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Recent research on creativity has put great emphasis on how multiple systems influence an individual¡¦s development in creativity. The main purposes of this study were (a) to understand the current situation of pupils¡¦ explanatory style, domain knowledge, creative life experience, and technological creativity; (b) to investigate the effects of pupils¡¦ grade, explanatory style, domain knowledge, and creative life experience on their technological creativity; and (c) to analyze the predictive power of pupils¡¦ grade, explanatory style, domain knowledge, creative life experience on their ability group membership of technological creativity. The participants included 418 third to sixth graders sampling from six elementary schools in Taipei City. The employed instruments were The Questionnaire of Children¡¦s Explanatory Style, The Questionnaire of Creative Life Experience, The Test of Technological Creativity, and the scores on the final exam of Science and Living Technology Areas. The employed analysis methods were Descriptives, ANOVA, one-way and two-way MANOVA, Canonical Correlation Analysis, and Discriminant Analysis. The main findings in this study were as follows: 1. There were gender differences on the pupils¡¦ performance of explanatory style, creative life experience, and technological creativity; more specifically, the girls outperformed the boys on explanatory style, creative life experience, and technological creativity. 2. There were grade differences on the pupils¡¦ explanatory style. 3. Although no significant interaction effect of grade ´ explanatory style on technological creativity was found, there were significant main effects of grade on the pupils¡¦ technological creativity. 4. Domain knowledge contributed to the pupils¡¦ performance on technological creativity. 5. Creative life experience had significant effects on the pupils¡¦ technological creativity. Among the indices of both sides, ¡§language and performing arts¡¨ had the highest correlation with technological creativity. 6. Grade, explanatory style, domain knowledge, and creative life experience could effectively predict the pupils¡¦ ability group membership of technological creativity, and grade as well as domain knowledge had better predictive power. Finally, some suggestions were proposed for teachers, relative educational institutions, parents, and further studies.

Data mining is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. It is widely acknowledged that the role of domain knowledge in the discovery process is essential. However, the synergy between domain knowledge and data mining is still at a rudimentary level. This motivates me to develop a framework for explicit incorporation of domain knowledge in a data mining system so that insights can be drawn from both data and domain knowledge. I call such technology "semantic data mining." Recent research in knowledge representation has led to mature standards such as the Web Ontology Language (OWL) by the W3C's Semantic Web initiative. Semantic Web ontologies have become a key technology for knowledge representation and processing. The OWL ontology language is built on the W3C's Resource Description Framework (RDF) that provides a simple model to describe information resources as a graph. On the other hand, there has been a surge of interest in tackling data mining problems where objects of interest can be best described as a graph of interrelated nodes. I notice that the interface between domain knowledge and data mining can be achieved by using graph representations. Therefore I explore a graph-based approach for modeling both knowledge and data and for analyzing the combined information source from which insight can be drawn systematically. In summary, I make three main contributions in this dissertation to achieve semantic data mining. First, I develop an information integration solution based on metaheuristic optimization when data mining task require accessing heterogeneous data sources. Second, I describe how a graph interface for both domain knowledge and data can be structured by employing the RDF model and its graph representations. Finally, I describe several graph theoretic analysis approaches for mining the combined information source. I showcase the utility of the proposed methods on finding semantically associated itemsets, a particular case of the frequent pattern mining. I believe these contributions in semantic data mining can provide a novel and useful way to incorporate domain knowledge. This dissertation includes published and unpublished coauthored material.

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Doctor of Philosophy / Department of Psychological Sciences / Richard Jackson Harris / In recent years, Baddeley (2010) has added a new component, the episodic buffer, to his Working Memory (WM) model. The episodic buffer binds information from long-term memory (LTM) to the central executive but has been researched very little, especially with respect to its use with a second language. In fact, Juffs and Harrington (2011) stated, “To date there has been no research on the possible role of the episodic buffer in L2 learning and use” (p. 140). One goal of this study was to do just that. Domain knowledge (DK) in baseball (Experiment 1) and English proficiency levels (Experiment 2) were used as proxies for difficulty level to study how inference processing under different conditions of domain knowledge and cognitive load in native (L1) and non-native (L2) English readers contribute to understanding the episodic buffer. In Experiment 1, 67 participants varying in domain knowledge about baseball read stories related to baseball with or without a concurrent cognitive load task of responding to tones while reading; they then answered comprehension questions of varying degrees of inferential difficulty. In Experiment 2, three groups varying in English reading proficiency, split into groups based on their lexical decision task scores (72 native, 40 intermediate, 40 beginner readers) read general stories with or without cognitive load and answered comprehension questions requiring varying degrees of inferential difficulty. Accuracy and Reaction Time (RT) were differentially affected by working memory (OSpan), cognitive load, and inferential complexity. In Experiment 1, greater DK explained variance in effectiveness (accuracy) and efficiency (RT) as inferential complexity increased. In Experiment 2 OSpan was needed even at lower levels of inferential complexity for beginning readers. Surprisingly, for both experiments, participants responded faster under cognitive load conditions, although not at the expense of accuracy. This suggests that the episodic buffer is important for different levels of DK and proficiency, especially as the task becomes more difficult. Theoretical and practical implications of these findings are discussed.

