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ERIC Number: ED560791
Record Type: Non-Journal
Publication Date: 2015-Jun
Pages: 4
Abstractor: As Provided
Reference Count: 10
A Transfer Learning Approach for Applying Matrix Factorization to Small ITS Datasets
Voß, Lydia; Schatten, Carlotta; Mazziotti, Claudia; Schmidt-Thieme, Lars
International Educational Data Mining Society, Paper presented at the International Conference on Educational Data Mining (EDM) (8th, Madrid, Spain, Jun 26-29, 2015)
Machine Learning methods for Performance Prediction in Intelligent Tutoring Systems (ITS) have proven their efficacy; specific methods, e.g. Matrix Factorization (MF), however suffer from the lack of available information about new tasks or new students. In this paper we show how this problem could be solved by applying Transfer Learning (TL), i.e. combining similar but not equal datasets to train Machine Learning models. In our case we obtain promising results by combining data collected of German fractions' tasks (517 interactions, 88 students, 20 tasks) with their nonexact translation of a previously American US version (140 interactions, 14 students, 16 tasks). In order to do so we also analyze the performance of MF based predictors on smaller ITS' samples evaluating their usefulness. [To view the complete proceedings, see ED560503.]
International Educational Data Mining Society. e-mail:; Web site:
Publication Type: Reports - Research; Speeches/Meeting Papers
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: International Educational Data Mining Society
Identifiers - Location: Germany