Abstract
This study investigates the degree to which textual complexity indices applied on students’ online contributions, corroborated with a longitudinal analysis performed on their weekly posts, predict academic performance. The source of student writing consists of blog and microblog posts, created in the context of a project-based learning scenario run on our eMUSE platform. Data is collected from six student cohorts, from six consecutive installments of the Web Applications Design course, comprising of 343 students. A significant model was obtained by relying on the textual complexity and longitudinal analysis indices, applied on the English contributions of 148 students that were actively involved in the undertaken projects.
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Acknowledgments
This work was supported by the FP7 208-212578 LTfLL project, the 644187 EC H2020 RAGE project, and a grant of the Romanian National Authority for Scientific Research and Innovation, CNCS – UEFISCDI, project number PN-II-RU-TE-2014-4-2604.
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Dascalu, M., Popescu, E., Becheru, A., Crossley, S., Trausan-Matu, S. (2016). Predicting Academic Performance Based on Students’ Blog and Microblog Posts. In: Verbert, K., Sharples, M., Klobučar, T. (eds) Adaptive and Adaptable Learning. EC-TEL 2016. Lecture Notes in Computer Science(), vol 9891. Springer, Cham. https://doi.org/10.1007/978-3-319-45153-4_29
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DOI: https://doi.org/10.1007/978-3-319-45153-4_29
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