Works (44)
Updated: February 19th, 2024 08:11
2021 journal article
A Theoretical and Evidence-Based Conceptual Design of MetaDash: An Intelligent Teacher Dashboard to Support Teachers' Decision Making and Students’ Self-Regulated Learning
Frontiers in Education, 6.
author keywords: self-regulated learning (SRL); teacher decision making; learning; multimodal data; teacher dashboards
TL;DR:
A conceptual system design for integrating reinforcement learning into a teacher dashboard to help guide the utilization of multimodal data collected on students’ and teachers’ CAMM SRL processes during complex learning.
(via Semantic Scholar)
www.frontiersin.org (publisher PDF)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science)
Sources: Web Of Science, NC State University Libraries, Crossref
Added: August 23, 2021
2021 journal article
Predictive Student Modeling in Game-Based Learning Environments with Word Embedding Representations of Reflection
INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION, 31(1), 1–23.
author keywords: Student modeling; Early prediction; Game-based learning environments; Self-regulated learning; Reflection
TL;DR:
A predictive student modeling framework that leverages natural language responses to in-game reflection prompts to predict student learning outcomes in a game-based learning environment for middle school microbiology, CRYSTAL ISLAND is presented.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science; OpenAlex)
Source: Web Of Science
Added: November 9, 2020
2020 journal article
Multimodal learning analytics for game-based learning
BRITISH JOURNAL OF EDUCATIONAL TECHNOLOGY, 51(5), 1505–1526.
TL;DR:
The findings suggest that multimodal learning analytics can accurately predict students?
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science; OpenAlex)
Source: Web Of Science
Added: July 20, 2020
2020 article
PRIME: Block-Wise Missingness Handling for Multi-modalities in Intelligent Tutoring Systems
MULTIMEDIA MODELING (MMM 2020), PT II, Vol. 11962, pp. 63–75.
author keywords: Multimodal; Block-wise missing; Learning gain prediction
TL;DR:
A Progressively Refined Imputation for Multi-modalities by auto-Encoder (PRIME), which trains the model based on single, pairwise, and entire modalities for imputation in a progressive manner, and therefore enables us to maximally utilize all the available data.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
3. Good Health and Well-being
(Web of Science)
4. Quality Education
(OpenAlex)
Source: Web Of Science
Added: February 22, 2021
2019 journal article
Are Visual Search Patterns Predictive of Hazard Recognition Performance? Empirical Investigation Using Eye-Tracking Technology
Journal of Construction Engineering and Management, 145(1), 04018115.
author keywords: Construction safety; Hazard recognition; Hazard identification; Eye-tracking; Visual search; Safety training; Personalized training; Occupational safety; Human factors
TL;DR:
Poor hazard recognition is a widespread issue in the construction industry and when construction hazards remain unrecognized, workers are more likely to indulge in unsafe behavior, experience unsafe experience, and so on.
(via Semantic Scholar)
Sources: Web Of Science, Crossref, ORCID
Added: December 3, 2018
2019 journal article
How Does Prior Knowledge Influence Eye Fixations and Sequences of Cognitive and Metacognitive SRL Processes during Learning with an Intelligent Tutoring System?
INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION, 29(1), 1–28.
author keywords: Intelligent tutoring systems; Metacognition; Multichannel data; Prior knowledge; Self-regulated learning
TL;DR:
Results revealed that students with high prior knowledge engaged in processes containing cognitive strategies and metacognitive strategies whereas students with low prior knowledge did not, and have implications for designing adaptive intelligent tutoring systems that provide individualized scaffolding and feedback based on individual differences, such as levels of prior knowledge.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science; OpenAlex)
Source: Web Of Science
Added: June 4, 2019
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
2019 article
The Role of Achievement Goal Orientation on Metacognitive Process Use in Game-Based Learning
ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2019, PT II, Vol. 11626, pp. 36–40.
author keywords: Motivation; Metacognition; Game-based learning environments
TL;DR:
To examine relations between achievement goal orientation—a construct of motivation, metacognition and learning, multiple data channels were collected from 58 students while problem solving in a game-based learning environment.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science; OpenAlex)
Source: Web Of Science
Added: December 2, 2019
2018 journal article
"Let's set up some subgoals": Understanding human-pedagogical agent collaborations and their implications for learning and prompt and feedback compliance
IEEE Transactions on Learning Technologies, 11(1), 54–66.
