An intelligent learning diagnosis system for Web-based thematic learning platform
Computers & Education
Information and Software Technology
Information Resources Management Journal
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Department of Economics, University of Illinois at Urbana-Champaign This study tests the hypothesis that underlying learning style is a useful predictor of attitude toward computer-based instruction and learning. My undergraduate economics students participated in a learning style assessment based on the Gregorc Learning Style Delineator to determine their basic learning style: concrete or abstract, sequential or random. Students were also surveyed as to their attitudes toward the computer-based aspects of the class. Correlation analysis showed that students with sequential learning styles use computer-based instructional techniques more frequently and prefer them to traditional instructional techniques when compared with students whose learning styles are random.