Works (4)
Updated: July 5th, 2023 15:42
2019 article
Predicting Dialogue Breakdown in Conversational Pedagogical Agents with Multimodal LSTMs
ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2019, PT II, Vol. 11626, pp. 195–200.
author keywords: Conversational pedagogical agent; Multimodal; Dialogue breakdown detection; Natural language processing; Gaze
TL;DR:
Results from a study with 92 middle school students demonstrate that multimodal long short-term memory network (LSTM)-based dialogue breakdown detectors incorporating eye gaze features achieve high predictive accuracies and recall rates, suggesting that multi-modal detectors can play an important role in designing conversational pedagogical agents that effectively engage students in dialogue.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science; OpenAlex)
Sources: Web Of Science, NC State University Libraries, ORCID
Added: December 2, 2019
2019 article
Take the Initiative: Mixed Initiative Dialogue Policies for Pedagogical Agents in Game-Based Learning Environments
ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2019, PT II, Vol. 11626, pp. 314–318.
author keywords: Pedagogical agents; Game-based learning; Initiative
TL;DR:
A study to investigate two different agent dialogue policies with regard to conversational initiative, a core consideration in dialogue system design found the Mixed Initiative policy better supported the goals of the game-based learning environment by fostering exploration, yielding better performance on in-game assessments, and creating higher student engagement.
(via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries, ORCID
Added: December 2, 2019
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
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
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