Works (7)
Updated: July 5th, 2023 15:42
2016 article
Are Pedagogical Agents' External Regulation Effective in Fostering Learning with Intelligent Tutoring Systems?
INTELLIGENT TUTORING SYSTEMS, ITS 2016, Vol. 9684, pp. 197–207.
author keywords: Self-regulated learning; Metacognition; Pedagogical agents; Externally regulated learning; ITS; Scaffolding; Learning; Product data; Process data
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)
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.
author keywords: Self-efficacy; Tutorial dialogue; Computer science education
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)
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
INTELLIGENT TUTORING SYSTEMS, ITS 2016, Vol. 9684, pp. 154–164.
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 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.
Source: NC State University Libraries
Added: August 6, 2018
2013 article
Automatically Recognizing Facial Indicators of Frustration: A Learning-Centric Analysis
2013 HUMAINE ASSOCIATION CONFERENCE ON AFFECTIVE COMPUTING AND INTELLIGENT INTERACTION (ACII), pp. 159–165.
author keywords: affect; frustration; learning; computer-mediated tutoring; facial expression recognition; facial action units; intensity
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 Goal Categories
4. Quality Education
(OpenAlex)
Sources: Web Of Science, NC State University Libraries
Added: August 6, 2018
2012 conference paper
Multimodal analysis of the implicit affective channel in computer-mediated textual communication
ICMI '12: Proceedings of the ACM International Conference on Multimodal Interaction, 145–152.
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 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.
Source: NC State University Libraries
Added: August 6, 2018
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