What if learning analytics were based on learning science?

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

Research output: Contribution to journalArticleResearchpeer-review

57 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1-18
Number of pages18
JournalAustralasian Journal of Educational Technology
Volume32
Issue number6
DOIs
Publication statusPublished - 15 Dec 2016
Externally publishedYes

Keywords

  • Learning Analytics
  • Learning sciences
  • Human-Computer Interaction

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