Development and adoption of an adaptive learning system: reflections and lessons learned

Hassan Khosravi, Shazia Sadiq, Dragan Gasevic

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

66 Citations (Scopus)

Abstract

Adaptive learning systems (ALSs) aim to provide an efficient, effective and customised learning experience for students by dynamically adapting learning content to suit their individual abilities or preferences. Despite consistent evidence of their effectiveness and success in improving student learning over the past three decades, the actual impact and adoption of ALSs in education remain restricted to mostly research projects. In this paper, we provide a brief overview of reflections and lessons learned from developing and piloting an ALS in a course on relational databases. While our focus has been on adaptive learning, many of the presented lessons are also applicable to the development and adoption of educational tools and technologies in general. Our aim is to provide insight for other instructors, educational researchers and developers that are interested in adopting ALSs or are involved in the implementation of educational tools and technologies.

Original languageEnglish
Title of host publicationProceedings of the 51st ACM Technical Symposium on Computer Science Education
EditorsSarah Heckman, Pamela Cutter, Alvaro Monge
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages58-64
Number of pages7
ISBN (Electronic)9781450367936
DOIs
Publication statusPublished - 2020
EventACM Technical Symposium on Computer Science Education (SIGCSE) 2020 - Portland, United States of America
Duration: 11 Mar 202014 Mar 2020
Conference number: 51st
https://sigcse2020.sigcse.org
https://dl.acm.org/doi/proceedings/10.1145/3328778 (Proceedings)

Conference

ConferenceACM Technical Symposium on Computer Science Education (SIGCSE) 2020
Abbreviated titleSIGCSE 2020
Country/TerritoryUnited States of America
CityPortland
Period11/03/2014/03/20
Internet address

Keywords

  • Adaptive learning systems
  • Crowdsourcing
  • Educational technologies

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