Cocea, M., & Weibelzahl, S. (2007). Eliciting motivation knowledge from log files towards motivation diagnosis for Adaptive Systems. In C. Conati, K. McCoy & G. Paliouras (Eds.), User Modeling 2007. Proceedings of 11th International Conference, UM2007, Lecture Notes in Artificial Intelligence LNAI 4511 (© Springer Verlag) (pp. 197-206). Berlin: Springer
DOI: 10.1007/978-3-540-73078-1_23
Motivation is well-known for its importance in learning and its influence on cognitive processes. Adaptive systems would greatly benefit from having a user model of the learner's motivation, especially if integrated with information about knowledge. In this paper a log file analysis for eliciting motivation knowledge is presented, as a first step towards a user model for motivation. Several data mining techniques are used in order to find the best method and the best indicators for disengagement prediction. Results show a very good level of prediction: around 87% correctly predicted instances of all three levels of engagement and 93% correctly predicted instances of disengagement. Data sets with reduced attribute sets show similar results, indicating that engagement level can be predicted from information like reading pages and taking tests, which are common to most e-Learning systems.
@InProceedings{cocea-um2007,
author = {Mihaela Cocea and Stephan Weibelzahl},
title = {Eliciting Motivation Knowledge from Log Files
towards Motivation Diagnosis for Adaptive Systems},
editor = {Cristina Conati and Kathleen McCoy and Georgios Paliouras},
booktitle = {User {M}odeling. {P}roceedings of 11th
{I}nternational {C}onference, {UM2007}, 25-29 {J}une 2007},
publisher = {Springer},
address = {Berlin},
year = {2007},
pages = {197--206},
doi = {10.1007/978-3-540-73078-1\_23}
}