Applying classification techniques on temporal trace data for shaping student behavior models

Author:

Papamitsiou Zacharoula1,Karapistoli Eirini1,Economides Anastasios A.1

Affiliation:

1. University of Macedonia, Thessaloniki, Greece

Publisher

ACM Press

Reference31 articles.

1. Aramo-Immonen, H., Jussila, J., Huhtamäki, J. 2015. Exploring co-learning behavior of conference participants with visual network analysis of Twitter data,Comput Human Behav, 51, 1154--1162.

2. Agudo-Peregrina, Á. F., Iglesias-Pradas, S., Conde-González, M. Á., Hernández-García, Á. 2015. Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning,Comput Human Behav, 31, 542--550.

3. Kovanović, V., Gašević, D., Joksimović, S., Hatala, M., Adesope, O. 2015. Analytics of communities of inquiry: Effects of learning technology use on cognitive presence in asynchronous online discussions,Internet High Educ, 27, 74--89.

4. Tempelaar, D. T., Rienties, B., & Giesbers, B. 2014. In search for the most informative data for feedback generation: Learning analytics in a data-rich context,Comput Human Behav, 47, 157--167.

5. van Leeuwen, A., Janssen, J., Erkens, G., Brekelmans, M. 2015. Teacher regulation of cognitive activities during student collaboration: Effects of learning analytics,Comput Educ, 90, 80--94.

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