The nature of statistical learning theory
The nature of statistical learning theory
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Evaluating Learning Behavior of Web-Based Training (WBT) Using Web Log
ICCE '02 Proceedings of the International Conference on Computers in Education
Is Computer-Based Learning Right for Everyone?
HICSS '99 Proceedings of the Thirty-Second Annual Hawaii International Conference on System Sciences-Volume 1 - Volume 1
SmartTutor: an intelligent tutoring system in web-based adult education
Journal of Systems and Software
Word identification for Mandarin Chinese sentences
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 1
Unknown word extraction for Chinese documents
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
A bottom-up merging algorithm for Chinese unknown word extraction
SIGHAN '03 Proceedings of the second SIGHAN workshop on Chinese language processing - Volume 17
Operations Research: An Introduction (8th Edition)
Operations Research: An Introduction (8th Edition)
Guest editorial vapnik-chervonenkis (vc) learning theory and its applications
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Research on Personalized E-Learning System Using Fuzzy Set Based Clustering Algorithm
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007
An Overview on Mobile E-Learning Research of Domestic and Foreign
ICWL '08 Proceedings of the 7th international conference on Advances in Web Based Learning
Temporal learning analytics for computer based testing
Proceedings of the Fourth International Conference on Learning Analytics And Knowledge
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In recent years, designing useful learning diagnosis systems has become a hot research topic in the literature. In order to help teachers easily analyze students' profiles in intelligent tutoring system, it is essential that students' portfolios can be transformed into some useful information to reflect the extent of students' participation in the curriculum activity. It is observed that students' portfolios seldom reflect students' actual studying behaviors in the learning diagnosis systems given in the literature; we thus propose three kinds of learning parameter improvement mechanisms in this research to establish effective parameters that are frequently used in the learning platforms. The proposed learning parameter improvement mechanisms can calculate the students' effective online learning time, extract the portion of a message in discussion section which is strongly related to the learning topics, and detect plagiarism in students' homework, respectively. The derived numeric parameters are then fed into a Support Vector Machine (SVM) classifier to predict each learner's performance in order to verify whether they mirror the student's studying behaviors. The experimental results show that the prediction rate for the SVM classifier can be increased up to 35.7% in average after the inputs to the classifier are ''purified'' by the learning parameter improvement mechanisms. This splendid achievement reveals that the proposed algorithms indeed produce the effective learning parameters for commonly used e-learning platforms in the literature.