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Latham, Annabel; Crockett, Keeley; McLean, David; Edmonds, Bruce – Computers & Education, 2012
This paper proposes a generic methodology and architecture for developing a novel conversational intelligent tutoring system (CITS) called Oscar that leads a tutoring conversation and dynamically predicts and adapts to a student's learning style. Oscar aims to mimic a human tutor by implicitly modelling the learning style during tutoring, and…
Descriptors: Cognitive Style, Teaching Methods, Cognitive Measurement, Prediction
Macfadyen, Leah P.; Dawson, Shane – Computers & Education, 2010
Earlier studies have suggested that higher education institutions could harness the predictive power of Learning Management System (LMS) data to develop reporting tools that identify at-risk students and allow for more timely pedagogical interventions. This paper confirms and extends this proposition by providing data from an international…
Descriptors: Network Analysis, Academic Achievement, At Risk Students, Prediction
Ogan, Amy; Aleven, Vincent; Jones, Christopher – International Journal of Artificial Intelligence in Education, 2009
Most successes in intelligent tutoring systems have come in well-defined domains like algebra or physics. We investigate how to support students in acquiring ill-defined skills of intercultural competence using an online environment that employs clips of feature films from a target culture. To test the effectiveness of a set of attention-focusing…
Descriptors: Foreign Countries, Intelligent Tutoring Systems, Perspective Taking, Cultural Awareness

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