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.

This paper draws from cognitive psychology and cognitive neuroscience to develop a preliminary similarity-choice theory of how people allocate attention among information patches on webpages while completing search tasks in complex informational websites. Study 1 applied stepwise multiple regression to a large dataset and showed that success rate for web navigation tasks approaches 100% if a single information patch is highly similar in meaning to the user goal, and success rate falls dramatically if two or more information patches compete for the user's attention and if only one contains a link that leads to accomplishing the search goal. Study 2 (n = 82) manipulated the independent variables task difficulty and website design and found statistically significant differences in success rate for both main effects and the interaction. Study 2 also found that the distribution of attention among available information patches was strongly determined by the rank ordering of semantic similarity between user goal and information patch but was not influenced by website designs with very different visual layouts. While these results offer verification of a similarity-choice theory of attention to information patches, caution is warranted in generalising too broadly from these results.

The Cognitive Walkthrough for the Web (CWW) is a partially automated usability evaluation method for identifying and repairing website navigation problems. Building on five earlier experiments [3,4], we first conducted two new experiments to create a sufficiently large dataset for multiple regression analysis. Then we devised automatable problem-identification rules and used multiple regression analysis on that large dataset to develop a new CWW formula for accurately predicting problem severity. We then conducted a third experiment to test the prediction formula and refined CWW against an independent dataset, resulting in full cross-validation of the formula. We conclude that CWW has high psychological validity, because CWW gives us (a) accurate measures of problem severity, (b) high success rates for repairs of identified problems (c) high hit rates and low false alarms for identifying problems, and (d) high rates of correct rejections and low rates of misses for identifying non-problems.

Methods for identifying usability problems in web page designs should ideally also provide practical methods for repairing the problems found. Blackmon et al. [2] proved the usefulness of the Cognitive Walkthrough for the Web (CWW) for identifying three types of problems that interfere with users' navigation and information search tasks. Extending that work, this paper reports a series of two experiments that develop and prove the effectiveness of both full-scale and quick-fix CWW repair methods. CWW repairs, like CWW problem identification, use Latent Semantic Analysis (LSA) to objectively estimate the degree of semantic similarity (information scent) between representative user goal statements (100-200 words) and heading/link texts on each web page. In addition to proving the effectiveness of CWW repairs, the experiments reported here replicate CWW predictions that users will face serious difficulties if web developers fail to repair the usability problems that CWW identifies in web page designs [2].

This paper proposes a transformation of the Cognitive Walkthrough (CW), a theory-based usability inspection method that has proven useful in designing applications that support use by exploration. The new Cognitive Walkthrough for the Web (CWW) is superior for evaluating how well websites support users' navigation and information search tasks. The CWW uses Latent Semantic Analysis to objectively estimate the degree of semantic similarity (information scent) between representative user goal statements (100-200 words) and heading/link texts on each web page. Using an actual website, the paper shows how the CWW identifies three types of problems in web page designs. Three experiments test CWW predictions of users' success rates in accomplishing goals, verifying the value of CWW for identifying these usability problems