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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
Romero-Zaldivar, Vicente-Arturo; Pardo, Abelardo; Burgos, Daniel; Delgado Kloos, Carlos – Computers & Education, 2012
The interactions that students have with each other, with the instructors, and with educational resources are valuable indicators of the effectiveness of a learning experience. The increasing use of information and communication technology allows these interactions to be recorded so that analytic or mining techniques are used to gain a deeper…
Descriptors: Academic Achievement, Prediction, Learning Experience, Data
Austin, Katherine A. – Computers & Education, 2009
In the wake of the information explosion and rapidly progressing technology [Mayer, R. E. (2001). "Multimedia learning". Cambridge: University Press] formulated a theory that focused on human cognition, rather than technology capacity and features. By measuring the effect of cognitive individual differences and display design manipulations on…
Descriptors: Multimedia Materials, Learning Modules, Transfer of Training, Cognitive Processes
Moridis, Christos N.; Economides, Anastasios A. – Computers & Education, 2009
Building computerized mechanisms that will accurately, immediately and continually recognize a learner's affective state and activate an appropriate response based on integrated pedagogical models is becoming one of the main aims of artificial intelligence in education. The goal of this paper is to demonstrate how the various kinds of evidence…
Descriptors: Prediction, Artificial Intelligence, Inferences, Psychological Patterns
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