CogTool-Explorer 1.2 (CTE1.2) predicts novice exploration behavior and how it varies with different user-interface (UI) layouts. CTE1.2 improves upon previous models of information foraging by adding a model of hierarchical visual search to guide foraging behavior. Built within CogTool so it is easy to represent UI layouts, run the model, and present results, CTE1.2's vision is to assess many design ideas at the storyboard stage before implementation and without the cost of running human participants. This paper evaluates CTE1.2 predictions against observed human behavior on 108 tasks (36 tasks on 3 distinct website layouts). CTE1.2's predictions accounted for 63-82% of the variance in the percentage of participants succeeding on each task, the number of clicks to success, and the percentage of participants succeeding without error. We demonstrate how these predictions can be used to identify areas of the UI in need of redesign.

In this paper, we report results on answering questions for the reading comprehension task, using a machine learning approach. We evaluated our approach on the Remedia data set, a common data set used in several recent papers on the reading comprehension task. Our learning approach achieves accuracy competitive to previous approaches that rely on hand-crafted, deterministic rules and algorithms. To the best of our knowledge, this is the first work that reports that the use of a machine learning approach achieves competitive results on answering questions for reading comprehension tests.