LEARNING STYLES BASED ADAPTIVE INTELLIGENT TUTORING SYSTEMS: DOCUMENT ANALYSIS OF ARTICLES PUBLISHED BETWEEN 2001. AND 2016.

  • Amit Kumar, M.tech School of Computer Science and Engineering, University of Petroleum and Energy Studies, Dehradun, Uttarakhand
  • Ninni Singh, M.tech School of Computer Science and Engineering, University of Petroleum and Energy Studies, Dehradun, Uttarakhand
  • Neelu Jyothi Ahuja, Dr. School of Computer Science and Engineering, University of Petroleum and Energy Studies, Dehradun, Uttarakhand

Abstract

Actualizing instructional intercessions to suit learner contrasts has gotten extensive consideration. Among these individual contrast factors, the observational confirmation in regards to the academic benefit of learning styles has been addressed, yet the examination on the issue proceeds. Late improvements in web-based executions have driven researchers to re-examine the learning styles in adaptive tutoring frameworks. Adaptivity in intelligent tutoring systems is strongly influenced by the learning style of a learner. This study involved extensive document analysis of adaptive tutoring systems based on learning styles. Seventy-eight studies in literature from 2001 to 2016 were collected and classified under select parameters such as main focus, purpose, research types, methods, types and levels of participants, field/area of application, learner modelling, data gathering tools used and research findings. The current studies reveal that majority of the studies defined a framework or architecture of adaptive intelligent tutoring system (AITS) while others focused on impact of AITS on learner satisfaction and academic outcomes. Currents trends, gaps in literature and ications were discussed.

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Published
2017-12-19
How to Cite
KUMAR, Amit; SINGH, Ninni; AHUJA, Neelu Jyothi. LEARNING STYLES BASED ADAPTIVE INTELLIGENT TUTORING SYSTEMS: DOCUMENT ANALYSIS OF ARTICLES PUBLISHED BETWEEN 2001. AND 2016.. International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), [S.l.], v. 5, n. 2, p. 83-98, dec. 2017. ISSN 2334-8496. Available at: <http://www.ijcrsee.com/index.php/IJCRSEE/article/view/168>. Date accessed: 19 jan. 2018. doi: https://doi.org/10.5937/IJCRSEE1702083K.