Works (13)
Updated: July 5th, 2023 15:37
2019 journal article
Integrating metacognitive judgments and eye movements using sequential pattern mining to understand processes underlying multimedia learning
COMPUTERS IN HUMAN BEHAVIOR, 96, 223–234.
author keywords: Metacomprehension; Multimedia learning; Eye movements; Sequential pattern mining; Differential sequence mining
TL;DR:
To determine if eye-movement dyads could be identified by sequence mining techniques and aligned with self-reported metacognitive judgments during learning with multimedia materials that contain conceptual discrepancies designed to interfere with participants' metacomprehension, this study examined students' eye movements as they learned with complex multimedia materials.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science; OpenAlex)
Source: Web Of Science
Added: June 17, 2019
2018 article
Changes in Emotion and Their Relationship with Learning Gains in the Context of MetaTutor
INTELLIGENT TUTORING SYSTEMS, ITS 2018, Vol. 10858, pp. 202–211.
author keywords: Computer-based learning environments; Academic emotions; Latent transition analysis
TL;DR:
The findings suggest the need for timely intervention for learners who are on the verge of negative emotional trajectories, and the complex relationship between learning gains and emotions.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science)
Source: Web Of Science
Added: November 26, 2018
2018 article
How Do Different Levels of AU4 Impact Metacognitive Monitoring During Learning with Intelligent Tutoring Systems?
INTELLIGENT TUTORING SYSTEMS, ITS 2018, Vol. 10858, pp. 223–232.
author keywords: Affective and metacognitive processes; Hypermedia-based ITS; Process data; Self-regulated learning
TL;DR:
Results revealed that participants with high pre-test scores had the highest accuracy scores with low levels of AU4 and use of more metacognitive monitoring processes, whereas participants with low pre- test scores had higher accuracy scoresWith high levels ofAU4 andUse of more meetacognitive Monitoring processes.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science; OpenAlex)
Source: Web Of Science
Added: November 26, 2018
2018 conference paper
Identifying How Metacognitive Judgments Influence Student Performance During Learning with MetaTutorIVH
INTELLIGENT TUTORING SYSTEMS, ITS 2018, 10858, 140–149.
TL;DR:
This paper reports on a study investigating how students’ EOL judgments can influence their performance and significantly predict their learning outcomes during learning with MetaTutorIVH, an ITS for human physiology.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(OpenAlex)
Source: NC State University Libraries
Added: November 26, 2018
2017 conference paper
Emotion recognition with facial expressions and physiological signals
2017 IEEE Symposium Series on Computational Intelligence (SSCI), 1170–1177.
TL;DR:
A temporal information preserving multi-modal emotion recognition framework based on physiological and facial expression data streams that significantly improves the emotion recognition performance when physiological signals are used and the best performance is achieved when fusing facial expressions and physiological data.
(via Semantic Scholar)
Sources: NC State University Libraries, ORCID
Added: August 6, 2018
2017 article
The Impact of Student Individual Differences and Visual Attention to Pedagogical Agents During Learning with MetaTutor
ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2017, Vol. 10331, pp. 149–161.
author keywords: Pedagogical agents; Personalization; Visual attention; Achievement goals; Personality traits; Intelligent tutoring systems
TL;DR:
The relationship between students’ individual differences and visual attention to pedagogical agents (PAs) during learning with MetaTutor, a hypermedia-based intelligent tutoring systems, is investigated.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science; OpenAlex)
Source: Web Of Science
Added: August 6, 2018
2017 conference paper
Toward affect-sensitive virtual human tutors: The influence of facial expressions on learning and emotion
International conference on affective computing and intelligent, 184–189.
TL;DR:
Results from the study suggest that learners' performance is significantly better when a human tutor agent facially expresses emotions that are congruent with the content relevancy and that learners facially express significantly more confusion when the human tutorAgent provides incongruent facial expressions.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(OpenAlex)
Source: NC State University Libraries
Added: August 6, 2018
2017 journal article
Using multi-channel data with multi-level modeling to assess in-game performance during gameplay with CRYSTAL ISLAND
COMPUTERS IN HUMAN BEHAVIOR, 76, 641–655.
author keywords: Cognitive strategies; Metacognitive monitoring; Game-based learning environments; Eye tracking; Log files; Self-regulated learning
TL;DR:
This study uses multi-level modeling with data from eye movements and log files to examine the cognitive and metacognitive self-regulatory processes used by 50 college students as they read books and completed the associated in-game assessments (concept matrices) while playing the Crystal Island game-based learning environment.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science; OpenAlex)
Source: Web Of Science
Added: August 6, 2018
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 article
Using Multi-level Modeling with Eye-Tracking Data to Predict Metacognitive Monitoring and Self-regulated Learning with CRYSTAL ISLAND
INTELLIGENT TUTORING SYSTEMS, ITS 2016, Vol. 9684, pp. 240–246.
author keywords: Metacognition; Self-regulated learning; Game-based learning; Eye tracking; Process data; Scientific reasoning
TL;DR:
A study that investigated how college students' eye tracking behavior predicted performance on embedded assessments within the Crystal Island GBLE found that participants strategized when reading book and article content and completing assessments, which led to better performance.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science)
Source: Web Of Science
Added: August 6, 2018
conference paper
Toward affect-sensitive virtual human tutors: The influence of facial expressions on learning and emotion
Mudrick, N. V., Taub, M., Azevedo, R., Rowe, J., & Lester, J. International conference on affective computing and intelligent, 184–189.
Source: NC State University Libraries
Added: August 6, 2018
conference paper
Using multi-level modeling with eye-tracking data to predict metacognitive monitoring and self-regulated learning with CRYSTAL ISLAND
Taub, M., Mudrick, N. V., Azevedo, R., Millar, G. C., Rowe, J., & Lester, J. Intelligent tutoring systems, its 2016, 0684, 240–246.
Source: NC State University Libraries
Added: August 6, 2018
conference paper
Using multi-level modeling with eye-tracking data to predict metacognitive monitoring and self-regulated learning with crystal island
Taub, M., Mudrick, N. V., Azevedo, R., Millar, G. C., Rowe, J., & Lester, J. Intelligent tutoring systems, its 2016, 9684, 240–246.
Source: NC State University Libraries
Added: August 6, 2018
Citation Index includes data from a number of different sources. If you have questions about the sources of data in the Citation Index or need a set of data which is free to re-distribute, please contact us.
Certain data included herein are derived from the Web of Science© and InCites© (2024) of Clarivate Analytics. All rights reserved. You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.
