Abstract
Social networks based on mutual interest, affinity or leadership are spontaneously generated when the training activities are carried out through online learning systems wherein collaboration and interaction among participants is encouraged. The bare structure of those interactions, reflected in a network graph, is known to contain relevant statistical information about the dynamics of the learning process within the group, thus it should be possible to extract such knowledge and exploit it either for improving the quality of the learning outcomes or for driving the educational process toward the desired goals. However, discovering the features and structural properties in the social network graph which enclose the maximum and most valuable information for educational purposes requires a careful analysis and identification of the deep connections between social graphs and students’ academic performance. In this chapter, we address a systematic study on the strength and statistical significance of a number of correlations between the underlying graphs triggered by the online learning activities and the prediction of the student’s achievements. Several structural features of networks are identified as the ones with larger impact on the effectiveness of the estimators. Our data source is a complete record of online student activity collected over two academic years both at the undergraduate and the graduate level.
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Ferreira-Pires, O., Sousa-Vieira, M.E., López-Ardao, J.C., Fernández-Veiga, M. (2020). Studying Relationships Between Network Structure in Educational Forums and Students’ Performance. In: Lane, H.C., Zvacek, S., Uhomoibhi, J. (eds) Computer Supported Education. CSEDU 2019. Communications in Computer and Information Science, vol 1220. Springer, Cham. https://doi.org/10.1007/978-3-030-58459-7_7
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