What if learning analytics were based on learning science?


  • Zahia Marzouk Simon Fraser University
  • Mladen Rakovic Simon Fraser University
  • Amna Liaqat Simon Fraser University
  • Jovita Vytasek Simon Fraser University
  • Donya Samadi Simon Fraser University
  • Jason Stewart-Alonso Simon Fraser University
  • Ilana Ram Simon Fraser University
  • Sonya Woloshen Simon Fraser University
  • Philip H Winne Simon Fraser University
  • John C Nesbit Simon Fraser University




self-regulated learning, metacognition, motivation, self-determination, learning analytics


Learning analytics are often formatted as visualisations developed from traced data collected as students study in online learning environments. Optimal analytics inform and motivate students’ decisions about adaptations that improve their learning. We observe that designs for learning often neglect theories and empirical findings in learning science that explain how students learn. We present six learning analytics that reflect what is known in six areas (we call them cases) of theory and research findings in the learning sciences: setting goals and monitoring progress, distributed practice, retrieval practice, prior knowledge for reading, comparative evaluation of writing, and collaborative learning. Our designs demonstrate learning analytics can be grounded in research on self-regulated learning and self-determination. We propose designs for learning analytics in general should guide students toward more effective self-regulated learning and promote motivation through perceptions of autonomy, competence, and relatedness.


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How to Cite

Marzouk, Z., Rakovic, M., Liaqat, A., Vytasek, J., Samadi, D., Stewart-Alonso, J., Ram, I., Woloshen, S., Winne, P. H., & Nesbit, J. C. (2016). What if learning analytics were based on learning science?. Australasian Journal of Educational Technology, 32(6). https://doi.org/10.14742/ajet.3058