RESEARCH PAPER
Topic modeling of the student emails sent before and during the birth of COVID-19 in physics and math classes
 
More details
Hide details
1
Seoul National University, Seoul, SOUTH KOREA
 
2
Yongsan International School of Seoul, Seoul, SOUTH KOREA
 
 
Publication date: 2022-09-13
 
 
EURASIA J. Math., Sci Tech. Ed 2022;18(10):em2167
 
KEYWORDS
ABSTRACT
The COVID-19 pandemic caused physical classes to suddenly transition to online learning all over the globe two years ago, resulting in students becoming more active in online email communication. The emails sent by the students were observed to contain students' concerns and needs for teacher support during the early stages of worldwide online classes due to COVID-19. As such, this study was interested in those email contents that were explored and analyzed through topic modeling, network analysis, and grounded theory. Six hundred twenty-three emails sent by seventy students in physics and math classes were analyzed using InfraNodus. This tool can perform topic modeling and visualize network graphs of verbal text data such as emails. By topic modeling and network graphical analysis, the findings revealed that the main topic clusters of the student emails corpus pertain to class assessments – questions and tests. Moreover, the influential keywords in the network graphs were coded, and the emails representing those keywords were further categorized using grounded theory. Doing so led to the finding that students needed teacher support on the content and supportive pedagogy. Supportive pedagogy needs may include test goals, schedule, content, and procedures, reviewing the test solutions and answers, and providing necessary test accommodations. Further study on teacher support in the online physics class and the effect of delivering teacher support on the student’s performance can be a topic of future research.
 
REFERENCES (30)
1.
Alawamleh, M., Al-Twait, L. M., & Al-Saht, G. R. (2020). The effect of online learning on communication between instructors and students during the COVID-19 pandemic. Asian Education and Development Studies, 11(2). https://doi.org/10.1108/AEDS-0....
 
2.
Alghamdi, R., & Alfalqi, K. (2015). A survey of topic modeling in text mining. International Journal of Advanced Computer Science and Applications, 6(1), 147-153. https://doi.org/10.14569/IJACS....
 
3.
Blei, D. M., & Lafferty, J. D. (2009). Topic models. In D. M. Blei, & J. D. Lafferty (Eds.), Text mining (pp. 101-124). Chapman and Hall/CRC. https://doi.org/10.1201/978142....
 
4.
Cai, Z., Eagan, B., Dowell, N., Pennebaker, J., Shaffer, D., & Graesser, A. (2017). Epistemic network analysis and topic modeling for chat data from the collaborative learning environment. In X. Hu, T. Barnes, A. Hershkovitz, & L. Paquette (Eds.), Proceedings of the 10th International Conference on Educational Data Mining (pp. 104-111). EDM Society.
 
5.
Charmaz, K., & Bryant, A. (2010). The SAGE handbook of grounded theory. SAGE. https://doi.org/10.1016/B978-0....
 
6.
Csanadi, A., Eagan, B., Kollar, I., Shaffer, D. W., & Fischer, F. (2018). When coding-and-counting is not enough: using epistemic network analysis (ENA) to analyze verbal data in CSCL research. International Journal of Computer-Supported Collaborative Learning, 13(4), 419-438. https://doi.org/10.1007/s11412....
 
7.
Dickinson, A. (2017). Communicating with the online student: The impact of email tone on student performance and teacher evaluations. Journal of Educators Online, 14(2), 1-10. https://doi.org/10.9743/jeo.20....
 
8.
Diehl, A., Abdul-Rahman, A., Bach, B., El-Assady, M., Kraus, M., Laramee, R. S., & Chen, M. (2022). Characterizing grounded theory approaches in visualization. arXiv:2203.01777. https://doi.org/10.48550/arXiv....
 
9.
Dürscheid, C., Frehner, C., Herring, S. C., Stein, D., & Virtanen, T. (2013). Email communication. Handbooks of Pragmatics, (9), 35-54. https://doi.org/10.1515/978311....
 
10.
Fougt, S. S., Siebert-Evenstone, A., Eagan, B., Tabatabai, S., & Misfeldt, M. (2018). Epistemic network analysis of students’ longer written assignments as formative/summative evaluation. In Proceedings of the 8th International Conference on Learning Analytics and Knowledge (pp. 126-130). ACM. https://doi.org/10.1145/317035....
 
11.
Gibbs, S. (2016). How did email grow from messages between academics to a global epidemic? The Guardian. https://www.theguardian.com/te....
 
