Learning analytics: Supporting at-risk student through eye-Tracking and a robust intelligent tutoring system

Alexander Muriuki Njeru, Samiullah Paracha

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

7 Citations (Scopus)

Abstract

Learning difficulties among graduate students have always been greatest challenge. Learning analytics offers an evidence-based problem solving approach. In this context, eye-Tracking is a profound technology that captures real-Time eye gaze data of learners. This paper proposes a robust intelligent tutoring system based on the eye tracking data of learners. The system offers adaptive learning by providing customized feedback to learners. Learner-centered design approach has been followed throughout the development process. Measuring success, evaluation will be performed with real learners.

Original languageEnglish
Title of host publicationProceedings of the 2017 IEEE International Conference on Applied System Innovation
Subtitle of host publicationApplied System Innovation for Modern Technology, ICASI 2017
EditorsTeen-Hang Meen, Artde Donald Kin-Tak Lam, Stephen D. Prior
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1002-1005
Number of pages4
ISBN (Electronic)9781509048977
DOIs
Publication statusPublished - 21 Jul 2017
Externally publishedYes
Event2017 IEEE International Conference on Applied System Innovation, ICASI 2017 - Sapporo, Japan
Duration: 13 May 201717 May 2017

Publication series

NameProceedings of the 2017 IEEE International Conference on Applied System Innovation: Applied System Innovation for Modern Technology, ICASI 2017

Conference

Conference2017 IEEE International Conference on Applied System Innovation, ICASI 2017
Country/TerritoryJapan
CitySapporo
Period13/05/1717/05/17

Keywords

  • E-learning
  • Intelligent Tutoring System
  • Learner-Centered Approach
  • Learning Analytics

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