A multi-modal study into students’ timing and learning regulation: time is ticking

Dirk Tempelaar*, Bart Rienties, Quan Nguyen

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


This empirical study aims to demonstrate how the combination of trace data derived from technology-enhanced learning environments and self-response survey data can contribute to the investigation of self-regulated learning processes.

Using a showcase based on 1,027 students’ learning in a blended introductory quantitative course, the authors analysed the learning regulation and especially the timing of learning by trace data. Next, the authors connected these learning patterns with self-reports based on multiple contemporary social-cognitive theories.

The authors found that several behavioural facets of maladaptive learning orientations, such as lack of regulation, self-sabotage or disengagement negatively impacted the amount of practising, as well as timely practising. On the adaptive side of learning dispositions, the picture was less clear. Where some adaptive dispositions, such as the willingness to invest efforts in learning and self-perceived planning skills, positively impacted learning regulation and timing of learning, other dispositions such as valuing school or academic buoyancy lacked the expected positive effects.

Research limitations/implications
Due to the blended design, there is a strong asymmetry between what one can observe on learning in both modes.

Practical implications
This study demonstrates that in a blended setup, one needs to distinguish the grand effect on learning from the partial effect on learning in the digital mode: the most adaptive students might be less dependent for their learning on the use of the digital learning mode.

The paper presents an application of embodied motivation in the context of blended learning.
Original languageEnglish
Article number2
Pages (from-to)298-313
Number of pages16
JournalInteractive Technology and Smart Education
Issue number4
Publication statusPublished - Dec 2018


  • Blended learning
  • Dispositional learning analytics
  • E-tutorials
  • Learning dispositions
  • Learning regulation
  • Learning timing

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