Taiwan's semiconductor industry to foster the Government's Trillion, Twin Stars of the second focus of the development of high value-added industries, while Taiwan's semiconductor industry is that the strengths of high economic efficiency of the factory production management capabilities. This article attempts to study qualitative narrative approach to explore the internal semiconductor factory management factory guided practical side the core of the park will be the text in a large semiconductor factory production of new products in development issues in the two abnormal debug ( debug), the engineering units of the mutual interaction between the key issues discussed by the cases described in the story to explore the impact of the competitiveness of the semiconductor wafer manufacturing background causes of temporal and spatial dynamics of the paper try to explore new management theory with meaning to the new management. In this paper, the use of research methods repeated quenching of the chain and found three new management direction and way of thinking: (1) semiconductor factory in adaptive learning and inter-departmental communication is often to throw the issue of interoperability between the ball acts to the surface ball on the culture may appear to be due to cross-border barriers to the field of communication, in fact, the semiconductor industry this is a special kind of division of labor approach to the play of its mobile emergency power needs. (2) In practice, the real effective and valuable cross-border organization of adaptive learning often takes place in the "after the review of the object", new object collection by different departments of the adaptive learning and communication can smooth-going until the object was to clarify the boundaries. (3) in practice, whether novice or veteran engineers through the KM (knowledge benchmark) or OJB (on job training) will be ready a certain degree of "Sense of the object", but because the pressure of the workplace, emotional, responsibilities will Engineers do not have a conscious "Selected Sense", the deliberate lowering of objects Sense. In this paper, semiconductor plant by practical examples of projects dealing with abnormal events to re-find and explore the meaning of new management practices with a view to the future management of the semiconductor manufacturing plant competitiveness have contributed to analysis and improvement, and look forward to the future, Taiwan's semiconductor sustainable manufacturing plant, to maintain competitiveness and enhance the management of a new theoretical framework and practices.

There has recently been great promise and interest in the use of adaptive learning systems to provide personalized course content, tailored to the ability levels and pace of individual students. Yet, not all the technologies in this space provide the same capabilities. In this thesis I analyze a representative group of adaptive learning providers according to the pedagogical model of their design. Then, I discuss case studies of two systems to analyze their design according to a humanist design philosophy and a more cybernetic design tradition, and I conclude with a set of design guidelines and selection criteria for faculty and administrators interested in evaluating, selecting, and implementing an adaptive learning system that fits their pedagogical values.

The electronic mail has become one of the most popular communication channels in the modern world. Due to its convenience and low cost, however, many business salesmen utilize this channel to promote their products by distributing e-mails to people as far as they can reach, which causes troubles to irrelevant e-mail receivers. As a result, many a research has been devoted to filtering irrelevant e-mails based on data mining techniques to alleviate users¡¦ mental loadings in processing e-mails they receive. Nevertheless, current approaches have their own drawbacks. Issues on what appropriate classifies to construct, how to endow such classifiers with the adaptive learning ability, and how to customize the e-mail management process for each user are still under investigation. The objective of this research is therefore to construct an e-mail classifier with learning ability to self-correct from erroneous outcomes. Furthermore, we propose a customized e-mail management process that can handle users¡¦ e-mails based on their own preferences. Ultimately, it can adapt itself to the changes of users¡¦ preferences when handling their e-mails. Several experiments are conducted to verify the performance of the constructed classifier. The results show that our proposed classifier possesses high accuracy and high precision with outstanding adaptive learning ability. We also illustrate a real application of the customized e-mail management process. It shows that our approach can detect the changes of users¡¦ preferences and learn to follow the changes. The feasibility of employing our approach to constructing e-mail classifiers is thus justified.

The perception of incorporating digital information into online educational systems and the ideal of developing digital schools for lifelong learning have drawn much attention of the governments, academia, and industries around the world. The techniques of interactive learning have become a primary research topic in E-learning. However, most existing E-learning systems provide static instructional materials. The techniques of dynamic learning content management that adaptive to individual user knowledge level and learning goals have been tough challenges for the related research communities. The resulting repetitive and blind learning phenomena have significantly reduced user performance and motivation. We hypothesized that new algorithms of adaptive learning based on the integration of current information technologies, the use of fuzzy theory to express the uncertainty features of the user knowledge, and the exploitation of clustering techniques to analyze the knowledge of a user for the comprehended areas of the domain knowledge will effectively improve user satisfaction. In this study, a prototype system is developed, implemented, and experimented by using SCORM run-time environment. The knowledge of teaching domain and the features of the learner behavior are modeled by ontology to represent the hierarchy and relationship of the learning concepts. To quantify user knowledge and learning ability, fuzzy sets are applied with multiple analysis dimensions based on the pedagogical strategies and user learning experiences. The performance of a user for learning knowledge concepts is then evaluated. In particular, an algorithm is designed to extract the existing learning paths of a user by the relative position of the concepts that the user attains in the domain knowledge. Furthermore, the candidate direction types for recommending concepts are inferred and the candidate learning concepts that are appropriate or inappropriate to learn followed up can be identified by rules. Moreover, the candidate learning concepts are scheduled to construct customizable learning routes by clustering techniques. The personalized learning contents that best matching user learning intention would then be presented to the user. Simulations study in the uniform and normal distributions for the grades of users is conducted to evaluate the tutoring model for three levels of users. The experimental results show that the proposed model helps different levels of users to learn the domain knowledge effectively and the accuracy of recommending the relevant learning object contents is superior than the random selection method. With a richer description of user knowledge and features, the proposed adaptive system for online learning assistance may better diagnose the understanding of a learner and enhance the pertinence of the retrieved courses to user intended learning to improve the service quality for the user.

