Analyzing Learning Log Data
Log data from online learning platforms provide a powerful window into student learning processes though there are challenges to accessing and analyzing it.
Rodriguez, F., Lee, H. R., Rutherford, T., Fischer, C., Potman, E., & Warschauer, M. (2021). Using Clickstream Data Mining Techniques to Understand and Support First-Generation College Students in an Online Chemistry Course. Proceedings of the 11th International Conference on Learning Analytics & Knowledge (LAK '21). Virtual Conference. doi 10.1145/3448139.3448169 [link]
Yu, R., Li, Q., Fischer, C., Doroudi, S. & Xu, D. (2020). Towards Accurate and Fair Prediction of College Success: Evaluating Different Sources of Student Data. Proceedings of the 13th International Conference on Educational Data Mining (EDM). Ifrane, Morocco. [link]
Li, Q., Baker, R., & Warschauer, M. (2020). Using clickstream data to measure, understand, and support self-regulated learning in online courses. The Internet and Higher Education, 45, 100727. [link][preprint]
Fischer, C., Pardos, Z., Baker, R. S., Williams., J. J., Smyth, P., Yu, R., Slater, S., Baker, R., & Warschauer, M. (2020). Mining big data in education: Affordances and challenges. Review of Research in Education, 45(1). [link][preprint]
Baker, R., Xu, D., Park, J., Yu, R., Li, Q., Cung, B., Fischer, C., Rodriguez, F., Warschauer, M. & Smyth, P. (2020). The Benefits and Caveats of Using Clickstream Data to Understand Student Self-Regulatory Behaviors: Opening the Black Box of Learning Processes. International Journal of Educational Technology in Higher Education, 17(13), 1-24. doi 10.1186/s41239-020-00187-1 [link][preprint]
Yu, R., Li, Q., Fischer, C., Xu, D., & Doroudi, S. Predicting College Success: What Data Are Useful and for Whom? (2020). In Companion Proceedings of the 10th International Conference on Learning Analytics & Knowledge (LAK '20). [paper]
Rodriguez, F., Yu, R., Park, J., Rivas, M., Warschauer, M., & Sato, B. (2019). Utilizing Learning Analytics to Map Students’ Self-Reported Study Strategies to Click Behaviors in STEM Courses. In Proceedings of the 9th International Conference on Learning Analytics & Knowledge (LAK '19) (pp. 456-460). Tempe, AZ, USA. [link][paper]
Yu, R., Pardos, Z., & Scott, J. (2019). Student Behavioral Embeddings and Their Relationship to Outcomes in a Collaborative Online Course. In Learning Analytics: Building Bridges Between the Education and the Computing Communities Workshop at the 12th International Conference on Educational Data Mining (EDM '19). Montreal, QC, Canada. [paper]
Yan, W., Dowell, N. M., Holman, C., Welsh, S., Cho, H., & Brooks, C. (2019). Exploring learner engagement patterns in Teach-Outs: Using topic, sentiment and on-topicness to reflect on pedagogy. In Proceedings of the 9th International Conference for Learning Analytics & Knowledge (pp. 180-184). [link][preprint]
Glick, D., Cohen, A., Festinger, E., Xu, D., Li, Q., & Warschauer, M. (2019). Predicting Success, Preventing Failure: Using Learning Analytics to Examine the Strongest Predictors of Persistence and Performance in an Online English Language Course. In Utilizing Learning Analytics to Support Study Success (pp. 249-273). Springer, Cham. [link]
Joksimović, S., Dowell, N. M., Gašević, D., Mirriahi, N., Dawson, S., & Graesser, A. C. (2018). Linguistic characteristics of reflective states in video annotations under different instructional conditions. Computers in Human Behavior, 96, 211-222. [link][preprint]
Park, J., Yu, R., Rodriguez, F., Baker, R., Smyth, P., & Warschauer, M. (2018). Understanding student procrastination via mixture models. In Proceedings of the 11th International Conference on Educational Data Mining (pp. 187-197). Buffalo, NY. (Best Paper Award) [paper]
Yu, R., Jiang, D., & Warschauer, M. (2018). Representing and Predicting Student Navigational Pathways in Online College Courses. In Proceedings of the 5th ACM Conference on Learning at Scale. London, United Kingdom. [paper]
Dowell, N. M., Nixon, T., & Graesser, A. C. (2018). Group communication analysis: A computational linguistics approach for detecting socio-cognitive roles in multi-party interactions. Behavior Research Methods, 51(3),1007-1041. [link][paper]
Joksimović, S., Poquet, O., Kovanović, V., Dowell, N. M., Mills, C., Gašević, D., Dawson, S., Graesser, A., & Brooks, C. (2017). How do we model learning at scale? A systematic review of research on MOOCs. Review of Educational Research, 88(1), 43-86. [link][paper]
Park, J., Denaro, K., Rodriguez, F., Smyth, P., & Warschauer, M. (2017). Detecting changes in student behavior from clickstream data. In Proceedings of the seventh International Learning Analytics and Knowledge Conference (pp. 21-30). Vancouver, BC, Canada. (Best Paper Honorable Mention) [paper]
Cai, Z., Eagen, B., Dowell, N. M., Pennebaker, J. W., & Graesser, A. C. (2017). Epistemic network analysis and topic modeling for chat data from collaborative learning environments. In Proceeding of the 10th International Conference on Educational Data Mining (pp. 104-111). Wuhan, China. [link][paper]
