By Joseph F. Grafsgaard, Joseph B. Wiggins, Kristy Elizabeth Boyer, Eric N. Wiebe (auth.), H. Chad Lane, Kalina Yacef, Jack Mostow, Philip Pavlik (eds.)

This e-book constitutes the refereed court cases of the sixteenth foreign convention on synthetic Intelligence in schooling, AIED 2013, held in Memphis, TN, united states in July 2013. The fifty five revised complete papers offered including seventy three poster shows have been rigorously reviewed and chosen from a complete of 168 submissions. The papers are prepared in classes on pupil modeling and personalization, open-learner modeling, affective computing and engagement, academic facts mining, studying jointly (collaborative studying and social computing), ordinary language processing, pedagogical brokers, metacognition and self-regulated studying, suggestions and scaffolding, designed studying actions, academic video games and narrative, and outreach and scaling up.

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Extra resources for Artificial Intelligence in Education: 16th International Conference, AIED 2013, Memphis, TN, USA, July 9-13, 2013. Proceedings

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Phase 3: Retrospective Affect Judgment. The retrospective affect judgment phase commenced immediately after the programming session. It involved the participant providing judgments of their emotions while viewing synchronized videos of their face and screen recorded during the session. Participants provided judgments on 13 emotions, which were mostly selected from Pekrun’s taxonomy of academic emotions [15]. These included basic emotions (anger, disgust, fear, sadness, surprise, happiness), learning-centered emotions (anxiety, boredom, frustration, flow/engaged, curiosity, confusion/uncertainty) and neutral (no apparent feeling).

Benefits and Challenges of Real-Time Uncertainty Detection and Adaptation in a Spoken Dialogue Computer Tutor. F. Grafsgaard et al. 9. : Disequilibrium in the Mind, Disharmony in the Body. Cognition & Emotion 26, 362–374 (2012) 10. : Real-Time Inference of Mental States from Facial Expressions and Upper Body Gestures. In: Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition, pp. 909–914 (2011) 11. : Real-Time Monitoring of ECG and GSR Signals during Computer-Based Training.

This exercise represents an incremental step from reading user input and storing it as a variable (previous exercise) to reading two different inputs into different variables (current exercise). Participants could complete as many exercises as possible in the 20 minute time limit for the scaffolding phase before being automatically directed to the fadeout phase. 40). Phase 2: Fadeout (15 minutes). The fadeout phase consisted of two exercises that integrated the individual concepts covered in the scaffolding phase.

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