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In an eLearning context, Adaptive Hypermedia Systems have been developed to improve learning success by increasing learner satisfaction, learning speed, and educational effectiveness. However, creating adaptive eLearning content and structures is still a time consuming and complicated task, in particular if individual lecturers are the intended authors. The way of thinking that is needed to create adaptive structures as well as the workflows is one that lecturers are unaccustomed to. The aim of this research project is to develop a concept that helps authors create adaptive eLearning content and structures, which focuses on its applicability for lecturers as intended authors. The research is targeted at the sequencing of content, which is one of the main aspects of adaptive eLearning. To achieve this aim the problem has been viewed from the author’s side. First, in terms of complexity of thoughts and threads, explanations about content structures have been found in storytelling theory. It also provides insights into how authors work, how story worlds are created, story lines intertwined, and how they are all merged together into one content. This helps us understand how non technical authors create content that is understandable and interesting for recipients. Second, the linear structure of learning content has been investigated to extract all the information that can be used for sequencing purposes. This investigation led to an approach that combines existing models to ease the authoring process for adaptive learning content by relating linear content from different authors and therefore defining interdependencies that delinearise the content structure. The technical feasibility of the authoring methods for adaptive learning content has been proven by the implementation of the essential parts in a research prototype and by authoring content from real life lectures with the prototype’s editor. The content and its adaptive structure obtained by using the concept of this research have been tested with the prototype’s player and monitor. Additionally, authoring aspects of the concept have been shown along with practical examples and workflows. Lastly, the interviewees who took part in expert interviews have agreed that the concept significantly reduces authoring complexity and potentially increases the amount of lecturers that are able to create adaptive content. The concept represents the common and traditional authoring process for linear content to a large extent. Compared to existing approaches the additional work needed is limited, and authors do not need to delve into adaptive structures or other authors’ content structures and didactic approaches.

This thesis investigates the use of machine learning techniques in computer games to create a computer player that adapts to its opponent's game-play. This includes first confirming that machine learning algorithms can be integrated into a modern computer game without have a detrimental effect on game performance, then experimenting with different machine learning techniques to maximize the computer player's performance. Experiments use three machine learning techniques; static prediction models, continuous learning, and reinforcement learning. Static models show the highest initial performance but are not able to beat a simple opponent. Continuous learning is able to improve the performance achieved with static models but the rate of improvement drops over time and the computer player is still unable to beat the opponent. Reinforcement learning methods have the highest rate of improvement but the lowest initial performance. This limits the effectiveness of reinforcement learning because a large number of episodes are required before performance becomes sufficient to match the opponent.

I present an algorithm which allows two agents to generate a simple language based only on observations of a shared environment. Vocabulary and roles for the language are learned in linear time. Communication is robust and degrades gradually as complexity increases. Dissimilar modes of experience will lead to a shared kernel vocabulary.

The main objective of this research is to adopt SCORM SSS (Simple Sequence Specification) standard to develop an adaptive learning system and to access its effectiveness for adaptive learning. The dependent variable of this research is the learners¡¦ learning outcomes, including three constructs that are the learning performance, satisfaction and learning efficiency. The subjects of this research are two classes with total of 61 students in the fifth grade of elementary school, the experiment group has 31 students, and the control group has 30 students. Experiment learning topic is decimal division in mathematics of primary school. The learning materials are designed by a professional teacher in the field of mathematics domain. The concept map and instructional flow chart were used to guide the content design. The result has found that the adaptive learning not only has the same level of learning performance, but also has a higher learning efficiency comparing with the traditional learning. The contributions of this research are; to provide an exemplification of how to design an adaptive learning course using SCORM SSS compliant LMS; to show the advantage that learners don¡¦t have to strict on a fixed learning path like in a traditional courses; and to demonstrate the potential for those competent students who can use less time to complete normal tasks and hence get chances to explore more in depth knowledge.

This dissertation studies monetary-fiscal policy interactions and adaptive learning applications in regime-switching DSGE models. A common thread through my research is understanding how policymakers may be affected by the interaction of policy regime change and agents' beliefs about past, current or future policy in general equilibrium. The work I present in this dissertation shows that conventional and unconventional policy outcomes, as well as the existence, uniqueness and expectational stability of rational expectations solutions, depend heavily on the expectational effects of time-varying policy. These findings suggest that uncertainty over future fiscal policy may curb the effectiveness of monetary policy, or otherwise constrain the actions of central bankers. In carrying out this research agenda, my work also examines the relationship between determinacy and expectational stability in a general class of Markov-switching DSGE models.