Tracking student behavior, persistence, and achievement in online courses

By L. Morris, C. Finnegan, Sz-Shyan Wu. Internet and Higher Education, 2005

450 citations recorded by the source corpus.

Subfields: Computer Science Applications, Education

View Paper: https://www.semanticscholar.org/paper/69611d34564e116121e960361a92b4c11da3aa08

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In Computer Science Applications

In Education

Citations in the corpus

Factors Associated With Student Persistence in an Online Program of Study: A Review of the Literature (2012, background): “Harrell and Bower (2011) and Morris, Finnegan, and Wu (2005) report grade point average (GPA) as significantly predictive of student persistence.”
Multimedia tools in the teaching and learning processes: A systematic review (2020, background): “Earlier attempts at analysing student behaviour while engaging with online material included analysing student access computer logs, and the frequency of participation and duration of participation (Morris et al., 2005).”
Mining LMS data to develop an "early warning system" for educators: A proof of concept (2010, background): “As previously highlighted by Morris et al. (2005), this research does to some extent appear to be ''documenting the obvious " (p. 229).”
A review of online course dropout research: implications for practice and future research (2011, background): “…(Frydeberg 2007) • Tutorial attendance (Cheung and Kan 2002) Interactions • Inter-student interaction (Pigliapoco and Bogliolo 2008) and (Tello 2007) • Faculty interaction with students (Bocchi et al. 2004) and (Ivankova and Stick 2007) • Student participation (Morris et al. 2005a) dropout rates.”
Attrition and Achievement Gaps in Online Learning (2015, background): “An analysis of student behavior on an online education platform showed that 31% of variation in achievement could be accounted for by a small set of participation measures [16].”
Predicting Student Performance from LMS Data: A Comparison of 17 Blended Courses Using Moodle LMS (2017, methodology): “Discussion forum posts was the only predictor in both final prediction models of [27] and [28].”