C4.5: programs for machine learning
C4.5: programs for machine learning
User Modeling and User-Adapted Interaction
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Personalized Courseware Construction Based on Web Data Mining
WISE '00 Proceedings of the First International Conference on Web Information Systems Engineering (WISE'00)-Volume 2 - Volume 2
Unsupervised and supervised machine learning in user modeling for intelligent learning environments
Proceedings of the 12th international conference on Intelligent user interfaces
Expert Systems with Applications: An International Journal
International Journal of Human-Computer Studies
Educational data mining: A survey from 1995 to 2005
Expert Systems with Applications: An International Journal
Adaptive feedback generation to support teachers in web-based distance education
User Modeling and User-Adapted Interaction
Adaptive and Intelligent Web-based Educational Systems
International Journal of Artificial Intelligence in Education
Supporting teachers in collaborative student modeling: A framework and an implementation
Expert Systems with Applications: An International Journal
Intelligent assistance for teachers in collaborative e-learning environments
Computers & Education
Mining LMS data to develop an "early warning system" for educators: A proof of concept
Computers & Education
Enhancing E-Learning Through Teacher Support: Two Experiences
IEEE Transactions on Education
Review: Student modeling approaches: A literature review for the last decade
Expert Systems with Applications: An International Journal
Review: Educational data mining: A survey and a data mining-based analysis of recent works
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Adaptive educational systems (AESs) guide students through the course materials in order to improve the effectiveness of the learning process. However, AES cannot replace the teacher. Instead, teachers can also benefit from the use of adaptive educational systems enabling them to detect situations in which students experience problems (when working with the AES). To this end the teacher needs to monitor, understand and evaluate the students' activity within the AES. In fact, these systems can be enhanced if tools for supporting teachers in this task are provided. In this paper, we present the experiences with predictive models that have been undertaken to assist the teacher in PDinamet, a web-based adaptive educational system for teaching Physics in secondary education. Although the obtained models are still very simple, our findings suggest the feasibility of predictive modeling in the area of supporting teachers in adaptive educational systems.