SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Fearnot!: an experiment in emergent narrative
Lecture Notes in Computer Science
Evaluating the effect of technology on note-taking and learning
CHI '06 Extended Abstracts on Human Factors in Computing Systems
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Measuring Presence in Virtual Environments: A Presence Questionnaire
Presence: Teleoperators and Virtual Environments
Serious Use of a Serious Game for Language Learning
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Comparing Linguistic Features for Modeling Learning in Computer Tutoring
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Using Machine Learning Techniques to Analyze and Support Mediation of Student E-Discussions
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Profiling Student Interactions in Threaded Discussions with Speech Act Classifiers
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Narrative-Centered tutorial planning for inquiry-based learning environments
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Believable agents and intelligent story adaptation for interactive storytelling
TIDSE'06 Proceedings of the Third international conference on Technologies for Interactive Digital Storytelling and Entertainment
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Note-taking has a long history in educational settings. Previous research has shown that note-taking leads to improved learning and performance on assessment. It was therefore hypothesized that note-taking could play an important role in narrative-centered learning. To investigate this question, a note-taking facility was introduced into a narrative-centered learning environment. Students were able to use the facility to take and review notes while solving a science mystery. In this paper we explore the individual differences of note-takers and the notes they take. Finally, we use machine learning techniques to model the content of student notes to support future pedagogical adaptation in narrative-centered learning environments.