Megan Wiedbusch

dblp:243/3782 · also Megan D. Wiedbusch · DBLP profile ↗
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9ranked-venue papers
4as first author
6since 2021 · last 2025
0000-0002-4619-1709ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 8 · 3 first-author · 6 since 2021Human-computer interaction and ubiquitous computing · 6 · 2 first-author · 4 since 2021Artificial intelligence and machine learning · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author
YearPublicationVenuePosition
2025 Balancing Emotional and Motivational Self-Regulatory Processes During Complex Learning with Intelligent Tutoring Systems
Annamarie Brosnihan, Megan Wiedbusch, Cameron Marano, Maral Karimi, Matthew Moreno, Tara Delgado, Oscar Tidwell, Roger Azevedo
AIED (2)2
2025 Examining the Influence of Students' Personality on Self-regulatory Behaviors and Learning Outcomes During Complex Learning with MetaTutor
Cameron Marano, Megan Wiedbusch, Annamarie Brosnihan, Matthew Moreno, Milla Sherman, Maral Karimi, Tara Delgado, Roger Azevedo
AIED (5)2
2022 Pedagogical Agent Support and Its Relationship to Learners' Self-regulated Learning Strategy Use with an Intelligent Tutoring System
Daryn A. Dever, Nathan A. Sonnenfeld, Megan Wiedbusch, Roger Azevedo
AIED (1)3
2022 Clustering Learner's Metacognitive Judgment Accuracy and Bias to Explore Learning with AIEd Systems
Megan Wiedbusch, Nathan A. Sonnenfeld, Daryn A. Dever, Roger Azevedo
AIED (1)1
2022 Enhancing Learner Models for Pedagogical Agent Scaffolding of Self-Regulated Learning
Daryn A. Dever, Megan Wiedbusch, Roger Azevedo
ICCE2
2022 Pedagogical Companions to Support Teachers' Interpretation of Students' Engagement from Multimodal Learning Analytics Dashboards
Megan Wiedbusch, Nathan A. Sonnenfeld
ICCE1
2020 Can a Composite Metacognitive Judgment Accuracy Score Successfully Capture Performance Variance during Multimedia Learning?
Megan Wiedbusch, Roger Azevedo
CogSci1
2020 Modeling Metacomprehension Monitoring Accuracy with Eye Gaze on Informational Content in a Multimedia Learning Environment
abstract
Multimedia learning environments support learners in developing self-regulated learning (SRL) strategies. However, capturing these strategies and cognitive processes can be difficult for researchers because cognition is often inferred, not directly measured. This study sought to model self-reported metacognitive judgments using eye-tracking from 60 undergraduate students as they learned about biological systems with MetaTutorIVH, a multimedia learning environment. We found that participants’ gaze behaviors were different between the perceived relevance of the instructional content provided regardless of the actual content relevance. Additionally, we fit a cumulative link mixed effects ordinal regression model to explain reported metacognitive judgments based on content fixations, relevance, and presentation type. Main effects were found for all variables and several interactions between both fixations and content relevance as well as content fixations and presentation type. Surprisingly, accurate metacognitive judgments did not explain performance. Implication for multimedia learning environment design are discussed.
Megan Wiedbusch, Roger Azevedo
ETRA1
2019 Learners' Gaze Behaviors and Metacognitive Judgments with an Agent-Based Multimedia Environment
Daryn A. Dever, Megan Wiedbusch, Roger Azevedo
AIED (2)2