The mental capacity for knowledge production, storage and dissemination is one of the characteristics that makes us human. Throughout history humankind has built spaces to accommodate knowledge in its various forms: from medieval monasteries to state-of-the-art research laboratories. The evolution of these ‘knowledge spaces’ and their architectural character can tell us a lot about a society´s views and perceptions of knowledge. Using monasteries, universities, libraries and research laboratories as examples of ‘knowledge spaces’ this essay aims to explore the relationship between knowledge and architecture throughout history in order to understand where knowledge has traditionally been produced, stored and disseminated. Drawing on this information, the appropriateness of the historical typologies will be assessed with regards to today´s context where the nature of knowledge is rapidly changing as a result of the emergence of new technologies

For many courses, mathematics included, there is an associated interactive e-learning system that provides assessment and tutoring. Some of these systems are classified as Intelligent Tutoring Systems. MyMathLab, Mathzone, and Assessment of LEarning in Knowledge Space (ALEKS) are just a few of the interactive e-learning systems in mathematics. In ALEKS, assessment and tutoring are based on the Knowledge Space Theory. Previous studies in a traditional learning environment have shown ALEKS users to perform equally or better in mathematics achievement than the group who did not use ALEKS. The purpose of this research was to investigate the effect of ALEKS on students’ achievement in mathematics in an online learning environment and to determine the cognitive complexity of mathematical tasks enacted by ALEKS’s initial (pretest) and final (posttest) assessments. The targeted population for this study was undergraduate students in College Mathematics I, in an online course at a private university in the southwestern United States. The study used a quasi-experimental One-Group non-randomized pretest and posttest design. Five methods of analysis and one model were used in analyzing data: t-test, correctional analysis, simple and multiple regression analysis, Cronbach’s Alpha reliability test and Webb’s depth of knowledge model. A t-test showed a difference between the pretest and posttest reports, meaning ALEKS had a significant effect on students’ mathematics achievement. The correlation analysis showed a significant positive linear relationship between the concept mastery reports and the formative and summative assessments reports meaning there is a direct relationship between the ALEKS concept mastery and the assessments. The regression equation showed a better model for predicting mathematics achievement with ALEKS when the time spent learning in ALEKS and the concept mastery scores are used as part of the model. According to Webb’s depth of knowledge model, the cognitive complexity of the pretest and posttest question items used by ALEKS were as follows: 50.5% required application of skills and concepts, 37.1% required recall of information, and 12.4% required strategic thinking: None of the questions items required extended thinking or complex reasoning, implying ALEKS is appropriate for skills and concepts building at this level of mathematics.

The disulfide bond in a protein is a single covalent bond formed from the oxidation of two cysteines. It plays an important role in the folding and structure stability, and may regulate protein functions. The connectivity prediction problem is difficult because the number of possible patterns grows rapidly with respect to the number of cysteines. We discover some rules to discriminate the patterns with high accuracy in many methods. We implement multiple SVM methods, and utilize the BKS to fuse these classifiers. We apply the hybrid method to SP39 dataset with 4-fold cross-validation for the comparison with the previous works. We raise the accuracy to 71.5%, which improves significantly that of the best previous work, 65.9%.

