Contemporary organizations live in an environment of networks: internally, they manage the networks of employees, information resources, and knowledge assets to enhance productivity and improve efficiency; externally, they form alliances with strategic partners, suppliers, buyers, and other stakeholders to conserve resources, share risks, andgain market power. Many managerial and strategic decisions are made by organizations based on their understanding of the structure of these networks. This dissertation is devoted to network structure mining, a new research topic on knowledge discovery indatabases (KDD) for supporting knowledge management and decision making in organizations.A comprehensive computational framework is developed to provide a taxonomy and summary of the theoretical foundations, major research questions, methodologies,techniques, and applications in this new area based on extensive literature review. Research in this new area is categorized into static structure mining and dynamic structure mining. The major research questions of static mining are locating criticalresources in networks, reducing network complexity, and capturing topological properties of large-scale networks. An inventory of techniques developed in multiple reference disciplines such as social network analysis and Web mining are reviewed. These techniques have been used in mining networks in various applications including knowledge management, marketing, Web mining, and intelligence and security. Dynamic pattern mining is concerned with network evolution and major findings are reviewed.A series of case studies are presented in this dissertation to demonstrate how network structure mining can be used to discover valuable knowledge from various networks ranging from criminal networks to patent citation networks. Several techniques aredeveloped and employed in these studies. Performance evaluation results are provided to demonstrate the usefulness and potential of this new research field in supporting knowledge management and decision making in real applications.

Research in corrective feedback (CF) has developed over thirty years; however, little is known about how metalinguistic corrective feedback (MCF) assists the development of procedural knowledge. This quasi-experimental study sought to evaluate and compare the roles of oral MCF in proceduralizing English as a foreign language (EFL) learners’ declarative knowledge of gender referents and morphosyntactical structure—third person singular -s. Sixty college EFL learners participated in the study. They were randomly assigned to the experimental group and the control group. All learners participated in 14 communicative form-focused activities that elicited the use of gender referents and third person singular morpheme -s in regular classes over 14 weeks. The experimental group received MCF on errors in use of the target structures. The control group received no MCF in use of the target structures. Pretests, posttests, and long delayed posttests administered 20 weeks after the instructional treatment assessed the participants’ declarative knowledge and procedural knowledge of both target structures. The metalinguistic knowledge test was employed to measure declarative knowledge. The elicited oral imitation test was adopted to measure procedural knowledge. Repeated measures ANOVA results indicated that the MCF group significantly increased their procedural knowledge of both target structures. Cohen’s effect size d revealed that MCF exerted a bigger effect on proceduralizing EFL learners’ knowledge on third person singular morpheme –s than on gender referents. These results also demonstrated a relationship between the efficacy of oral MCF and the target structures as well as the interactional effect of test formats tapping into declarative knowledge and procedural knowledge. These findings shed light on the learnability of difficult structures through MCF and the necessity to employ metalanguage in EFL classrooms.

Cette thèse s'interroge sur la connaissance de soi émotionnelle, sur sa place ainsi que sa participation à la connaissance de soi, sur la valeur qu'elle a pour les individus ordinaires et sur la manière dont les individus ordinaires peuvent l'obtenir. L'examen de la nature des émotions et de la valeur que les individus accordent à la connaissance de leurs émotions, qui est effectué dans cette thèse, met en avant l'importance de l'acquisition de la connaissance de soi émotionnelle, ainsi que le lien que cette dernière est communément entendue entretenir avec le bonheur. Si l'acquisition de cette connaissance apparaît comme primordiale, elle n'en est pas pour autant facile. Elle requiert le déploiement d'un certain nombre de capacités cognitives, ainsi qu'un effort cognitif important, notamment au cours de l'utilisation de l'introspection. En effet, cette thèse discute exclusivement de l'accès introspectif à la connaissance de soi émotionnelle, et laisse de côté les autres formes d'accès à la connaissance de soi. Je m'interroge donc dans ce travail, sur la portée pratique mais aussi morale de l'utilisation de l'introspection au regard des émotions, en proposant une défense du processus introspectif comme d'un processus capable de permettre à un individu ordinaire de détecter, d'identifier et d'interpréter ses propres émotions. Cette thèse est donc axée autour de deux arguments principaux. Le premier de ces aruments octroie à la connaissance de soi émotionnelle, une place fondamentale au sein de la forme de connaissance de soi qui importe aux individus ordinaires. Le second défend l'idée selon laquelle l'introspection offre un accès à cette connaissance émotionnelle, notamment par le biais de l'appréhension qu'elle permet de la phénoménologie des émotions, et que cet accès a une fiabilité minimale et donc une valeur épistémique, même faible, ainsi qu'une valeur morale et intrinsèque. / This thesis examines emotional self-knowledge, its place as well as its participation in self-knowledge, the value it has for ordinary individuals and how ordinary individuals can obtain it. The examination of the nature of the emotions and the value that individuals place on the knowledge of their emotions, which is carried out in this thesis, highlights the importance of the acquisition of emotional-selfknowledge, as well as the link thet the latter is commonly heard to maintain with happiness. If the acquisition of this knowledge appears to be essential, it is not easy. It requires the unfurling of a number of cognitive abilities, as well as a significant cognitive effort, especially during the use of introspection. Indeed, this thesis exclusively discusses introspective access to emotional self-knowledge, and set aside other forms of access to self-knowledge. In this woek, I therefore question the practical but also moral scope of the use of introspection with regard to emotions, proposing a defense of the introspective process as a process capable of allowing an ordinary individual to detect, to identify and interpret one's own emotions. This thesis is therefore organized around two main arguments. The first of these arguments gives emotional self-knowledge a fundamental place in the form of self-knowledge that matters to ordinary people. The second defends the idea that introspection offers access ti this emotional knowledge, notably through the apprehension that its allows of the phenomenology of emotions, and that this access has a minimum reliability and therefore an epistemic value - even a weak one - as well as a moral and intrinsic value.

