Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Student assessment using Bayesian nets
International Journal of Human-Computer Studies - Special issue: real-world applications of uncertain reasoning
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Investigating Students' Self-Assessment Skills
UM '01 Proceedings of the 8th International Conference on User Modeling 2001
Individualized exercises for self-assessment of programming knowledge: An evaluation of QuizPACK
Journal on Educational Resources in Computing (JERIC)
Improving Student Performance Using Self-Assessment Tests
IEEE Intelligent Systems
Technology supports for distributed and collaborative learning over the internet
ACM Transactions on Internet Technology (TOIT)
IEEE Transactions on Learning Technologies
Adaptive Learning with the LS-Plan System: A Field Evaluation
IEEE Transactions on Learning Technologies
Attributed concept maps: fuzzy integration and fuzzy matching
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Student progress is critical for determining proper learning materials and their dissemination schedules in an e-learning system. However, existing work usually identifies student progress by scoring subject specific attributes or by determining status about task completion, which are too simple to suggest how teaching and learning strategies can be adjusted for improving student performance. To address this, we propose a set of student progress indicators based on the fuzzy cognitive map to comprehensively describe student progress on various aspects together with their causal relationships. These indicators are built on top of a student attribute matrix that models both performance and non-performance based student attributes, and a progress potential function that evaluates student achievement and development of such attributes. We have illustrated our method by using real academic performance data collected from 60 high school students. Experimental results show that our work can offer both teachers and students a better understanding on student progress.