Empirically Building and Evaluating a Probabilistic Model of User Affect
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
As early as the 1970s, AI-based systems were first implemented in support of personalized and adaptive instruction, giving rise to the field of Intelligent Tutoring Systems.
“As early as the 1970s, AI-based systems were first implemented in support of personalized and adaptive instruction, giving rise to the field of ITS [17][18][19].”
From Artificial Intelligence in Higher Education: A State-of-the-Art Overview of Pedagogical Integrity, Artificial Intelligence Literacy, and Policy Integration (2025) · cites this paper as [18]
The reason students were mainly in positive affective states might be the nature of the learning environment (the exploratory nature of Fractions Lab).
“Similar to Conati and MacLaren (2009), the reason might be the nature of the learning environment (in our case, the exploratory nature of Fractions Lab ).”
From Affective learning: Improving engagement and enhancing learning with affect-aware feedback (2017) · cites this paper as Conati and MacLaren (2009)
The citation lists haven’t been fetched yet
References 76 · Cited by 328
- Authors
- Conati, C., & Maclaren, H.
- Affiliations
- University of British Columbia (CA)
- Corresponding author
- Conati, Cristina
- ORCID
- Conati, Cristina
- Venue
- User Modeling and User-Adapted Interaction
- Published
- 2009-01-30
- Volume
- 19
- Issue
- 3
- Pages
- 267-303
- ISSN
- 0924-1868 (ISSN-L)
- 1573-1391
- OpenAlex
- W2061319836
- Cited by
- 328
- References
- 76
- Citations by year
- 2026: 6 · 2025: 6 · 2024: 2 · 2023: 3 · 2022: 12 · 2021: 10 · 2020: 12 · 2019: 15 · 2018: 19 · 2017: 13 · 2016: 18 · 2015: 29 · 2014: 33 · 2013: 40 · 2012: 40
- FWCI
- 46.61
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- User Modeling and User-Adapted Interaction (publishedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Keywords
- emotion recognition, probabilistic models, dynamic Bayesian networks, OCC model, affective computing, educational video games, plan recognition, user goals, appraisal theory, emotional agents, model evaluation
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 ›
Are you an author, or do you see a problem with this page? Request a review
