By Janet Metcalfe (auth.), Gautam Biswas, Susan Bull, Judy Kay, Antonija Mitrovic (eds.)
This e-book constitutes the refereed lawsuits of the fifteenth overseas convention on man made Intelligence in schooling, AIED 2011, held in Auckland, New Zealand in June/July 2011.
The forty nine revised complete papers provided including 3 invited talks and prolonged abstracts of poster displays, younger researchers contributions and interactive structures reviews and workshop reviews have been conscientiously reviewed and chosen from a complete of 193 submissions. The papers file on technical advances in and cross-fertilization of methods and concepts from the numerous topical components that make up this hugely interdisciplinary box of analysis and improvement together with synthetic intelligence, agent know-how, computing device technology, cognitive and studying sciences, schooling, academic know-how, video game layout, psychology, philosophy, sociology, anthropology and linguistics.
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Additional resources for Artificial Intelligence in Education: 15th International Conference, AIED 2011, Auckland, New Zealand, June 28 – July 2011
These features are based on thresholds which are given initial values but are also optimized by grid search, using as goodness criterion the cross-validated correlation between an individual feature and each student’s performance on the transfer test. Finally a model is trained on these features (using both the original and optimized thresholds) to predict each student’s performance on the transfer test, and is cross-validated. We then compare this model to a baseline prediction of transfer, Bayesian Knowledge Tracing (BKT)  fit using brute force, which has been previously shown to predict student post-test problem-solving performance reasonably well within this lesson .
After a correct choice, the flower flew across the screen to Paul’s vase with a fanfare; an incorrect choice led him to say: “Not that one”, and indicate the target again. This simple narrative provides a framework to support joint attention and to motivate a shared (and repeated) activity between the child and the character. In the initial trials, many participants had difficulty self-regulating and often touched the screen before Paul had indicated a flower. g. 2 There were no training trials, as the participants’ behaviour when they were still unfamiliar with Paul’s cues was of prime interest.
AIED 2011, LNAI 6738, pp. 7–14, 2011. © Springer-Verlag Berlin Heidelberg 2011 8 A. Alcorn et al. Many general questions about the abilities of young children with ASD to interact with VEs and virtual characters remain unanswered. The ECHOES Technology Enhanced Learning (TEL) project is developing an intelligent multi-modal VE for supporting and scaffolding social communication in children aged 5-8 years, with and without an ASD. It comprises a range of touch screen-based learning activities focused on joint attention initiation and response.