Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success

By Dragan Gasevic, Shane Dawson, Tim Rogers, Danijela Gasevic. Internet and Higher Education, 2016

663 citations recorded by the source corpus.

Abstract This study examined the extent to which instructional conditions influence the prediction of academic success in nine undergraduate courses offered in a blended learning model (n = 4134). The study illustrates the differences in predictive power and significant predictors between course-specific models and generalized predictive models. The results suggest that it is imperative for learning analytics research to account for the diverse ways technology is adopted and applied in course-specific contexts. The differences in technology use, especially those related to whether and how learners use the learning management system, require consideration before the log-data can be merged to create a generalized model for predicting academic success. A lack of attention to instructional conditions can lead to an over or under estimation of the effects of LMS features on students' academic success. These findings have broader implications for institutions seeking generalized and portable models for identifying students at risk of academic failure.

Subfields: Artificial Intelligence, Computer Science Applications, Education

View Paper: https://www.semanticscholar.org/paper/2b43caf05d86d45401e67a6c502c5dfda16bdc54

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Citations in the corpus

Predicting Student Performance from LMS Data: A Comparison of 17 Blended Courses Using Moodle LMS (2017, methodology): “2 910 Number of resources viewed [10], [32], [33] 2,277 7.”
Practical and ethical challenges of large language models in education: A systematic scoping review (2023, background): “Researchers need to carefully examine the extent of generalisability of their innovations and inform the limitations to stakeholders (Gašević et al., 2016).”
Learning Analytics for Learning Design: A Systematic Literature Review of Analytics-Driven Design to Enhance Learning (2019, background): “However, recent studies advocate that learning analytics should not promote ”one-size-fits-all” research due to the fact that students’ individual differences have strong implications on the learning behavior, motivation, and engagement [15], [16].”