One of the main problems of collaborative filtering recommenders is the sparsity of the ratings in the users-items matrix, and its negative effect on the prediction accuracy. This ...
Using an eye-tracker, this paper investigates the information that learners visually attend to in their open learner model, and the degree to which this is related to the method of...
Student models for Intelligent Computer Assisted Language Learning (ICALL) have largely focused on the acquisition of grammatical structures. In this paper, we motivate a broader p...
: SimStudent is a machine-learning agent that learns cognitive skills by demonstration. It was originally developed as a building block of the Cognitive Tutor Authoring Tools (CTAT...
Noboru Matsuda, William W. Cohen, Jonathan Sewall,...
A standing question in the field of Intelligent Tutoring Systems and User Modeling in general is what is the appropriate level of model granularity (how many skills to model) and h...
Zachary A. Pardos, Neil T. Heffernan, Brigham Ande...
User’s privacy concerns represent one of the most serious obstacles to the wide adoption of mobile social software applications. In this paper, we introduce a conceptual model wh...
Abstract. Recommender systems produce social networks as a side effect of predicting what users will like. However, the potential for these social networks to aid in recommending i...
Web personalization has demonstrated to be advantageous for both online customers and vendors. However, its benefits may be severely counteracted by privacy constraints. Personaliz...
Despite the growing popularity of user modeling servers, little attention has been paid to optimizing and evaluating the performance of these servers. We argue that implementation ...
Michael Yudelson, Peter Brusilovsky, Vladimir Zado...