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

• Predictive models in learning analytics need to account for instructional conditions.
• Instructional conditions are based in the theory of self-regulated learning.
• The study was conducted with a nine undergraduate blended learning (n = 4139) courses.
• Generalized predictive models were not suitable to inform practice and research.
• Course specific models better detected variables of relevance for teaching practice.
• Further implications for educational research and practice are discussed.

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.