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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CITATION STYLE
Dascalu, M., Popescu, E., Becheru, A., Crossley, S., & Trausan-Matu, S. (2016). Predicting academic performance based on students’ blog and microblog posts. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9891 LNCS, pp. 370–376). Springer Verlag. https://doi.org/10.1007/978-3-319-45153-4_29
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