47
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Using data-informed learning design to support teacher to understand students’ learning sentiment via journal entries

, , , , &

References

  • Aldhous, P. (2012). NodeXL for network analysis. NICAR 2012, St Louis, MO. http://www.peteraldhous.com/CAR/NodeXL_CAR2012.pdf
  • Altrabsheh, N., Cocea, M., & Fallahkhair, S. (2015). Predicting students’ emotions using machine learning techniques. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9112, 537–540. https://doi.org/10.1007/978-3-319-19773-9_56
  • Altrabsheh, N., Gaber, M. M., & Cocea, M. (2013). SA-E: Sentiment analysis for education. Frontiers in Artificial Intelligence and Applications, 255, 353–362. https://doi.org/10.3233/978-1-61499-264-6-353
  • Baek, C., & Doleck, T. (2021). Educational data mining versus learning analytics: A review of publications from 2015 to 2019. Interactive Learning Environments, 31(6), 3828–3850. https://doi.org/10.1080/10494820.2021.1943689
  • Batrinca, B., & Treleaven, P. C. (2015). Social media analytics: A survey of techniques, tools and platforms. AI & SOCIETY, 30(1), 89–116. https://doi.org/10.1007/s00146-014-0549-4
  • Bergström, P. (2010). Process-based assessment for professional learning in higher edu- cation: Perspectives on the student-teacher relationship. International Review of Re- Search in Open and Distance Learning, 11(2), 33–48. https://doi.org/10.19173/ir-rodl.v11i2.816
  • Blumenstein, M. (2020). Synergies of learning analytics and learning design: A systematic review of student outcomes. Journal of Learning Analytics, 7(3), 13–32. https://doi.org/10.18608/JLA.2020.73.3
  • Decker, J., & Beltran, V. (2021). Preservice teachers in distance learning: Mitigating the impact on social and emotional learning. International Journal of Online Pedagogy and Course Design, 11(3), 49–61. https://doi.org/10.4018/IJOPCD.2021070104
  • Durães, D., Toala, R., & Novais, P. (2021). Emotion analysis in distance learning. Ad- Vances in Intelligent Systems and Computing, 1328 AISC, 629–639. https://doi.org/10.1007/978-3-030-68198-2_58
  • Goffman, E. (1959). The presentation of self in everyday life. Overlook Press.
  • Goleman, D. (1995). Emotional intelligence (the 10th anniversary edition). Bantam Dell.
  • Gunawardena, C. N., Flor, N., Gómez, D., & Sánchez, D. (2016). Analyzing social con- struction of knowledge online by employing interaction analysis, learning analytics, and social network analysis. Quarterly Review of Distance Education, 17(3), 35.
  • Järvenoja, H., & Järvelä, S. (2009). Emotion control in collaborative learning situations: Do students regulate emotions evoked by social challenges? British Journal of Educational Psychology, 79(3), 463–481. https://doi.org/10.1348/000709909X402811
  • Kechaou, Z., Ben Ammar, M., & Alimi, A. M. (2011). Improving e-learning with sentiment analysis of users’ opinions. https://doi.org/10.1109/EDUCON.2011.5773275
  • Kort, B., Reily, R., & Picard, R. (2001). An affective model of interplay between emotions and learning: Reengineering educational pedagogy—building a learning compan- ion. Proceedings IEEE International Conference on Advanced Learning Technology: Issues, Achievements and Challenges, 43–48.
  • Lawson, C. (2005). The Connections Between Emotions and Learning. Center for Development and Learning. http://www. dl.org/resourcelibrary/articles/connect_emotions.php
  • Li, L., Flynn, K. S., DeRosier, M. E., Weiser, G., & Austin-King, K. (2021). Social-emotional learning amidst COVID-19 school closures: Positive findings from an efficacy study of adventures aboard the S.S. GRIN Program. Frontiers in Education, 6. https://doi.org/10.3389/feduc.2021.683142
  • Liu, B. (2012). Sentiment analysis and opinion mining. Morgan & Claypool Publishers.
  • Mah, D.-K., Yau, J. Y.-K., & Ifenthaler, D. (2019). Epilogue: Future directions on learning analytics to enhance study success. In Utilizing learning analytics to support study success (pp. 313–321). Springer International Publishing. https://doi.org/10.1007/978-3-319-64792-0
  • Martinho, D., Sobreiro, P., & Vardasca, R. (2021). Teaching sentiment in emergency online learning—A conceptual model. Education Sciences, 11(2), 1–16. https://doi.org/10.3390/educsci11020053
  • Maybee, C., & Zilinski, L. (2015). Data informed learning: A next phase data literacy framework for higher education. Proceedings of the Association for Information Science and Technology, 52(1), 1–4. https://doi.org/10.1002/pra2.2015.1450520100108
  • Mohhammad, S. M., & Turney, P. D. (2012). Crowdsourcing a word-emotion association lexicon. Computation Intelligence, 29(3), 436–465. https://doi.org/10.1111/j.1467-8640.2012.00460.x
  • Munezero, M., Montero, C. S., Mozgovoy, M., & Sutinen, E. (2013). Exploiting sentiment analysis to track emotions in students’ learning diaries. https://doi.org/10.1145/2526968.2526984
  • Oksanen, K., & Hämäläinen, R. (2013). Perceived sociability and social presence in a collaborative serious game. International Journal of Game-Based Learning, 3(1), 34–50. https://doi.org/10.4018/ijgbl.2013010103
  • Plutchik, R. (1980). A general psychoevolutionary theory of emotion. In Emotion: Theory, research, and experience (Vol. 1, pp. 3–33). Academic Press.
  • Rodriguez, P., Ortigosa, A., & Carro, R. M. (2012). Extracting emotions from texts in elearning environments. Sixth International Conference on Complex, Intelligent, and Software Intensive Systems., 887–892.
  • Treceñe, J. K. D. (2019). Delving the sentiments to track emotions in gender issues: A plutchik-based sentiment analysis in students’ learning diaries. International Journal of Scientific and Technology Research, 8(12), 1134–1139.
  • Wise, A. (2016). Data-informed learning environments. EDUCAUSE Review. https://er.educause.edu/articles/2016/10/data-informed-learning-environments
  • Zou, W., Pan, Z., Li, C., & Liu, M. (2021). Does social presence play a role in learners’ positions in MOOC learner network? A machine learning approach to analyze social presence in discussion forums. Communications in Computer and Information Science, 1312, 248–264. https://doi.org/10.1007/978-3-030-67788-6_17

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.