Assessing learning analytics impact on coding competence growth

David Boulanger, Jeremie Seanosky, Rebecca Guillot, Isabelle Guillot, Claudia Guillot, Shawn Fraser, Vivekanandan Kumar, Kinshuk

Research output: Chapter in Book/Report/Conference proceedingPublished Conference contributionpeer-review

2 Citations (Scopus)

Abstract

The literature reveals that the effectiveness of learning analytics (LA) tools is best evaluated with a mixed-method approach and not only with summative measurements such as grades. A recent study on the impact of an LA tool over academic performance did not substantiate its effect in improving grades for coding tasks despite qualitative comments of experimental group participants indicating that the tool was effective in improving coding competences in Java. In analyzing this viewpoint, this paper describes the impact of LA formative feedback on the growth of students' coding competences.

Original languageEnglish
Title of host publicationProceedings - IEEE 19th International Conference on Advanced Learning Technologies, ICALT 2019
EditorsMaiga Chang, Demetrios G Sampson, Ronghuai Huang, Alex Sandro Gomes, Nian-Shing Chen, Ig Ibert Bittencourt, Kinshuk Kinshuk, Diego Dermeval, Ibsen Mateus Bittencourt
Pages170-172
Number of pages3
ISBN (Electronic)9781728134857
DOIs
Publication statusPublished - Jul. 2019
Event19th IEEE International Conference on Advanced Learning Technologies, ICALT 2019 - Maceio, Brazil
Duration: 15 Jul. 201918 Jul. 2019

Publication series

NameProceedings - IEEE 19th International Conference on Advanced Learning Technologies, ICALT 2019

Conference

Conference19th IEEE International Conference on Advanced Learning Technologies, ICALT 2019
Country/TerritoryBrazil
CityMaceio
Period15/07/1918/07/19

Keywords

  • Analytics
  • Coding
  • Competence
  • Time series

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