Personalizing online educational tools

Michael J. Lee, Bruce Ferwerda

Research output: Chapter in Book/Report/Conference proceedingConference contribution

20 Scopus citations

Abstract

As more people turn to online resources to learn, there will be an increasing need for systems to understand and adapt to the needs of their users. Engagement is an important aspect to keep users committed to learning. Learning approaches for online systems can benefit from personalization to engage their users. However, many approaches for personalization currently rely on methods (e.g., historical behavioral data, questionnaires, quizzes) that are unable to provide a personalized experience from the start-of-use of a system. As users in a learning environment are exposed to new content, the first impression that they receive from the system influences their commitment with the program. In this position paper we propose a quantitative approach for personalization in online learning environments to overcome current problems for personalization in such environments.

Original languageEnglish (US)
Title of host publicationHUMANIZE 2017 - Proceedings of the 2017 ACM Workshop on Theory-Informed User Modeling for Tailoring and Personalizing Interfaces, co-located with IUI 2017
PublisherAssociation for Computing Machinery, Inc
Pages27-30
Number of pages4
ISBN (Electronic)9781450349055
DOIs
StatePublished - Mar 13 2017
Event1st ACM Workshop on Theory-Informed User Modeling for Tailoring and Personalizing Interfaces, HUMANIZE 2017 - Limassol, Cyprus
Duration: Mar 13 2017 → …

Publication series

NameHUMANIZE 2017 - Proceedings of the 2017 ACM Workshop on Theory-Informed User Modeling for Tailoring and Personalizing Interfaces, co-located with IUI 2017

Other

Other1st ACM Workshop on Theory-Informed User Modeling for Tailoring and Personalizing Interfaces, HUMANIZE 2017
Country/TerritoryCyprus
CityLimassol
Period3/13/17 → …

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems and Management
  • Human-Computer Interaction

Keywords

  • Adaptive learning
  • Engagement
  • Intelligent tutoring systems
  • Learning styles

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