Learning engagement, learning outcomes and learning gains: lessons from LA

Dirk Tempelaar, Bart Rienties, Quan Nguyen

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review


Learning analytic models are built upon traces students leave in technology-enhanced learning platforms as the digital footprints of their learning processes. Learning analytics uses these traces of learning engagement to predict performance and provide learning feedback to students and teachers when these predictions signal the risk of failing a course, or even dropping-out. But not all of these trace variables act as stable and reliable predictors of course performance. In previous research, the authors concluded that trace variables of product type, such as mastery, do a better job than trace variables of process type, such as clicks or time-on-task, in predicting performance. In this study, we extend this analysis by focusing on learning gains rather than learning outcomes as the most important performance dimension. Distinguishing two different levels of initial proficiency, our empirical analysis into the learning of mathematics by first-year university students indicates that the lack of stability of the engagement types of process type is mainly explained by learning pattern found in students of high initial proficiency. For these students, high levels of engagement lead to lower, rather than higher, predicted learning outcomes. Amongst students with lower initial proficiency, higher levels of engagement play a different role.
Original languageEnglish
Title of host publicationProceedings of the 16th International Conference on Cognition and Exploratory Learning in the Digital Age (CELDA 2019)
EditorsDemetrios G. Sampson, Dirk Ifenthaler, Pedro Isaías, Maria Lidia Mascia
Place of PublicationLisbon
PublisherIADIS Press
Number of pages8
ISBN (Print)978-989-8533-93-7
Publication statusPublished - Nov 2019


  • Blended learning
  • E-tutorials
  • Learning analytics
  • learning engagement
  • learning traces
  • process and product data


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