Cognitive walkthrough for the web
Marilyn Blackmon2002
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Abstract
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.
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Repairing usability problems identified by the cognitive walkthrough for the web
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].
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Automating Web-Navigation Support Using a Cognitive Model
Proceedings of the 4th International Conference on Web Intelligence, Mining and Semantics (WIMS14), 2014
Reducing cognitive overhead is one way to ensure fast and easy web-navigation for users. We present here an automated navigation-support tool for websites. Whenever a user visits a website for searching some information, the tool will suggest relevant links to click, where they can find desired information on that website. The tool is based on a new cognitive model CoLiDeS++Pic, which combines two related models, CoLiDeS+ and CoLiDeS+Pic. The model computes semantic similarity between the user goal and the website information using latent semantic analysis technique. It also takes into account the path adequacy, performs appropriate backtracking if required, and uses semantic information from pictures. In the current implementation, the tool is not fully automated because the semantic features of pictures have to be obtained manually. To evaluate the effectiveness of the tool, we conducted an experiment with tool support and multi-tasking as independent variables. Statistical analysis showed a significant positive impact of the tool support for time needed to perform search tasks, disorientation in navigation and task-accuracy. The navigation performance in time and disorientation of the users and answering questions are improved when provided with the tool support. Multi-tasking had no effect on time needed or answering questions, but we observed an unexpected positive effect on disorientation, suggesting that perhaps the participants were more motivated to perform primary task due to the assigned secondary task (monitoring a video at the same time), which resulted in less disorientation.
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Contents Preface 5 1. Introduction 7 2. Web Navigation-Behavioral and Cognitive Perspectives 11 2.1. Instances and description of Web navigation behavior 11 2.1.1. What is the Web used for? Domains and tasks 12 2.1.2. The information space 13 2.1.3. What do users do? Web navigation behavior 14 2.2. Individual differences in Web navigation 16 2.3. Cognitive processes involved in Web navigation 16 2.3.1. Web navigation as a reading comprehension process 17 2.3.2. Web navigation as information foraging. Information Scent 18 2.3.3. Spatial cognition involved in Web navigation 19 2.3.4. Cognitive models of Web navigation 20 2.3.4.1. SNIF-ACT: Scent-based Navigation and Information Foraging in the ACT architecture 21 2.3.4.2. CoLiDeS: A Comprehension-based Linked model of Deliberate Search 21 2.3.4.3. MESA: Method for Evaluating Site Architectures 22 2.3.5. Value and limitations of existing theories and models of Web navigation 23 2.4. Practical constraints and opportunities 24 2.4.1. Web Human Factors and Web Usability 25 2.4.2. Navigation support 26 2.4.3. User models and personalization 27 3. Methodological Issues 29 3.1. Measuring and analyzing Web navigation behavior 29 3.1.1. On-line collecting and mining of Web navigation data 30 3.2. Cognitive modeling of Web navigation behavior 32 3.2.1. Modeling as a methodological approach 32 3.2.2. Common user versus individual differences 33 3.2.3. Cognitive modeling as source for support 33 3.2.4. Computational techniques: Latent semantic analysis (LSA) 35 4. Model Development and Empirical Studies 39 4.1. Task analysis 39 4.1.1. Task domains, instances and criteria 39 4.
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Information scent and web navigation: Theory, models and automated …
Human-Computer Interaction International …
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Paul van Schaik is a psychologist with an interest in human-computer interaction, and cognitive psychology; he is a Professor of Psychology in the School of Social Sciences and Law at Teesside University. Jonathan Ling is a psychologist with an interest in human-computer interaction, cognitive performance and perception; he is a research fellow in the Faculty of Applied Sciences at the University of Sunderland.
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