The DOI matches its Crossref record; 92 of the 93 references it cites were found in a registry or an index.
Affect-aware tutors: recognising and responding to student affect
Abstract
Theories and technologies are needed to understand and integrate the knowledge of student affect (e.g., frustration, motivation and self-confidence) into learning models. Our goals are to redress the cognitive versus affective imbalance in teaching systems, develop tools that model student affect and build tutors that elicit, measure and respond to student affect. This article describes our broad approach towards this goal and our three main objectives: develop tools for affect recognition, interventions in response to student affect, and emotionally animated agents.
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In the citing paper
“As described in (Woolf et al., 2009), affect boosts can help to enhance a student's motivation to solve a particular learning task.”
From Affective learning: Improving engagement and enhancing learning with affect-aware feedback (2017) · cites this paper as Woolf et al., 2009
In the citing paper
“Woolf et al. (2009) show that a student's on task behaviour leads to higher post-test scores”
From Affective learning: Improving engagement and enhancing learning with affect-aware feedback (2017) · cites this paper as Woolf et al. (2009)
In the citing paper
“Scientific research proposes incorporating affect and behavioral state of the learner into the design of the tutoring system as it enhances the effectiveness of the teaching process (Woolf et al., 2009; San Pedro et al., 2013).”
From Proactive and reactive engagement of artificial intelligence methods for education: a review (2023) · cites this paper as Woolf et al., 2009
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Cites
(1978) · Medical Entomology and Zoology · Cited by 53,488
Self-efficacy: Toward a unifying theory of behavioral change
Bandura, A. (1977) · Psychological Review · doi:10.1037/0033-295x.84.2.191 · Cited by 43,925
Vygotsky, L. S. (1964) · Bulletin of the Orton Society · doi:10.1007/bf02928399 · Cited by 20,704
(1999) · Cited by 12,812
Flow: the psychology of optimal experience
(1990) · Choice Reviews Online · doi:10.5860/choice.28-0597 · Cited by 12,654
(1964) · Medical Entomology and Zoology · Cited by 10,439
A social-cognitive approach to motivation and personality.
Dweck, C. S., & Leggett, E. L. (1988) · Psychological Review · doi:10.1037/0033-295x.95.2.256 · Cited by 9,745
Motivational processes affecting learning.
Dweck, C. S. (1986) · American Psychologist · doi:10.1037/0003-066x.41.10.1040 · Cited by 6,971
Classrooms: Goals, structures, and student motivation.
Ames, C. (1992) · Journal of Educational Psychology · doi:10.1037/0022-0663.84.3.261 · Cited by 6,468
(1999) · Cited by 6,308
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Metadata
- Authors
- Woolf, B., Burleson, W., Arroyo, I., Dragon, T., Cooper, D., & Picard, R.
- Venue
- International Journal of Learning Technology
- Publisher
- Inderscience Publishers
- Published
- 2009
- Volume
- 4
- Issue
- 3/4
- Pages
- 129
- ISSN
- 1477-8386 (ISSN-L)
- 1741-8119
- OpenAlex
- W2172071592
- Semantic Scholar
- 8d5c9d8cf6badfd8e048af68d0009b33c53b6c49
- Cited by
- 371
- References
- 93
- Citations by year
- 2026: 13 · 2025: 13 · 2024: 13 · 2023: 17 · 2022: 22 · 2021: 22 · 2020: 21 · 2019: 28 · 2018: 33 · 2017: 32 · 2016: 31 · 2015: 25 · 2014: 30 · 2013: 23 · 2012: 27
- FWCI
- 17.32
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Learning Technology (publishedVersion)
- Funders
- National Science Foundation
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Keywords
- student affect, affect recognition, affective computing, frustration, motivation, self-confidence, affective interventions
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