12.
Glaser, B. G., & Strauss, A. L. (2017). Discovery of grounded theory: Strategies for qualitative research. Routledge. https://doi.org/10.4324/978020....
 
13.
Glesne, C. (1999). Glesne, Corrine, becoming qualitative researchers: An introduction. Longman.
 
14.
Kayumova, L. R., Gainullina, L. N., Akhmadieva, R. S., Matvienko, V. V., & Kabakhidze, E. L. (2021). Using interactive platform “round” to organize online leisure activities for children during the pandemic. EURASIA Journal of Mathematics, Science and Technology Education, 17(10), em2016. https://doi.org/10.29333/ejmst....
 
15.
Maphosa, C., Van Den Berg, G., & Mudau, P. K. (2021). Assessment of the perceived usefulness of mobile phone technology for communication in learning by distance education students in a rural-based university. African Perspectives of Research in Teaching and Learning, 5(2), 45-61.
 
16.
Martin, A. (2020). How to optimize online learning in the age of coronavirus (COVID-19): A 5-point guide for educators. UNSW Newsroom, 53(9), 1-30.
 
17.
Miller, M. A. (2020). The importance of tone and attitude in email and the online classroom. In C. N. Stevenson, & J. C. Bauer (Eds.), Enriching collaboration and communication in online learning communities (pp. 52-67). IGI Global. https://doi.org/10.4018/978-1-....
 
18.
Naji, K. K., Du, X., Tarlochan, F., Ebead, U., Hasan, M. A., & Al-Ali, A. K. (2020). Engineering students’ readiness to transition to emergency online learning in response to COVID-19: Case of Qatar. EURASIA Journal of Mathematics, Science and Technology Education, 16(10), em1886. https://doi.org/10.29333/ejmst....
 
19.
Nambiar, D. (2020). The impact of online learning during COVID-19: Students’ and teachers’ perspectives. The International Journal of Indian Psychology, 8(2), 783-793.
 
20.
Paranyushkin, D. (2019). InfraNodus: Generating insight using text network analysis. In Proceedings of the World Wide Web Conference (pp. 3584-3589). https://doi.org/10.1145/330855....
 
21.
Porras-Hernández, L. H., & Salinas-Amescua, B. (2013). Strengthening TPACK: A broader notion of context and the use of teacher’s narratives to reveal knowledge construction. Journal of Educational Computing Research, 48(2), 223-244. https://doi.org/10.2190/EC.48.....
 
22.
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, 9-45. https://doi.org/10.18608/jla.2....
 
23.
Shaw, E., Phillips, R., Kao, A. & Torres, R (2017). Using topic modeling to assess middle school science discourse based on next generation science standards. In Proceedings of the 2017 International Conference on Advanced Technologies Enhancing Education (pp. 42-45). Atlantis press. https://doi.org/10.2991/icat2e....
 
24.
Sintema, E. J. (2020). Effect of COVID-19 on the performance of grade 12 students: Implications for STEM education. EURASIA Journal of Mathematics, Science and Technology Education, 16(7), em1851. https://doi.org/10.29333/ejmst....
 
25.
Strauss, A., & Corbin, J. M. (1997). Grounded theory in practice. SAGE.
 
26.
Yang, Y., & Cornelius, L. F. (2004). Students’ perceptions towards the quality of online education: A qualitative approach. In Proceedings of the 27th Association for Educational Communications and Technology (pp. 861-877).
 
27.
Yates, A., Starkey, L., Egerton, B., & Flueggen, F. (2020). High school students’ experience of online learning during COVID-19: The influence of technology and pedagogy. Technology, Pedagogy and Education, 30(1), 59-73. https://doi.org/10.1080/147593....
 
28.
Yun, E. (2020). Review of trends in physics education research using topic modeling. Journal of Baltic Science Education, 19(3), 388-400. https://doi.org/10.33225/jbse/....
 
29.
Zhang, W. Y., & Perris, K. (2004). Researching the efficacy of online learning: A collaborative effort amongst scholars in Asian open universities. Open Learning: The Journal of Open, Distance and e-Learning, 19(3), 247-264. https://doi.org/10.1080/026805....
 
30.
Zhou, S., Gu, H., & Yao, J. (2018). From “strict learning” to “smart learning”: An analysis of the key factors affecting students’ academic performance: An empirical study based on the educational quality monitoring data of 262245 students in Jiangsu province. Primary Middle School Management, 32(11), 39-42.
 
eISSN:1305-8223
ISSN:1305-8215
Journals System - logo
Scroll to top