On predicting learning styles in conversational intelligent tutoring systems using fuzzy decision trees
- Submitting institution
-
Manchester Metropolitan University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2334
- Type
- D - Journal article
- DOI
-
10.1016/j.ijhcs.2016.08.005
- Title of journal
- International Journal of Human-Computer Studies
- Article number
- -
- First page
- 98
- Volume
- 97
- Issue
- -
- ISSN
- 1071-5819
- Open access status
- Compliant
- Month of publication
- August
- Year of publication
- 2016
- URL
-
https://e-space.mmu.ac.uk/617098/
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
2
- Research group(s)
-
C - Machine Intelligence
- Citation count
- 32
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper overcomes the limitations of previous work by proposing a new approach that uses fuzzy decision tree models to improve conversational intelligent tutoring systems (CITS). The key contribution is improved profiling of an individual’s learning style, leading to better CITS adaptation and the provision of a more personalised learning experience 24/7. Real-world experimentation using 75-University students on an SQL tutorial yielded a 14% increase in learning style predictive accuracy.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -