analytics implementation design. Handbook of Learning Analytics, 1, 151–160.

    Article  Google Scholar 

  • Yeager, D. S. (2017). Social and emotional learning programs for adolescents. The Future of Children, 27(1), 73–94.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Wisconsin - Madison, Madison, USA

    Jennifer Scianna

  2. University of Oslo, Oslo, Norway

    Rogers Kaliisa

Authors
  1. Jennifer Scianna
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rogers Kaliisa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Jennifer Scianna or Rogers Kaliisa.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Scianna, J., Kaliisa, R. ‘SSEEN’: a networked approach to uncover connections between sentiment, social, and epistemic elements of student online forum discourse. Education Tech Research Dev (2023). https://doi.org/10.1007/s11423-023-10310-4

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11423-023-10310-4

Keywords

Search

Navigation