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oai:uvadoc.uva.es:10324/248902021-06-23T13:29:26Zcom_10324_1191com_10324_931com_10324_894col_10324_1381

Predicting the decrease of engagement indicators in a MOOC Bote Lorenzo, Miguel Luis Gómez Sánchez, Eduardo Predicting the decrease of students’ engagement in typical MOOC tasks such as watching lecture videos or submitting assignments is key to trigger timely interventions in order to try to avoid the disengagement before it takes place. This paper proposes an approach to build the necessary predictive models using students’ data that becomes available during a course. The approach was employed in an experimental study to predict the decrease of three different engagement indicators in a MOOC. The results suggest its feasibility with values of area under the curve for different predictors ranging from 0.718 to 0.914. 2017-08-17T11:09:40Z 2017-08-17T11:09:40Z 2017-08-17T11:09:40Z 2017 info:eu-repo/semantics/conferenceObject Bote-Lorenzo, M.L., Gómez-Sánchez, E. Predicting the decrease of engagement indicators in a MOOC. Proceedings of the 7th International Conference on Learning Analytics and Knowledge, LAK 2017, 143-147, Vancouver, Canada, March 2017 http://uvadoc.uva.es/handle/10324/24890 10.1145/3027385.3027387 eng http://lak17.solaresearch.org info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-nd/4.0/ Attribution-NonCommercial-NoDerivatives 4.0 International Association for Computing Machinery (ACM)