Well developed innovation processes are becoming an essential component to the continued success of a large number of industries. Such processes build upon the evolutionary steps taken to advance innovation. In light of the need for innovation, companies and engineers must create the most efficient processes for their systems or product development teams. A step toward the creation of such processes, as well as the corresponding teaching of such processes in higher-education, is the development of a baseline of current best practices. This paper considers a contribution to this effort in the form of a study of a specific group of innovation practitioners. The study was created to probe a group of leaders in the engineering design domain using technical, demographic, and short answer questions. Various analysis methods are used to obtain a fundamental view of the answers to the questions but also the demographics of the participant group. Two deductive analysis methods are used, the first a set of hypotheses are explored from participant responses, and second a qualitative technique to understand links in the short answer portion of the study. An additional inductive approach is used, consisting of a correlating approach to compare responses to questions and understand trends across the participants. Results from the analysis emphasize the current perceptions of innovation by the participants and opportunities to refine our search for better innovation practices. / text

The educational benefits of involving learners as active players in the learner modelling process have been an important motivation for research on this form of learner modelling, henceforth referred to as participative learner modelling. Such benefits, conceived as the promotion of learners' reflection on and awareness of their own knowledge, have in most cases been asserted on the grounds of system design and supported only by anecdotal evidence. This dissertation explores the issue of whether participative learner modelling actually promotes learners' reflection and awareness. It does so by firstly interpreting 'reflection' and 'awareness' in light of "classical" theories of human cognitive architecture, skill acquisition and meta-cognition, in order to infer changes in learner abilities (and therefore behaviour) amenable to empirical corroboration. The occurrence of such changes is then tested for an implementation of a paradigmatic form of participative learner modelling: allowing learners to inspect and modify their learner models. The domain of application centres on the sensorimotor skill of controlling a pole on a cart and represents a novel type of domain for participative learner modelling. Special attention is paid to evaluating the method developed for constructing learner models and the form of presenting them to learners: the former is based on a method known as behavioural cloning for acquiring expert knowledge by means of machine learning; the latter deals with the modularity of the learner models and the modality and interactivity of their presentation. The outcome of this research suggests that participative learner modelling may increase the abilities of learners to report accurately their problem-solving knowledge and to carry out novel tasks in the same domain—the sort of behavioural changes expected from increased learners' awareness and reflection. More importantly perhaps, the research suggests a viable methodology for examining the educational benefits of participative learner modelling. It also exemplifies the difficulties that such endeavours will face.

In contemporary software development, an emergent understanding of the problem domain and envisioned goals forms the basis of designing, testing and development activities. Lack of a common understanding of the domain can result in costly rework or client dissatisfaction. Research shows that the development of shared understanding in this context is a complex and error-prone process and there is room for improvement. Is this because practitioners are not following suggested practice from literature? Or are the actual barriers to shared understanding not being addressed by current tools and techniques? Is the development of shared domain understanding even viewed as problematic (or even important) by practitioners? These are some questions that need to be investigated in order to effectively design process improvements and tool support in this area, yet there is little information related to this. This study takes a multi-case study approach, which incorporate semi-structured interviews with representative from ten small-to-medium organisations. This study focuses on the vendor’s perspective and includes a mix of application domains. Result of the interviews is analysed to discover themes and patterns related to an analysis framework constructed from the literature review. The findings indicate that vendors perceive the process of developing shared application-domain understanding with their clients as being both problematic and important to a successful implementation. Twelve barriers have been identified from the analysis. The results also confirm that the process of sharing understanding development is generally perceived as being evolutionary and collaborative. This process is described by most interviewees comprises iterative phases of elicitation, confirmation and refinement of the understanding. A definite preference for face-to-face interaction is evident at regular times throughout development, particularly in early stages, although the importance of ad-hoc communications by phone or email, as domain knowledge needs arise, is also emphasised. Access to cooperative domain-expert throughout development is generally seen as a critical success factor. Several companies report using in-house domain-expert as client “proxies” in this regard. There is a mix of attitudes apparent regarding the direct communications of developers with client stakeholders. This ranged from insisting that developers are involved from initial elicitation and “kick-off” meetings, to “shielding” developers almost entirely from client. In terms of representations of understanding, participants relate natural-language, screen-shots, mock-ups, prototypes and product-demonstrations as the most useful artefacts for sharing and confirming understanding of the problem domain. They emphasise the importance of flexibility and client familiarity with the representations. In general, there is no clear separation between problem and solution spaces evident when the interviewees discussed representations of understanding, and the preference seems to be for concrete rather than abstract representations. In conclusion, comparisons between the findings and literature generally confirm contemporary thinking regarding domain knowledge sharing, although a number of barriers were given particular emphasis in this field study. The use of computer-based tool support is not widespread and the need to improve the domain knowledge sharing process and tool support in practice is widely acknowledged by the participants in this investigation. This study has identified some fruitful areas of research in this regard.

<p>In this thesis a new concept called domain knowledge management for informationproviding dialogue systems is introduced. Domain knowledge management includes issues related to representation and use of domain knowledge as well as access of background information sources, issues that previously have been incorporated in dialogue management.</p><p>The work on domain knowledge management reported in this thesis can be divided in two parts. On a general theoretical level, knowledge sources and models used for dialogue management, including domain knowledge management, are studied and related to the capabilities they support. On a more practical level, domain knowledge management is examined in the contexts of a dialogue system framework and a specific instance of this framework, the ÖTRAF system. In this system domain knowledge management is implemented in a separate module, a Domain Knowledge Manager.</p><p>The use of a specialised Domain Knowledge Manager has a number of advantages. The first is that dialogue management becomes more focused as it only has to consider dialogue phenomena, while domain-specific reasoning is handled by the Domain Knowledge Manager. Secondly, porting of a system to new domains is facilitated since domain-related issues are separated out in specialised domain knowledge sources. The third advantage with a separate module for domain knowledge management is that domain knowledge sources can be easily modified, exchanged, and reused.</p> / Report code: LiU-Tek-Lic-2001:27.