@article{Schatten_Janning_Schmidt-Thieme_2015, title={Integration and Evaluation of a Matrix Factorization Sequencer in Large Commercial ITS}, volume={29}, url={https://ojs.aaai.org/index.php/AAAI/article/view/9380}, DOI={10.1609/aaai.v29i1.9380}, abstractNote={ <p> Correct evaluation of Machine Learning based sequencers require large data availability, large scale experiments and consideration of different evaluation measures. Such constraints make the construction of ad-hoc Intelligent Tutoring Systems (ITS) unfeasible and impose early integration in already existing ITS, which possesses a large amount of tasks to be sequenced. However, such systems were not designed to be combined with Machine Learning methods and require several adjustments. As a consequence more than a half of the components based on recommender technology are never evaluated with an online experiment. In this paper we show how we adapted a Matrix Factorization based performance predictor and a score based policy for task sequencing to be integrated in a commercial ITS with over 2000 tasks on 20 topics. We evaluated the experiment under different perspectives in comparison with the ITS sequencer designed by experts over the years. As a result we achieve same post-test results and outperform the current sequencer in the perceived experience questionnaire with almost no curriculum authoring effort. We also showed that the sequencer possess a better user modeling, better adapting to the knowledge acquisition rate of the students. </p> }, number={1}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Schatten, Carlotta and Janning, Ruth and Schmidt-Thieme, Lars}, year={2015}, month={Feb.} }