Diagnosing student learning problems based on historical assessment records

In the past decade, researchers have attempted to develop computer-assisted learning and testing systems to help students improve their learning performance. Conventional testing systems simply provide students with a score, and do not offer sufficient information in order to improve their learning...

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Bibliographic Details
Published in:Innovations in education and teaching international 2008-02, Vol.45 (1), p.77-89
Main Authors: Hwang, Gwo-Jen, Tseng, Judy C.R., Hwang, Gwo-Haur
Format: Article
Language:English
Subjects:
Achievement tests
Adaptive Testing
Algorithms
Answer Sheets
Chemical elements
Cognitive Mapping
Computer Assisted Testing
Computer science
computer-assisted learning
Computer-based testing
Concept Mapping
concept-effect relationships
Confidence intervals
Courseware
Evaluation Methods
Expert systems
Instructional Improvement
Intelligent Tutoring Systems
Learning
learning diagnosis
Learning Problems
Mathematics
Neural networks
Performance evaluation
Portfolio Assessment
Program Effectiveness
Researchers
Schematic Studies
Science Education
Skills
Students
Studies
Teachers
Technological change
Tutoring
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Summary:In the past decade, researchers have attempted to develop computer-assisted learning and testing systems to help students improve their learning performance. Conventional testing systems simply provide students with a score, and do not offer sufficient information in order to improve their learning performance. It would be of more benefit to students if the test results could be critically analysed and hence learning suggestions could be offered accordingly. This study proposes an algorithm for diagnosing students' learning problems and provides personalised learning suggestions for Science and Mathematics courses. An intelligent tutoring, evaluation and diagnosis system has been implemented based on the novel approach. Experimental results on a Mathematics course have demonstrated the feasibility of this approach in enhancing students' learning performance, making it highly promising for further study.
ISSN:1470-3297
1470-3300
DOI:10.1080/14703290701757476