Agrawal, A., Venkatraman, J., Leonard, S., & Paepcke, A. (2015). YouEDU: Addressing confusion in MOOC discussion forums by recommending instructional video clips. Proceedings of the 8th International Conference on Educational Data Mining, 297–304. http://ilpubs.stanford.edu:8090/1125/1/you_edu.pdf
Akyol, Z., & Garrison, D. R. (2011). Understanding cognitive presence in an online and blended community of inquiry: Assessing outcomes and processes for deep approaches to learning. British Journal of Educational Technology, 42(2), 233–250. https://doi.org/10.1111/j.1467-8535.2009.01029.x
Article
Google Scholar
Alario-Hoyos, C., Estévez-Ayres, I., Pérez-Sanagustín, M., Kloos, C. D., & Fernández-Panadero, C. (2017). Understanding learners’ motivation and learning strategies in MOOCs. International Review of Research in Open and Distributed Learning, 18(3), 119–137. https://doi.org/10.19173/irrodl.v18i3.2996
Almatrafi, O., Johri, A., & Rangwala, H. (2018). Needle in a haystack: Identifying learner posts that require urgent response in MOOC discussion forums. Computers and Education, 118, 1–9. https://doi.org/10.1016/j.compedu.2017.11.002
Article
Google Scholar
Asch, V. Van. (2013). Macro-and micro-averaged evaluation measures. Belgium: CLiPS, 1–27. https://pdfs.semanticscholar.org/1d10/6a2730801b6210a67f7622e4d192bb309303.pdf
Atapattu, T., Falkner, K., Thilakaratne, M., Sivaneasharajah, L., & Jayashanka, R. (2019). An identification of learners’ confusion through language and discourse analysis. ArXiv Preprint ArXiv: 1903.03286.
Barbosa, G., Camelo, R., Cavalcanti, A. P., Miranda, P., Mello, R. F., Kovanovic, V., & Gaševic, D. (2020). Towards automatic cross-language classification of cognitive presence in online discussions. ACM International Conference Proceeding Series. https://doi.org/10.1145/3375462.3375496
Article
Google Scholar
Breiman, L. (2001). Random forests. Machine Learning., 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
Article
MATH
Google Scholar
Buchem, I., Amenduni, F., Poce, A. M. V., Andone, A., Tur, G., Urbina, S., & Šmitek, B. (2020). Integrating mini-moocs into study programs in higher education during COVID-19 five pilot case studies in context of the open virtual mobility project. Human and Artificial Intelligence for the Society of the Future. 299–310. https://doi.org/10.38069/edenconf-2020-ac0028
Casella, G., Fienberg, S., & Olkin, I. (2013). Resampling methods. In An introduction to statistical learning with applications in R (p. 181). Springer Science+Business Media. https://doi.org/10.1016/j.peva.2007.06.006
Chakravarthi, B. R., Priyadharshini, R., Muralidaran, V., Suryawanshi, S., Jose, N., Sherly, E., McCrae, J. P., & Mural-idaran, V. (2020). Overview of the track on sentiment analysis for dravidian languages in code-mixed text; overview of the track on sentiment analysis for Dravidian languages in code-mixed text. https://doi.org/10.1145/3441501.3441515
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16(Sept. 28), 321–357. https://doi.org/10.1613/jair.953
Article
MATH
Google Scholar
Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement. https://doi.org/10.1177/001316446002000104
Article
Google Scholar
Corich, S., Hunt, K., & Hunt, L. M. (2004). Assessing discussion forum participation: In search of quality. International Journal of Instructional Technology & Distance Learning.
Corich, S., Hunt, K., & Hunt, L. M. (2006). Computerised content analysis for measuring critical thinking within discussion forums. Journal of E-Learning and Knowledge Society, 2(1), 47–60.