Dowell, N. M., Brooks, C., Kovanović, V., Joksimović, S., & Gašević, D. (2017). The changing patterns of MOOC discourse. In Proceedings of the Fourth ACM Conference on Learning @ Scale (pp. 283-286). Cambridge, MA. [link][paper]
Dowell, N. M., Graesser, A. C., & 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. [link][paper]
Yu, R., Li, Q., Fischer, C., Doroudi, S. & Xu, D. (2020). Towards Accurate and Fair Prediction of College Success: Evaluating Different Sources of Student Data. Proceedings of the 13th International Conference on Educational Data Mining (EDM). Ifrane, Morocco. [link]
Li, Q., Baker, R., & Warschauer, M. (2020). Using clickstream data to measure, understand, and support self-regulated learning in online courses. The Internet and Higher Education, 45, 100727. [link][preprint]
Fischer, C., Pardos, Z., Baker, R. S., Williams., J. J., Smyth, P., Yu, R., Slater, S., Baker, R., & Warschauer, M. (2020). Mining big data in education: Affordances and challenges. Review of Research in Education, 45(1). [link][preprint]
Baker, R., Xu, D., Park, J., Yu, R., Li, Q., Cung, B., Fischer, C., Rodriguez, F., Warschauer, M. & Smyth, P. (2020). The Benefits and Caveats of Using Clickstream Data to Understand Student Self-Regulatory Behaviors: Opening the Black Box of Learning Processes. International Journal of Educational Technology in Higher Education, 17(13), 1-24. doi 10.1186/s41239-020-00187-1 [link][preprint]
Yu, R., Li, Q., Fischer, C., Xu, D., & Doroudi, S. Predicting College Success: What Data Are Useful and for Whom? (2020). In Companion Proceedings of the 10th International Conference on Learning Analytics & Knowledge (LAK '20). [paper]
Rodriguez, F., Yu, R., Park, J., Rivas, M., Warschauer, M., & Sato, B. (2019). Utilizing Learning Analytics to Map Students’ Self-Reported Study Strategies to Click Behaviors in STEM Courses. In Proceedings of the 9th International Conference on Learning Analytics & Knowledge (LAK '19) (pp. 456-460). Tempe, AZ, USA. [link][paper]
Yu, R., Pardos, Z., & Scott, J. (2019). Student Behavioral Embeddings and Their Relationship to Outcomes in a Collaborative Online Course. In Learning Analytics: Building Bridges Between the Education and the Computing Communities Workshop at the 12th International Conference on Educational Data Mining (EDM '19). Montreal, QC, Canada. [paper]
Yan, W., Dowell, N. M., Holman, C., Welsh, S., Cho, H., & Brooks, C. (2019). Exploring learner engagement patterns in Teach-Outs: Using topic, sentiment and on-topicness to reflect on pedagogy. In Proceedings of the 9th International Conference for Learning Analytics & Knowledge (pp. 180-184). [link][preprint]
Glick, D., Cohen, A., Festinger, E., Xu, D., Li, Q., & Warschauer, M. (2019). Predicting Success, Preventing Failure: Using Learning Analytics to Examine the Strongest Predictors of Persistence and Performance in an Online English Language Course. In Utilizing Learning Analytics to Support Study Success (pp. 249-273). Springer, Cham. [link]
Joksimović, S., Dowell, N. M., Gašević, D., Mirriahi, N., Dawson, S., & Graesser, A. C. (2018). Linguistic characteristics of reflective states in video annotations under different instructional conditions. Computers in Human Behavior, 96, 211-222. [link][preprint]
Park, J., Yu, R., Rodriguez, F., Baker, R., Smyth, P., & Warschauer, M. (2018). Understanding student procrastination via mixture models. In Proceedings of the 11th International Conference on Educational Data Mining (pp. 187-197). Buffalo, NY. (Best Paper Award) [paper]
Yu, R., Jiang, D., & Warschauer, M. (2018). Representing and Predicting Student Navigational Pathways in Online College Courses. In Proceedings of the 5th ACM Conference on Learning at Scale. London, United Kingdom. [paper]
Dowell, N. M., Nixon, T., & Graesser, A. C. (2018). Group communication analysis: A computational linguistics approach for detecting socio-cognitive roles in multi-party interactions. Behavior Research Methods, 51(3),1007-1041. [link][paper]
Joksimović, S., Poquet, O., Kovanović, V., Dowell, N. M., Mills, C., Gašević, D., Dawson, S., Graesser, A., & Brooks, C. (2017). How do we model learning at scale? A systematic review of research on MOOCs. Review of Educational Research, 88(1), 43-86. [link][paper]
Park, J., Denaro, K., Rodriguez, F., Smyth, P., & Warschauer, M. (2017). Detecting changes in student behavior from clickstream data. In Proceedings of the seventh International Learning Analytics and Knowledge Conference (pp. 21-30). Vancouver, BC, Canada. (Best Paper Honorable Mention) [paper]
Cai, Z., Eagen, B., Dowell, N. M., Pennebaker, J. W., & Graesser, A. C. (2017). Epistemic network analysis and topic modeling for chat data from collaborative learning environments. In Proceeding of the 10th International Conference on Educational Data Mining (pp. 104-111). Wuhan, China. [link][paper]
Dowell, N. M., Brooks, C., Kovanović, V., Joksimović, S., & Gašević, D. (2017). The changing patterns of MOOC discourse. In Proceedings of the Fourth ACM Conference on Learning @ Scale (pp. 283-286). Cambridge, MA. [link][paper]
Dowell, N. M., Graesser, A. C., & 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. [link][paper]