Learning teaching strategies in an Adaptive and Intelligent Educational System through Reinforcement Learning
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Recent approaches have applied ML techniques such as multi-armed bandit techniques to adaptively personalize sequences of learning activities, LSTMs to generate questions to evaluate language comprehension, and RL to improve the strategies used within the ITS.
“While ITSs have traditionally been defined and constructed by hand, recent approaches have applied ML techniques such as multi-armed bandit techniques to adaptively personalize sequences of learning activities [135], LSTMs to generate questions to evaluate language comprehension [205], and RL to improve the strategies used within the ITS [365,441].”
From Tackling climate change with machine learning (2023) · cites this paper as [365]
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- Authors
- Iglesias, A., Martínez, P., Aler, R., & Fernández, F.
- Affiliations
- Universidad Carlos III de Madrid (ES)
- Corresponding author
- Iglesias, Ana
- Venue
- Applied Intelligence
- Published
- 2008-02-16
- Volume
- 31
- Issue
- 1
- Pages
- 89-106
- ISSN
- 0924-669X (ISSN-L)
- 1573-7497
- OpenAlex
- W1971681797
- Cited by
- 96
- References
- 46
- Citations by year
- 2026: 6 · 2025: 7 · 2024: 6 · 2023: 3 · 2022: 8 · 2021: 9 · 2020: 6 · 2019: 5 · 2018: 6 · 2017: 3 · 2016: 4 · 2015: 3 · 2014: 4 · 2013: 5 · 2012: 3
- FWCI
- 9.52
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- other-oa
- www.springer.com/tdm (tdm)
- Locations
- Applied Intelligence (publishedVersion)
- e-Archivo (Carlos III University of Madrid) (acceptedVersion, other-oa)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Keywords
- reinforcement learning, pedagogical policy, teaching methods, exploration-exploitation balance, learning convergence, personalized tutoring
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