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What if Learning Analytics Were Based on Learning Science?

Authors :
Marzouk, Zahia
Rakovic, Mladen
Liaqat, Amna
Vytasek, Jovita
Samadi, Donya
Stewart-Alonso, Jason
Ram, Ilana
Woloshen, Sonya
Winne, Philip H.
Nesbit, John C.
Source :
Australasian Journal of Educational Technology. 2016 32(6):1-18.
Publication Year :
2016

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.

Details

Language :
English
ISSN :
1449-5554
Volume :
32
Issue :
6
Database :
ERIC
Journal :
Australasian Journal of Educational Technology
Publication Type :
Academic Journal
Accession number :
EJ1123341
Document Type :
Journal Articles<br />Reports - Evaluative