What learning analytics based prediction models tell us about feedback preferences of students

Nguyen, Quan; Tempelaar, Dirk; Rienties, Bart and Giesbers, Bas (2016). What learning analytics based prediction models tell us about feedback preferences of students. Quarterly Review of Distance Education, 17(3) pp. 13–33.

Abstract

Learning analytics (LA) seeks to enhance learning processes through systematic measurements of learning related data and to provide informative feedback to learners and educators (Siemens & Long, 2011). This study examined the use of preferred feedback modes in students by using a dispositional learning analytics framework, combining learning disposition data with data extracted from digital systems. We analyzed the use of feedback of 1062 students taking an introductory mathematics and statistics course, enhanced with digital tools. Our findings indicated that compared with hints, fully worked-out solutions demonstrated a stronger effect on academic performance and acted as a better mediator between learning dispositions and academic performance. This study demonstrated how e-learners and their data can be effectively re-deployed to provide meaningful insights to both educators and learners.

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About

  • Item ORO ID
  • 47700
  • Item Type
  • Journal Item
  • ISSN
  • 1528-3518
  • Keywords
  • blended learning; dispositional learning analytics; e-tutorials; learning feedback; learning dispositions; higher education; problem solving; STEM
  • Academic Unit or School
  • Institute of Educational Technology (IET)
  • Copyright Holders
  • © 2016 The Author(s)
  • Depositing User
  • Quan Nguyen
CORE (COnnecting REpositories)

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