The goal of this study is to align teachers’ Mathematical Knowledge for Teaching (MKT) with their classroom instruction. To reduce the classroom complexity while keeping the connection between teaching and learning, I focused on Teacher Responses to Student Errors and Difficulties (TRED) in teaching equivalent fractions with an eye on students’ cognitive gains as the assessment of teaching effects. This research used a qualitative paradigm. Classroom videos concerning equivalent fractions from six teachers were observed and triangulated with tests of teacher knowledge and personal interviews. The data collection and analysis went through a naturalistic inquiry process. The results indicated that great differences about TRED existed in different classrooms around six themes: two learning difficulties regarding critical prior knowledge; two common errors related to the learning goal, and two emergent topics concerning basic mathematical ideas. Each of these themes affected students’ cognitive gains. Teachers’ knowledge as reflected by teacher interviews, however, was not necessarily consistent with their classroom instruction. Among these six teachers, other than one teacher whose knowledge obviously lagged behind, the other five teachers demonstrated similar good understanding of equivalent fractions. With respect to the basic mathematical ideas, their knowledge and sensitivity showed differences. The teachers who understood equivalent fractions and also the basic mathematical ideas were able to teach for understanding. Based on these six teachers’ practitioner knowledge, a Mathematical Knowledge Package for Teaching (MKPT) concerning equivalent fractions was provided as a professional knowledge base. In addition, this study argued that only when teachers had knowledge bases with strong connections to mathematical foundations could they flexibly activate and transfer their knowledge (CCK and PCK) to their use of knowledge (SCK) in the teaching contexts. Therefore, further attention is called for in collaboratively cultivating teachers’ mathematical sensitivity.

The goal of this study is to align teachers’ Mathematical Knowledge for Teaching (MKT) with their classroom instruction. To reduce the classroom complexity while keeping the connection between teaching and learning, I focused on Teacher Responses to Student Errors and Difficulties (TRED) in teaching equivalent fractions with an eye on students’ cognitive gains as the assessment of teaching effects. This research used a qualitative paradigm. Classroom videos concerning equivalent fractions from six teachers were observed and triangulated with tests of teacher knowledge and personal interviews. The data collection and analysis went through a naturalistic inquiry process. The results indicated that great differences about TRED existed in different classrooms around six themes: two learning difficulties regarding critical prior knowledge; two common errors related to the learning goal, and two emergent topics concerning basic mathematical ideas. Each of these themes affected students’ cognitive gains. Teachers’ knowledge as reflected by teacher interviews, however, was not necessarily consistent with their classroom instruction. Among these six teachers, other than one teacher whose knowledge obviously lagged behind, the other five teachers demonstrated similar good understanding of equivalent fractions. With respect to the basic mathematical ideas, their knowledge and sensitivity showed differences. The teachers who understood equivalent fractions and also the basic mathematical ideas were able to teach for understanding. Based on these six teachers’ practitioner knowledge, a Mathematical Knowledge Package for Teaching (MKPT) concerning equivalent fractions was provided as a professional knowledge base. In addition, this study argued that only when teachers had knowledge bases with strong connections to mathematical foundations could they flexibly activate and transfer their knowledge (CCK and PCK) to their use of knowledge (SCK) in the teaching contexts. Therefore, further attention is called for in collaboratively cultivating teachers’ mathematical sensitivity.

The advent of the era of big data on the Web has made automatic web information extraction an essential tool in data acquisition processes. Unfortunately, automated solutions are in most cases more error prone than those created by humans, resulting in dirty and erroneous data. Automatic repair and cleaning of the extracted data is thus a necessary complement to information extraction on the Web. This thesis investigates the problem of inducing cleaning rules on web extracted data in order to (i) repair and align the data w.r.t. an original target schema, (ii) produce repairs that are as generic as possible such that different instances can benefit from them. The problem is addressed from three different angles: replace cross-site redundancy with an ensemble of entity recognisers; produce general repairs that can be encoded in the extraction process; and exploit entity-wide relations to infer common knowledge on extracted data. First, we present ROSeAnn, an unsupervised approach to integrate semantic annotators and produce a unied and consistent annotation layer on top of them. Both the diversity in vocabulary and widely varying accuracy justify the need for middleware that reconciles different annotator opinions. Considering annotators as "black-boxes" that do not require per-domain supervision allows us to recognise semantically related content in web extracted data in a scalable way. Second, we show in WADaR how annotators can be used to discover rules to repair web extracted data. We study the problem of computing joint repairs for web data extraction programs and their extracted data, providing an approximate solution that requires no per-source supervision and proves effective across a wide variety of domains and sources. The proposed solution is effective not only in repairing the extracted data, but also in encoding such repairs in the original extraction process. Third, we investigate how relationships among entities can be exploited to discover inconsistencies and additional information. We present RuDiK, a disk-based scalable solution to discover first-order logic rules over RDF knowledge bases built from web sources. We present an approach that does not limit its search space to rules that rely on "positive" relationships between entities, as in the case with traditional mining of constraints. On the contrary, it extends the search space to also discover negative rules, i.e., patterns that lead to contradictions in the data.