Source: NC State University Libraries
Added: August 6, 2018
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
Evaluating Adaptive Pedagogical Agents' Prompting Strategies Effect on Students' Emotions
INTELLIGENT TUTORING SYSTEMS, ITS 2018, Vol. 10858, pp. 33–43.
author keywords: Adaptivity; Prompting; Pedagogical agents; Intelligent tutoring systems; Emotions; Affects; Metacognition; Self-regulated learning
TL;DR:
Examination of the impact of different prompting strategies on self-reported agent-directed emotions in an ITS that scaffolds students’ use of self-regulated learning (SRL) strategies, taking into account students' prior knowledge indicates that more intense initial prompting can indeed lead to increased frustration, and sometimes boredom even toward pedagogical agents that are perceived as competent.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science; OpenAlex)
Source: Web Of Science
Added: November 26, 2018
2018 article
Gaze-Enhanced Student Modeling for Game-based Learning
PROCEEDINGS OF THE 26TH CONFERENCE ON USER MODELING, ADAPTATION AND PERSONALIZATION (UMAP'18), pp. 63–72.
author keywords: Student modeling; Gaze; Game-based learning
TL;DR:
Results of a study conducted with 65 college students interacting with the CRYSTAL ISLAND game-based learning environment indicate that the gaze-enhanced student model significantly outperforms the baseline model in dynamically predicting student problem-solving performance.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science; OpenAlex)
Source: Web Of Science
Added: April 2, 2019
2018 article
How Are Students' Emotions Associated with the Accuracy of Their Note Taking and Summarizing During Learning with ITSs?
INTELLIGENT TUTORING SYSTEMS, ITS 2018, Vol. 10858, pp. 233–242.
author keywords: Cognitive learning strategies; Facial expressions of emotion; Process data; Self-regulated learning; Latent semantic analysis
TL;DR:
Results reveal the importance of investigating specific self-regulated learning processes, such as taking notes or making summaries, with future research aimed at examining the differences and similarities between different cognitive and metacognitive processes and how they interact with different emotions similarly or differently during learning.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science; OpenAlex)
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
2018 article
Investigating the Role of Goal Orientation: Metacognitive and Cognitive Strategy Use and Learning with Intelligent Tutoring Systems
INTELLIGENT TUTORING SYSTEMS, ITS 2018, Vol. 10858, pp. 44–53.
author keywords: Achievement goal orientation; Intelligent tutoring systems; Motivation
TL;DR:
Investigation of undergraduate students’ proportional learning gain related to sub-goals set, cognitive strategy use and metacognitive processes differed based on self-reported achievement goal orientation suggested turning away from self-report data, where future studies aim to incorporate multi-channel data over durations of tasks as students interact with ITSs to measure motivation.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science; OpenAlex)
Source: Web Of Science
Added: November 26, 2018
2018 article
The Role of Negative Emotions and Emotion Regulation on Self-Regulated Learning with MetaTutor
INTELLIGENT TUTORING SYSTEMS, ITS 2018, Vol. 10858, pp. 170–179.
author keywords: Emotion regulation; Self-regulated learning; Metacognition; Emotions; Intelligent Tutoring Systems
TL;DR:
Results showed students who perceived themselves as using adaptive emotion regulation strategies reported less negative emotions and can be used to enhance learning outcomes by encouraging adaptive emotionregulation strategies as well as increased frequencies of metacognitive and cognitive SRL 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 journal article
Using sequence mining to reveal the efficiency in scientific reasoning during STEM learning with a game-based learning environment
LEARNING AND INSTRUCTION, 54, 93–103.
author keywords: Metacognition; Self-regulated learning; Scientific reasoning; Game-based learning; Sequence mining; Process data; Log files
TL;DR:
Results revealed that participants who were more efficient at solving the mystery tested significantly fewer partially-relevant and irrelevant items than less efficient participants and have implications for designing adaptive GBLEs that scaffold participants based on in-game behaviors.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education
(Web of Science)
Source: Web Of Science
Added: August 6, 2018
2017 journal article
Development and Testing of a Personalized Hazard-Recognition Training Intervention
JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT, 143(5).
author keywords: Construction safety; Safety training; Safety management; Hazard identification; Hazard recognition; Training methods; Personalized training; Safety intervention; Labor and personnel issues
TL;DR:
AbstractUnrecognized or unmanaged hazards can expose workers to unanticipated safety risk and can potentially result in catastrophic safety incidents.
