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LEUVEN STATISTICS RESEARCH CENTRE (LSTAT) CELESTIJNENLAAN 200 B BOX 5307 3001 LEUVEN, BELGIË Thesis topic Title: Generalized additive models for online learning data OUR REFERENCE YOUR REFERENCE Name promoter: Wim Van den Noortgate LEUVEN Available for students from - Biometrics, Social, Behavioral and Educational Statistics, Business Statistics, General Statistical Methodology, All Round Statistics (indicate the profile for which this topic is suitable) Description: Indicate whether the working area is the KU Leuven or a company outside KU Leuven. In an item-based electronic learning environment, learners make short exercises (items) and are supposed to learn from the automatic feedback they get on their answers. An analysis of the logged responses can yield insight in the learning rate of users (and in item and person characteristics that influence this learning rate). To that end, dynamic ‘item response theory’ (IRT) models that include a time-related predictor can be used. A drawback of this approach is that the validity and accuracy of the results depend on the correctness of the model, this is on the way the relationship with time is modelled (e.g., a linear time trend). Yet, the learning pattern can be very irregular and unpredictable. Generalized additive models may help in modelling complex learning patterns, but as far as we know, this kind of models has not been used before for online learning data. In this thesis, we will explore the use of this approach, by analyzing existing data, and possibly by using simulations. The student is expected to be proficient in statistical software (e.g., R or SAS). Working place: KU Leuven AN CARBONEZ TEL. + 32 16 32 22 42 [email protected] lstat.kuleuven.be