Scott W. McQuiggan

Works (4)

Updated: July 5th, 2023 15:55

2008 journal article

Modeling self-efficacy in intelligent tutoring systems: An inductive approach

USER MODELING AND USER-ADAPTED INTERACTION, 18(1-2), 81–123.

By: S. McQuiggan n, B. Mott* & J. Lester n

author keywords: affective user modeling; affective student modeling; self-efficacy; intelligent tutoring systems; inductive learning; human-computer interaction
TL;DR: An inductive approach to automatically constructing models of self-efficacy that can be used at runtime to inform pedagogical decisions is investigated, based on two complementary empirical studies. (via Semantic Scholar)
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4. Quality Education (Web of Science)
Source: Web Of Science
Added: August 6, 2018

2007 article

Modeling and evaluating empathy in embodied companion agents

McQuiggan, S. W., & Lester, J. C. (2007, April). INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES, Vol. 65, pp. 348–360.

By: S. McQuiggan n & J. Lester n

TL;DR: Care, a data-driven affective architecture and methodology for learning models of empathy by observing human-human social interactions is presented and it is suggested that the Care paradigm can provide the basis for effective empathetic behavior control in embodied companion agents. (via Semantic Scholar)
Source: Web Of Science
Added: August 6, 2018

2006 journal article

Diagnosing self-efficacy in intelligent tutoring systems: An empirical study

Lecture Notes in Computer Science, (4053), 565–574.

By: S. McQuiggan n & J. Lester n

TL;DR: The resulting static model is able to predict students' real-time levels of self-efficacy with reasonable accuracy, while the physiologically informed dynamic model is even more accurate. (via Semantic Scholar)
UN Sustainable Development Goals Color Wheel
UN Sustainable Development Goal Categories
4. Quality Education (OpenAlex)
Source: NC State University Libraries
Added: August 6, 2018

conference paper

Modeling task-based vs. affect-based feedback behavior in pedagogical agents: An inductive approach

Robison, J. L., McQuiggan, S. W., & Lester, J. C. Artificial intelligence in education - building learnning systems that care: from knowledge representation to affective modelling , 200, 25–32.

By: J. Robison, S. McQuiggan & J. Lester

Source: NC State University Libraries
Added: August 6, 2018

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