Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Journal of Artificial Intelligence in Education
Creating an empirical basis for adaptation decisions
Proceedings of the 5th international conference on Intelligent user interfaces
Machine Learning
Using Bayesian Networks to Manage Uncertainty in Student Modeling
User Modeling and User-Adapted Interaction
A bayesian approach to modelling users' information display preferences
UM'05 Proceedings of the 10th international conference on User Modeling
Graphical data displays and database queries: helping users select the right display for the task
SG'05 Proceedings of the 5th international conference on Smart Graphics
Proceedings of the 2013 international conference on Intelligent user interfaces
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Twelve years of diagrams research
Journal of Visual Languages and Computing
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This paper describes the evaluation of ERST, an adaptive system which is designed to improve its users' external representation (ER) selection accuracy on a range of database query tasks. The design of the system was informed by the results of experimental studies. Those studies examined the interactions between the participants' background knowledge-of-external representations, their preferences for selecting particular information display forms, and their performance across a range of tasks involving database queries. The paper describes how ERST's adaptation is based on predicting users' ER-to-task matching skills and performance at reasoning with ERs, via a Bayesian user model. The model drives ERST's adaptive interventions in two ways – by 1. hinting to the user that particular representations be used, and/or 2. by removing from the user the opportunity to select display forms which have been associated with prior poor performance for that user. The results show that ERST does improve an individual's ER reasoning performance. The system is able to successfully predict users' ER-to-task matching skills and their ER reasoning performance via its Bayesian user model.