An adaptation algorithm for an intelligent natural language tutoring system
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Learning styles have been among the broadly chosen components in previous studies as a reference for adapting learning.
“Learning styles (Çakıroğlu, 2014;Klašnja-Milićević et al., 2011;Latham, Crockett and McLean, 2014) have been among the broadly chosen components in previous studies as a reference for adapting learning.”
From A systematic literature review of personalized learning terms (2020) · cites this paper as Latham, Crockett and McLean, 2014
Cites
Learning Styles and Pedagogy in post-16 learning: a systematic and critical review
Coffield, F., Moseley, D., Hall, E., & Ecclestone, K. (2004) · Newcastle University · doi:10.25405/ncl.34085991 · Cited by 1,810 · see citing works
Chi, M. T. H., Siler, S. A., Jeong, H., Yamauchi, T., & Hausmann, R. G. (2001) · Cognitive Science · doi:10.1207/s15516709cog2504_1 · Cited by 855 · see citing works
Sentence similarity based on semantic nets and corpus statistics
Li, Y., McLean, D., Bandar, Z. A., O'Shea, J. D., & Crockett, K. (2006) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2006.130 · Cited by 819 · see citing works
Self-citationGeneration of allocation sequences in randomised trials: chance, not choice
Schulz, K. F., & Grimes, D. A. (2002) · The Lancet · doi:10.1016/s0140-6736(02)07683-3 · Cited by 774 · see citing works
E-Learning personalization based on hybrid recommendation strategy and learning style identification
Klašnja-Milićević, A., Vesin, B., Ivanović, M., & Budimac, Z. (2011) · Computers & Education · doi:10.1016/j.compedu.2010.11.001 · Cited by 580 · see citing works
Akbulut, Y., & Cardak, C. S. (2012) · Computers & Education · doi:10.1016/j.compedu.2011.10.008 · Cited by 269 · see citing works
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A systematic literature review of personalized learning terms
Shemshack, A., & Spector, J. M. (2020) · Smart Learning Environments · doi:10.1186/s40561-020-00140-9 · Cited by 539 · see citing works
“Learning styles (Çak ı ro ğ lu, 2014; Kla š nja-Mili ć evi ć et al., 2011; Latham, Crockett and McLean, 2014) have been among the broadly chosen components in previous studies as a reference for adapting learning.”
BackgroundA review of machine learning methods used for educational data
Ersozlu, Z., Taheri, S., & Koch, I. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12704-0 · Cited by 96 · see citing works
“Researchers have employed ML methods to predict student dropout rates (Romero et al, 2008 : Latham et al., 2014 ).”
Howard, C., Jordan, P., Di Eugenio, B., & Katz, S. (2017) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-015-0071-y · Cited by 51 · see citing works
- Authors
- Latham, A., Crockett, K., & McLean, D.
- Affiliations
- Manchester Metropolitan University (GB)
- Corresponding author
- Latham, Annabel
- Venue
- Computers & Education
- Publisher
- Elsevier BV
- Published
- 2014-02
- Volume
- 71
- Pages
- 97-110
- ISSN
- 0360-1315 (ISSN-L)
- OpenAlex
- W2060412612
- Cited by
- 113
- References
- 6
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 5 · 2023: 10 · 2022: 6 · 2021: 8 · 2020: 16 · 2019: 9 · 2018: 5 · 2017: 14 · 2016: 9 · 2015: 10 · 2014: 11
- FWCI
- 10.33
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computers & Education (publishedVersion)
- e-space (Manchester Metropolitan University) (acceptedVersion)
- Funders
- Engineering and Physical Sciences Research Council
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Psychology › Developmental and Educational Psychology › Learning Styles and Cognitive Differences
- Physical Sciences › Computer Science › Artificial Intelligence › Edcuational Technology Systems
- Keywords
- intelligent tutoring systems, adaptive tutoring, learning style adaptation, Felder-Silverman learning styles, natural language interfaces, conversational tutoring, personalized learning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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