Google Scholar
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Mlm. http://arxiv.org/abs/1810.04805
Dowell, N., Graesser, A., & Cai, Z. (2016). Language and discourse analysis with coh-metrix: Applications from educational material to learning environments at scale. Journal of Learning Analytics, 3(3), 72–95. https://doi.org/10.18608/jla.2016.33.5
Article
Google Scholar
Elgort, I., Lundqvist, K., & Mcdonald, J. (2018). Analysis of student discussion posts in a MOOC: Proof of concept. Companion Proceedings 8th International Conference on Learning Analytics & Knowledge (LAK18). https://www.researchgate.net/publication/324417971
Farrow, E., Moore, J., & Gasevic, D. (2019). Analysing discussion forum data: a replication study avoiding data contamination. The 9th International Learning Analytics & Knowledge Conference (LAK19), March. https://doi.org/10.1145/3303772.3303779
Farrow, E., Moore, J., & Gašević, D. (2020). Dialogue attributes that inform depth and quality of participation in course discussion forums. The 10th International Conference on Learning Analytics and Knowledge (LAK ’20), 129–134. https://doi.org/10.1145/3375462.3375481
Finegold, A., & Cooke, L. (2006). Exploring the attitudes, experiences and dynamics of interaction in online groups. Internet and Higher Education, 9(3), 201–215. https://doi.org/10.1016/j.iheduc.2006.06.003
Article
Google Scholar
Galikyan, I., Admiraal, W., & Kester, L. (2021). MOOC discussion forums: The interplay of the cognitive and the social. Computers & Education, 165, 104133. https://doi.org/10.1016/J.COMPEDU.2021.104133
Article
Google Scholar
Gardner, J., Brooks, C., Andres, J. M., & Baker, R. (2018). Replicating MOOC predictive models at scale. Proceedings of the 5th Annual ACM Conference on Learning at Scale, L at S 2018. https://doi.org/10.1145/3231644.3231656
Garrison, D. R., & Anderson, T. (2011). E-learning in the 21st century: A framework for research and practice (Second Edi). Routledge.
Book
Google Scholar
Garrison, D. R., Anderson, T., & Archer, W. (1999). Critical inquiry in a text-based environment: Computer conferencing in higher education. The Internet and Higher Education, 2(2), 87–105. https://doi.org/10.1016/S1096-7516(00)00016-6
Article
Google Scholar
Garrison, D. R., Anderson, T., & Archer, W. (2001). Critical thinking, cognitive presence, and computer conferencing in distance education. American Journal of Distance Education, 15(1), 7–23. https://doi.org/10.1080/08923640109527071
Article
Google Scholar
Graesser, A. C., McNamara, D. S., Cai, Z., Conley, M., Li, H., & Pennebaker, J. (2014). Coh-Metrix measures text characteristics at multiple levels of language and discourse. Elementary School Journal. https://doi.org/10.1086/678293
Article
Google Scholar
Graesser, A. C., McNamara, D. S., Louwerse, M. M., & Cai, Z. (2004). Coh-Metrix: Analysis of text on cohesion and language. Behavior Research Methods, Instruments, and Computers, 36(2), 193–202. https://doi.org/10.3758/BF03195564
Article
Google Scholar
Honnibal, M., & Montani, I. (2017). spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing. To appear, 7(1), 411-420.
Hu, Y., Donald, C., & Giacaman, N. (2021). Cross validating a rubric for automatic classification of cognitive presence in MOOC discussions. International Review of Research in Open and Distributed Learning, 23(2), 242–260. https://doi.org/10.19173/irrodl.v23i3.5994
Article
Google Scholar
Hu, Y., Donald, C., Giacaman, N., & Zhu, Z. (2020). Towards automated analysis of cognitive presence in MOOC discussions: a manual classification study. The 10th International Conference on Learning Analytics and Knowledge (LAK ’20), 135–140. https://doi.org/10.1145/3375462.3375473
Kovanovic, V., Gasevic, D., & Hatala, M. (2014). Learning analytics for communities of inquiry. Journal of Learning Analytics, 1(3), 195–198. https://doi.org/10.18608/jla.2014.13.21.
Article
Google Scholar
Kovanović, V., Joksimović, S., Gašević, D., & Hatala, M. (2014). Automated cognitive presence detection in online discussion transcripts. Proceedings of TheWorkshops at the LAK2014 Conference Co-Located with 4th International Conference on Learning Analytics and Knowledge (LAK 2014).
Kovanović, V., Joksimović, S., Waters, Z., Gašević, D., Kitto, K., Hatala, M., & Siemens, G. (2016). Towards automated content analysis of discussion transcripts. Proceedings of the Sixth International Conference on Learning Analytics & Knowledge - LAK ’16, 15–24. https://doi.org/10.1145/2883851.2883950
Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics. https://doi.org/10.2307/2529310
Article
MATH
Google Scholar
Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C. H., & Kang, J. (2020). BioBERT: A pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4), 1234–1240. https://doi.org/10.1093/bioinformatics/btz682
Article
Google Scholar
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V., & Allen, P. G. (2019). RoBERTa: A robustly optimized BERT pretraining approach. https://arxiv.org/abs/1907.11692v1
Lohr, S. (2020). Remember the MOOCs? After neardeath, they’re booming. The New Yorker Times. https://www.nytimes.com/2020/05/26/technology/moocs-online-learning.html
Louppe, G., Wehenkel, L., Sutera, A., & Geurts, P. (2013). Understanding variable importances in Forests of randomized trees. In C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, & K. Q. Weinberger (Eds.), Advances in Neural Information Processing Systems 26 (NIPS 2013) (pp. 431–439). Curran Associates, Inc. http://papers.nips.cc/paper/4928-understanding-variable-importances-in-forests-of-randomized-trees.pdf
Manning, C. D., & Schütze, H. (1999). Foundations of statistical natural language processing. MIT Press.
MATH
Google Scholar
McKlin, T., Harmon, S., Evans, W., & Jones, M. (2001). Cognitive presence in web-based learning: A content analysis of students’ online discussions. Annual Proceedings of Selected Research and Development, 272–277.
Moore, R. L., Oliver, K. M., & Wang, C. (2019). Setting the pace: Examining cognitive processing in MOOC discussion forums with automatic text analysis. Interactive Learning Environments, 27(5–6), 655–669. https://doi.org/10.1080/10494820.2019.1610453
Article
Google Scholar
Moore, R. L., Yen, C. J., & Powers, F. E. (2020). Exploring the relationship between clout and cognitive processing in MOOC discussion forums. British Journal of Educational Technology, 52(1), 482–497. https://doi.org/10.1111/bjet.13033
Article
Google Scholar
Neto, V., Rolim, V., Ferreira, R., Kovanovi, V., & Gašević, D. (2018). Automated analysis of cognitive presence in online discussions written in Portuguese. In: V. Pammer-Schindle, M. Pérez-Sanagustín, H. Drachsler, R. Elferink, & M. Scheffel (Eds.), Lifelong technology-enhanced learning (Vol. 11082, pp. 245–261). https://doi.org/10.1007/978-3-319-98572-5_19
Neto, V., Rolim, V., Pinheiro, A., Lins, R. D., Gašević, D., & Mello, R. F. (2021). Automatic content analysis of online discussions for cognitive presence: A study of the generalizability across educational contexts. IEEE Transactions on Learning Technologies, 14(3), 299–312. https://doi.org/10.1109/TLT.2021.3083178
Article
Google Scholar
Park, C. (2009). Replicating the use of a cognitive presence measurement tool. Journal of Interactive Online Learning, 8(2), 140–155. http://hdl.handle.net/2149/2330
Pennebaker, J. W., Boyd, R. L., Jordan, K., & Blackburn, K. (2015). The development and psychometric properties of LIWC2015 (pp. 1–22). University of Texas at Austin. https://doi.org/10.15781/T29G6Z
Book
Google Scholar
Ramos, J. (2003). Using TF-IDF to determine word relevance in document queries. Proceedings of the First Instructional Conference on Machine Learning.
Rourke, L., & Anderson, T. (2004). Validity in quantitative content analysis validity in quantitative content analysis. Educational Technology Research and Development, 52(1), 5.
Article
Google Scholar
Rourke, L., & Kanuka, H. (2009). Learning in communities of inquiry: A review of the literature (Winner 2009 Best Research Article Award). International Journal of E-Learning & Distance Education / Revue Internationale Du e-Learning et La Formation à Distance, 23(1), 19–48. https://www.ijede.ca/index.php/jde/article/view/474
Rourke, L., Anderson, T., Garrison, D. R., & Archer, W. (1999). Assessing social presence in asynchronous text-based computer conferencing. The Journal of Distance Education / Revue de l’ducation Distance, 14(2), 50–71. https://www.learntechlib.org/p/92000/
Tausczik, Y. R., & Pennebaker, J. W. (2009). The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psychology, 29(1), 24–54. https://doi.org/10.1177/0261927X09351676
Article
Google Scholar
Waters, Z., Kovanović, V., Kitto, K., & Gašević, D. (2015). Structure matters: Adoption of structured classification approach in the context of cognitive presence classification. Lecture Notes in Computer Science, 9460, 227–238. https://doi.org/10.1007/978-3-319-28940-3_18
Article
Google Scholar
Zhu, M., Bonk, C. J., & Sari, A. R. (2018). Instructor experiences designing MOOCs in higher education: Pedagogical, resource, and logistical considerations and challenges. Online Learning, 22(4), 203–241. https://doi.org/10.24059/olj.v22i4.1495
Article
Google Scholar