Generating actionable predictive models of academic performance

Abelardo Pardo, Jelena Jovanovic, Negin Mirriahi, Shane Dawson, Roberto Martinez-Maldonado, Dragan Gaševic

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

25 Citations (Scopus)

Abstract

The pervasive collection of data has opened the possibility for educational institutions to use analytics methods to improve the quality of the student experience. However, the adoption of these methods faces multiple challenges particularly at the course level where instructors and students would derive the most benefit from the use of analytics and predictive models. The challenge lies in the knowledge gap between how the data is captured, processed and used to derive models of student behavior, and the subsequent interpretation and the decision to deploy pedagogical actions and interventions by instructors. Simply put, the provision of learning analytics alone has not necessarily led to changing teaching practices. In order to support pedagogical change and aid interpretation, this paper proposes a model that can enable instructors to readily identify subpopulations of students to provide specific support actions. The approach was applied to a first year course with a large number of students. The resulting model classifies students according to their predicted exam scores, based on indicators directly derived from the learning design.

Original languageEnglish
Title of host publicationLAK '16 Conference Proceedings
Subtitle of host publicationThe Sixth International Learning Analytics & Knowledge Conference: Enhancing Impact: Convergence of Communities for Grounding, Implementation, and Validation
EditorsShane Dawson, Hendrik Drachsler, Carolyn Penstein Rosé
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages474-478
Number of pages5
Volume25-29-April-2016
ISBN (Electronic)9781450341905
DOIs
Publication statusPublished - 2016
Externally publishedYes
EventInternational Learning Analytics & Knowledge Conference 2016 - University of Edinburgh, Edinburgh, United Kingdom
Duration: 25 Apr 201629 Apr 2016
Conference number: 6th
http://lak16.solaresearch.org/

Conference

ConferenceInternational Learning Analytics & Knowledge Conference 2016
Abbreviated titleLAK 2016
Country/TerritoryUnited Kingdom
CityEdinburgh
Period25/04/1629/04/16
Internet address

Keywords

  • Feedback
  • Learning analytics
  • Personalization
  • Recursive partitioning

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