Joseph F. Grafsgaard

Works (7)

Updated: July 5th, 2023 15:42

2016 article

Are Pedagogical Agents’ External Regulation Effective in Fostering Learning with Intelligent Tutoring Systems?

Azevedo, R., Martin, S. A., Taub, M., Mudrick, N. V., Millar, G. C., & Grafsgaard, J. F. (2016, January 1). Lecture Notes in Computer Science.

By: R. Azevedo n, S. Martin n, M. Taub n, N. Mudrick n, G. Millar n & J. Grafsgaard n

author keywords: Self-regulated learning; Metacognition; Pedagogical agents; Externally regulated learning; ITS; Scaffolding; Learning; Product data; Process data
topics (OpenAlex): Intelligent Tutoring Systems and Adaptive Learning; Innovative Teaching and Learning Methods; Online Learning and Analytics
TL;DR: Testing whether external regulation provided by artificial pedagogical agents PAs was effective in facilitating learners' self-regulated learning SRL and can therefore foster complex learning with a hypermedia-based intelligent tutoring system indicated that those in the AS condition gained significantly more knowledge about the science topic thanThose in the C condition. (via Semantic Scholar)
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UN Sustainable Development Goal Categories
4. Quality Education (Web of Science; OpenAlex)
Source: Web Of Science
Added: August 6, 2018

2016 journal article

Do You Think You Can? The Influence of Student Self-Efficacy on the Effectiveness of Tutorial Dialogue for Computer Science

International Journal of Artificial Intelligence in Education, 27(1), 130–153.

By: J. Wiggins n, J. Grafsgaard n, K. Boyer *, E. Wiebe n  & J. Lester n 

author keywords: Self-efficacy; Tutorial dialogue; Computer science education
topics (OpenAlex): Intelligent Tutoring Systems and Adaptive Learning; Innovative Teaching and Learning Methods; Teaching and Learning Programming
TL;DR: This article examines a corpus of effective human tutoring for computer science to discover the extent to which considering self-efficacy as measured within a pre-survey, coupled with dialogue and task events during tutoring, improves models that predict the student's self-reported frustration and learning gains after tutoring. (via Semantic Scholar)
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UN Sustainable Development Goal Categories
4. Quality Education (Web of Science; OpenAlex)
Sources: Web Of Science, NC State University Libraries, Crossref
Added: August 6, 2018

2016 article

Predicting Learning from Student Affective Response to Tutor Questions

Vail, A. K., Grafsgaard, J. F., Boyer, K. E., Wiebe, E. N., & Lester, J. C. (2016, January 1). Lecture Notes in Computer Science, Vol. 0684, pp. 154–164.

By: A. Vail n, J. Grafsgaard n, K. Boyer *, E. Wiebe n  & J. Lester n 

topics (OpenAlex): Intelligent Tutoring Systems and Adaptive Learning; Innovative Teaching and Learning Methods; Visual and Cognitive Learning Processes
TL;DR: This work examines student facial expression, electrodermal activity, posture, and gesture immediately following inference questions posed by human tutors and shows that for human-human task-oriented tutorial dialogue, facial expression and skin conductance response following tutor inference questions are highly predictive of student learning gains. (via Semantic Scholar)
UN Sustainable Development Goals Color Wheel
UN Sustainable Development Goal Categories
4. Quality Education (Web of Science; OpenAlex)
Sources: Web Of Science, NC State University Libraries
Added: August 6, 2018

2015 conference paper

2015 multimodal learning and analytics grand challenge

ICMI'15: Proceedings of the 2015 ACM International Conference on Multimodal Interaction, 525–529.

By: M. Worsley, K. Chiluiza, J. Grafsgaard & X. Ochoa

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

2013 article

Automatically Recognizing Facial Indicators of Frustration: A Learning-centric Analysis

Grafsgaard, J. F., Wiggins, J. B., Boyer, K. E., Wiebe, E. N., & Lester, J. C. (2013, September 1). International Conference on Affective Computing and Intelligent, pp. 159–165.

By: J. Grafsgaard n, J. Wiggins n, K. Boyer n, E. Wiebe n  & J. Lester n 

author keywords: affect; frustration; learning; computer-mediated tutoring; facial expression recognition; facial action units; intensity
topics (OpenAlex): Intelligent Tutoring Systems and Adaptive Learning; Educational and Psychological Assessments; EEG and Brain-Computer Interfaces
TL;DR: A study to analyze a video corpus of computer-mediated human tutoring using an automated facial expression recognition tool that detects fine-grained facial movements reveals three significant relationships between facial expression, frustration, and learning. (via Semantic Scholar)
UN Sustainable Development Goals Color Wheel
UN Sustainable Development Goal Categories
4. Quality Education (OpenAlex)
Sources: Web Of Science, NC State University Libraries
Added: August 6, 2018

2012 article

Multimodal analysis of the implicit affective channel in computer-mediated textual communication

Grafsgaard, J. F., Fulton, R. M., Boyer, K. E., Wiebe, E. N., & Lester, J. C. (2012, October 22).

By: J. Grafsgaard n, R. Fulton n, K. Boyer n, E. Wiebe  n & J. Lester n 

topics (OpenAlex): Digital Communication and Language; Speech and dialogue systems; Language, Metaphor, and Cognition
TL;DR: Computer-mediated tutoring sessions were recorded with Kinect video and depth images and processed with novel tracking techniques for posture and hand-to-face gestures and it was demonstrated that tutors implicitly perceived students' focused attention, physical demand, and frustration. (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 self-efficacy across age groups with automatically tracked facial expression

Grafsgaard, J. F., Lee, S. Y., Mott, B. W., Boyer, K. E., & Lester, J. C. Artificial intelligence in education, aied 2015, 9112, 582–585.

By: J. Grafsgaard, S. Lee, B. Mott, K. Boyer & J. Lester 

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

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