Using Ranking and Multiple Linear Regression to Explore the Impact of Social Media Engagement on Student Performance

Abstract

Investigating learning performance predictors is an important part of the educational process. Active participation or engagement is one such predictor, which has been widely analyzed in traditional learning settings, but less in the emerging social-media based learning environments. This paper explores the relationship between students' active participation on three social media tools (wiki, blog, microblogging tool) and their academic performance, in the context of a project-based learning scenario. Two cohorts, with a total of 119 students, are included in the study. Multiple linear regression is used to build easily interpretable models, which explain the final grade in terms of social media activity. Results indicate that engagement with social media tools is a good predictor of the student performance. More specifically, the models show that several features have an influence on the grade, such as: the frequency of blog posts, the average length of the tweets, the number of wiki page revisions, the average number of revisions for each distinct wiki page, the number of blog comments, the number of wiki file uploads.