1. Introduction In this dissertation, we explore interactivity mechanisms based on event system model for personalizable and intelligently adaptive learning environment architecture over distributed system, which utilizes a two-way communications channel for information delivery. Specifically, a web-based system is here considered. The relevant research areas are human computer interface (HCI), cybernetics (Pask 1961), cognitive psychology (Carroll 1997), human factors engineering and education (Adams 1976; Howe 1977). The work is inherently interdisciplinary, yet subsumed entirely under a computer science framework, since interactivity is measured quantitatively as well as modeled in the context of architectures defined within computer science. Following this Introduction, Chapter 2 describes the system in general. It will explain system components, interactivity, and the learning system. Chapter 3 discusses interactivity and event. Chapter 4 describes the Smart Desk interactive learning environment; this chapter also presents learning system issues in more detail. Chapter 5 presents a methodology for measuring interactivity by detecting events in learning environments. We summarize on results in Chapter 6 and present the conclusion in Chapter 7. 1.1 Motivation and Background Human Computer Interface (HCI) is a relatively new research area in computer science. HCI deals with how people interact with computers so as to achieve better task performance; naturally it covers diverse subjects. Often, it has been an empirical project from the outset. Indeed, HCI studies provided useful data through explorations of user behavior and modeling. However, with respect to the disciplinary concerns of computer science, older methodologies do not fit well for HCI and can cause legitimacy problems for HCI’s positioning within a computer science framework(Wegner 1997). My approach is to implement an event system model to measure the interactivity parameters of a learning system. It focuses on system rather than on users. A controller, as a subsystem between a user and a learning system, provides a flexible method to measure the interactivity of a particular learning environment. In this work, accessibility is distinguished from interactivity, while in various contexts, two concepts are interchangeably used. For instance, a highly interactive system usually has good accessibility but it is not essential, and even noticeably accessible system can have few interactive features. In working with particular human subjects, we have been researching computerized learning tools and performance evaluation software (Kane and Kay 1992; Rodrigues and Viera da Rocha 1997) not only from computer science but also from neuropsychology and education. Computerized performance evaluation tools are a new (Kane and Kay 1992), but rapidly growing field both as products and research areas. These tools exploit computational resources to yield accurate measurements of motor skills, or user response times, for example. Some of them provide primitive user customizable environments. These, however, are limited because they cannot be changed dynamically and are seldomly customizable to specific user situations. Computerized learning tools or software are in similar circumstances. For the general public, ordering customized software is not easy, because the software market is supported by mass production products and services. For software developers to cope with every possible scenario and event for different users is practically impossible within a conventional programming model. User performance keeps changing and the complexity of considering all the possibilities is not polynomial, nondeterministic polynomial complexity (NP). This is precisely where an interaction model of programming is able to facilitate the embedding of future growth requirements and flexibilities for specific users(Wegner 1997). SmartDesk (see Figure 1-1) was developed to address exactly these questions. Built-in tracking tools and analysis for both sever and client sides provide a basis for measuring user performance. Interactivity is one of the most important structural variable in the learning environment. In the remainder of this paper, event detection methodology for measuring interactivity is developed and the Smart Desk environment is used for an experiment. Finally, interactivity in web applications is a preeminent concern for computer science work. The Internet is conceived of as an inherently interactive medium, yet with its short history and unprecedented technological status the old programming models are simply obsolete. Development of interactivity structures in this work are mainly done with a view to web based applications, which is generally built on client-server model. It leads to inevitable discussion of both client and server sides event detection and analyses here. 1.2 Thesis Statement and Contribution Detection of user events can provide solid basis to measure interactivity within learning environment. User events detection is mainly approached with methodology of Discrete Event System (DES) and explained in the same frame. This new approach has its significance in utilizing DES modeling so as to give quantitative data analysis of interactivity issues in a learning environment that is susceptible to subjective and biased analysis. Discussed topics in this work are at the border of computer science and cognitive science. In general, the latter provided frame works by its principles and the former, tools to model and analyze the system. Also intelligently interactive learning environment mechanisms are presented. In comparison to other models based on static and non-customizable theories and technologies, new mechanisms provide a superior prototype and system architecture as a learning model. Such an environment includes theoretical formulation of interactivity quantification based on the event model. Within the frame, first, interactivity at adaptive and personalizable learning environments is compared to opposite cases; secondly, other interactivity measurement is compared with the suggested event model coupled with content-based analysis. Finally, personalization and adaptive mechanism issues themselves that are inseparable in an educational system are discussed. Web based systems which can be accessed and used by anyone in any place is similar in its social impact to what J. Gutenberg's printing press and movable type in 15th century contributed to public education, in a sense that web based learning environments can bring good quality information to the public.