Introduction to Website Evaluation
In the previous module, you learned about the Technical team's communication process with the client and the other team members.
In this module, you will learn how to evaluate a site using the Web interaction model.
In the next lesson, you will learn what Nielsen's four criteria for a successful Web site are.
When you are finished with this module, you should be able to
- Identify Nielsen's four criteria for a successful Web site.
- Describe the characteristics of an effective information architecture.
- Describe the characteristics of effective signs and metaphors.
- Describe the several functional elements that should be tested hen evaluating a Web site.
- Identify the goals and trends of usability testing.
- Describe how to evaluate and compare competing Web sites.
User interfaces (UI) are more and more required to support several contexts-of-use.
They need to be able to be run on several platforms, consider different types of users and adapt to various usage situations. This poses new challenges when it comes to the development of interactive applications as well as their evaluation. In this work we present our approach combining a model-based runtime system with an (AUE) Automated Usability Evaluation tool to provide the ability to evaluate UIs that adapt at runtime. In order to attend to these issues we combined two approaches: The Mental
Models workbench, a workbench for AUE and the (MASP)Multi-Access Service Platform, a model-based framework for UI generation. Model-based UI development approaches already support the generation of multi-platform user-interfaces as well as context-of-use adaptation. They contain semantics stored in a well-structured form of declarative design models. This allows tools to assist developers at design-time by detecting questionable features and by offering the help of automated advisors. However, most of these approaches do not consider ad-hoc adaptation to the context-of-use which can only be calculated at runtime. The MASP allows the derivation of a UI from a set of executable models, defining the user interface state. Besides having the possibility to describe adaptive UIs, the models and the state information can also be utilized to support the AUE of the UI. By working with abstract UI models, which ideally contain all required concepts, the UIs could be generated for any platform and thus usability evaluation could be done by considering any platform without being forced to redefine the concepts of the evaluation target.