C4.5: programs for machine learning
C4.5: programs for machine learning
A secure on-line submission system
Software—Practice & Experience
A tutoring system for parameter passing in programming languages
Proceedings of the 7th annual conference on Innovation and technology in computer science education
A study of the effects of bias in criterion functions for temporal data clustering
Proceedings of the 43rd annual Southeast regional conference - Volume 1
Intelligent tutoring system for CS-I and II laboratory
Proceedings of the 44th annual Southeast regional conference
Modeling student online learning using clustering
Proceedings of the 44th annual Southeast regional conference
Activity sequence modelling and dynamic clustering for personalized e-learning
User Modeling and User-Adapted Interaction
| Hi-index | 0.00 |
Finding the optimal teaching strategy for an individual student is difficult even for an experienced teacher. Identifying and incorporating multiple optimal teaching strategies for different students in a class is even harder. This paper presents an Adaptive tutor for online Learning, AtoL, for Computer Science laboratories that identifies and applies the appropriate teaching strategies for students on an individual basis. The optimal strategy for a student is identified in two steps. First, a basic strategy for a student is identified using rules learned from a supervised learning system. Then the basic strategy is refined to better fit the student using models learned using an unsupervised learning system that takes into account the temporal nature of the problem solving process. The learning algorithms as well as the initial experimental results are presented.