(via Semantic Scholar)
Source: Web Of Science
Added: August 6, 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, 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 conference paper
Can adaptive pedagogical agents' prompting strategies improve students' learning and self-regulation?
Intelligent tutoring systems, its 2016, 0684, 368–374.
Source: NC State University Libraries
Added: August 6, 2018
2016 journal article
Examining the predictive relationship between personality and emotion traits and students' agent-directed emotions: Towards emotionally-adaptive agent-based learning environments
User Modeling and User-Adapted Interaction, 26(2-3), 177–219.
Source: NC State University Libraries
Added: August 6, 2018
2016 journal article
Self-regulated learning processes vary as a function of epistemic beliefs and contexts: Mixed method evidence from eye tracking and concurrent and retrospective reports
Learning and Instruction, 42, 31–46.
Source: NC State University Libraries
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
2016 conference paper
Using eye-tracking to determine the impact of prior knowledge on self-regulated learning with an adaptive hypermedia-learning environment
Intelligent tutoring systems, its 2016, 0684, 34–47.
Source: NC State University Libraries
Added: August 6, 2018
2016 conference paper
Using multi-channel data to assess, understand, and support affect and metacognition with intelligent tutoring systems
Intelligent tutoring systems, its 2016, 0684, 543–544.
Source: NC State University Libraries
Added: August 6, 2018
2015 journal article
A multi-componential analysis of emotions during complex learning with an intelligent multi-agent system
Computers in Human Behavior, 48, 615–625.
Source: NC State University Libraries
Added: August 6, 2018
2015 journal article
Defining and measuring engagement and learning in science: Conceptual, theoretical, methodological, and analytical issues
Educational Psychologist, 50(1), 84–94.
Source: NC State University Libraries
Added: August 6, 2018
2015 conference paper
Examining the predictive relationship between personality and emotion traits and learners' agent-direct emotions
Artificial intelligence in education, aied 2015, 9112, 145–154.
Source: NC State University Libraries
Added: August 6, 2018
2015 journal article
Motivation matters: Interactions between achievement goals and agent scaffolding for self-regulated learning within an intelligent tutoring system
Computers in Human Behavior, 52, 338–348.
Source: NC State University Libraries
Added: August 6, 2018
2015 journal article
Team regulation in a simulated medical emergency: An in-depth analysis of cognitive, metacognitive, and affective processes
Instructional Science, 43(3), 401–426.
Source: NC State University Libraries
Added: August 6, 2018
2015 journal article
The curious case of climate change: Testing a theoretical model of epistemic beliefs, epistemic emotions, and complex learning
Learning and Instruction, 39, 168–183.
Source: NC State University Libraries
Added: August 6, 2018
2014 journal article
Can the use of cognitive and metacognitive self-regulated learning strategies be predicted by learners' levels of prior knowledge in hypermedia-learning environments?
Computers in Human Behavior, 39, 356–367.
Source: NC State University Libraries
Added: August 6, 2018
2014 journal article
Issues in dealing with sequential and temporal characteristics of self- and socially-regulated learning
Metacognition and Learning, 9(2), 217–228.
Source: NC State University Libraries
Added: August 6, 2018
2014 journal article
Metacognition
Cambridge Handbook of the Learning Sciences, 2nd Edition, 63–87.
Source: NC State University Libraries
Added: August 6, 2018
2014 journal article
Note-taking within MetaTutor: Interactions between an intelligent tutoring system and prior knowledge on note-taking and learning
Educational Technology Research and Development: ETR & D, 62(5), 507–528.
Source: NC State University Libraries
Added: August 6, 2018
conference paper
Is more agency better? The impact of student agency on game-based learning
Sawyer, R., Smith, A., Rowe, J., Azevedo, R., & Lester, J. Artificial intelligence in education, aied 2017, 10331, 335–346.
Source: NC State University Libraries
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
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