Full Description
This two-volume set LNCS 12792 and 12793 constitutes the refereed proceedings of the Third International Conference on Adaptive Instructional Systems, AIS 2021, held as part of the 23rd International Conference, HCI International 2021, which took place in July 2021. Due to COVID-19 pandemic the conference was held virtually.
The total of 1276 papers and 241 posters included in the 39 HCII 2021 proceedings volumes was carefully reviewed and selected from 5222 submissions. The papers of AIS 2021, Part II, focus on Learner Modelling and State Assessment in AIS.
Contents
Helping Instructor Pilots Detect and Respond to Engagement Lapses in Simulations.- Dynamic Analytics for the Detection of Quality and Alignment Issues in an Online Hybrid Adaptive Instructional and Assessmen System.- Predicting Literacy Skills via Stealth Assessment in a Simple Vocabulary Game.- Skill Mastery Measurement and Prediction to Adapt Instruction Strategies.- Measuring Flow, Immersion and Arousal/Valence for Application in Adaptive Learning .- Modelling and Quantifying Learner Motivation for Adaptive Systems: Current Insight and Future Perspectives.- Competency-Based Experiential-Expertise and Future Adaptive Learning Systems.- Early Prediction of At-risk Students in a Virtual Learning Environment using Deep Learning Techniques.- An Experiential Competency Application Framework.- Individual Differences in the Relationship between Emotion and Performance in Command-and-Control Environments.- Using Principled Assessment Design and Item Difficulty Modeling to Connect Hybrid Adaptive Instructional and Assessment Systems: Proof of Concept.- Dynamic Cognitive Modeling for Adaptive Serious Games.- Staying Ahead of the Curve: Selecting Students for Newly Arising Tasks.- Designing a Learner Model for use in training Analysts in a Social Media Practice Environment.- Measuring Cognitive Load for Adaptive Instructional Systems by Using a Pressure Sensitive Computer Mouse.- Measuring and Integrating Facial Expressions and Head Pose as Indicators of Engagement and Affect in Tutoring Systems.- Enhancing Personalization by Integrating Top-down and Bottom-up Approaches to Learner Modeling.
