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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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
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.
What counts as a claim ›How we measure confidence ›How we check this ›
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