Optimizing and profiling users online with Bayesian probabilistic modeling
Petri Nokelainen, Kirsi A. Tirri, Miikka Miettinen, Tomi Silander, Jaakko Kurhila · 2002
One solution to build adaptive educational material is to model the user with a questionnaire before he/she enters the system, and then use this information to carry out adaptation of the platform. For example, users that are profiled could be offered personalised links to resources based on their metacognitive strategies or intrinsic goal orientations. These machine understandable beliefs of the profiles of different users could then be updated by collecting additional information with on-line questionnaire in regular intervals. An adaptive on-line questionnaire system EDUFORM is based on intelligent techniques that optimize the number of propositions presented to each respondent. In addition EDUFORM creates an individual profile for each respondent. The adaptive graphical user interface is generated automatically (e.g., propositions in the questionnaire, collaborative actions and links to resources), and profile analysis and the related selection of order of the propositions is performed with Bayesian probabilistic modeling. Preliminary testing implies that the obvious advantage with EDUFORM is that the questionnaires are usually significantly shorter compared to traditional nonadaptive questionnaires. The empirical results show that after reducing dramatically the number of propositions (from 50-60%) one is still able to control the error ratio (12-22%). In the context of course feedback from a web-based course, the model construction in the Profile creation phase can offer he1p for teachers to find differences among the various learner groups so that different versions of the web course can be prepared to suit the individual needs of the group. The correct profile information of the respondent is in most cases obtained already with less than 33% of the original prop...