In Paper Session: 29.028 - Novel Methods for Assessing and Enhancing Metacognitive Monitoring
On Paper: Can Sequence Mining Augment Our Understanding of Metacomprehension During Multimedia Learning? Evidence From Eye Movements

Non-Presenting Author

In Structured Poster Session: 32.079 - What Can Be Inferred From Trace Data? Current Methods to Triangulate and Validate Traces of Learning Behavior
On Session Paper: 3 - Inferring Emotional States During Metacomprehension Judgments: Evidence From Facial Expressions, Eye Movements, and Metacognitive Judgments

Non-Presenting Author

In Structured Poster Session: 32.079 - What Can Be Inferred From Trace Data? Current Methods to Triangulate and Validate Traces of Learning Behavior
On Session Paper: 2 - Using Eye-Tracking and Log File Data as Indicators of Metacognitive Monitoring and Cognitive Learning Strategies With Game-Based Learning Environments?

Non-Presenting Author

In Symposium: 34.030 - Physiology of Emotion in Education: Theory, Methodology, and Insight
On Session Paper: Does Skin Conductance Response Indicate Metacognitive Processes?

Non-Presenting Author

In Structured Poster Session: 41.025 - Learning, Engagement, and Assessment in Technology-Rich Learning Environments
On Session Paper: 7 - Understanding and Reasoning About Cognitive, Metacognitive, and Affective Processes Used During Complex Learning With Advanced Learning Technologies

Presenting Author

In Paper Session: 42.032 - Technology Experiences That Cognitively and Metacognitively Engage Learners

Chair

In Paper Session: 42.032 - Technology Experiences That Cognitively and Metacognitively Engage Learners
On Paper: Latent Class Profiling of Self-Regulated Learning in MetaTutor: A Technology-Rich Learning Environment

Non-Presenting Author

In Symposium: 44.081 - Data-Driven Techniques That Complement Theoretical Study of Cognition and Learning With Technology
On Session Paper: Challenges in Using Data Mining to Identify Robust Indicators of Cognitive, Affective, and Metacognitive Self-Regulatory Processes From Trace Data During Learning With Advanced Learning Technologies

Presenting Author