Bien que les modèles hydrologiques soient aujourd'hui utilisés pour la prévision des crues, leur mise en place sur un territoire particulier peut encore poser question. Classiquement, elle nécessite des chronologies observées de pluies et de débits. Cependant, ces données peuvent s'avérer incertaines, insuffisantes voire indisponibles. Cela impact alors l'identification des modèles ainsi que leurs prévisions en les rendant elles-mêmes incertaines et potentiellement inopérantes.Dans cette thèse, nous avons cherché à revisiter ces problématiques liées à la mise en place des modèles, en proposant des approches innovantes étalonnant les modèles sans certaines données pouvant s'avérer trop incertaines ou indisponibles. Ces dernières, habituellement exigées pour l'identification des modèles, sont de plus estimées au cours de l'étalonnage. Deux méthodes applicables quelque-soit la nature du modèle considéré sont proposées. La première est une méthode d'inversion des pluies et des paramètres. Elle permet d'étalonner un modèle hydrologique à partir de chronologies observées de débits et de cumuls de pluies seulement. Cette méthode identifie de plus simultanément les chronologies des pluies au pas de temps horaire. Une application particulière de cette méthode à des modèles déjà établis généralise par ailleurs à des modèles non-analytiques l'approche d'inversion des pluies proposée par Kirchner (2009). La seconde est une méthode de calibration pluie-hauteur d'eau. À partir de chronologies observées de pluies et de hauteurs d'eau seulement, elle permet de mettre en place un modèle hydrologique et d'identifier une courbe de tarage. Outre les analyses originales pouvant être menées sur les chronologies de pluies ou les courbes de tarage estimées par ces méthodes, ces dernières étendent à des contextes plus restreints en données, la possibilité d'identification de modèles pluie-débits et offrent des applications en opérationnel.En parallèle du développement de ces méthodes, cette thèse nous a aussi amené à construire une représentation conceptuelle appelée espace des connaissances. Celle-ci permet d'appréhender de manière commune les questions nouvelles ayant été étudiées ainsi que des approches plus classiques comme la calibration ou la simulation. / Rainfall-runoff models are used for flood forecasting by warning authorities. However their implementation on a particular territory is still a challenge. Indeed, they generally need to be calibrated by using observed rainfall and discharge time series. These data may be subject to errors and uncertainties. They are not always available. Then, the model calibration relevancy may be affected and the forecasts may also be subject to significant uncertainties. This research would like to address such issues related to the rainfall-runoff models calibration, by proposing original methods which may set up a model by using less data than the ``classical'' calibration. The unused data might be either subject to uncertainties or not available. Moreover, these unused data may be estimated by the methods. Two model independant approach were suggested. Both are an heuristic inversion algorithm of rainfall-runoff models. The first method estimates simultaneously hourly rainfall time series and models parameters, by using only observed hourly discharge time series and total areal rainfall of flood events. A specific application of this method to set up models (with fixed parameters), generalises to models which are not invertible analyticaly, the ``hydrology backward'' approach proposed by Kirchner (2009).The second method estimates simultaneously models parameters and a rating-curve, by using only observed hourly rainfall and stage time series. Original analysis may be performed on the rainfall time series and the rating-curve estimated by the methods. Also, they extend the applicability rainfall-runoff models to hydrological context with restricted available data and offer promising operational applications. Yet, this research lead us to build a conceptual framework, denoted knowledge space. This framework unifies not only the original approaches which were proposed, but also some more ``classical'' approaches to hydrology as the calibration and the simulation.

Submitted by Maike Costa (maiksebas@gmail.com) on 2016-08-10T13:45:22Z No. of bitstreams: 1 arquvo total.pdf: 1248500 bytes, checksum: de65e47cb8e64bca94a8b03e6af603bc (MD5) / Made available in DSpace on 2016-08-10T13:45:22Z (GMT). No. of bitstreams: 1 arquvo total.pdf: 1248500 bytes, checksum: de65e47cb8e64bca94a8b03e6af603bc (MD5) Previous issue date: 2011-10-17 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / We analyze the sensible forms, namely, space and time, and their possible relationships with knowledge. We credit, as well, the following interrogative: as the fundamentals of sensibility, space and time, organize our knowledge since, only we can only consider the object of knowledge that which is intuited spatially and temporally? The issue raised by Immanuel Kant leads us immediately to another intriguing overlapping issue, that is, we can only have access to the phenomenon, but not the thing itself. By the facts, here our discussion. / Analisamos as formas sensíveis, a saber: espaço e tempo, e seus possíveis relacionamentos perante o conhecimento. Creditamos, pois bem, a seguinte interrogativa: como os fundamentos da sensibilidade, espaço e tempo, organizam nosso conhecimento já que, unicamente, só podemos considerar objeto de conhecimento aquilo que é intuído espacialmente e temporalmente? A problemática levantada por Immanuel Kant leva-nos, de imediato, a outra intrigante questão sobreposta, isto é, só podemos ter acesso ao fenômeno, mas nunca a coisa em si. Mediante os fatos, eis aqui nossa discussão.

This volume deals with the exchange of spatial knowledge that accompanied the nineteenth-century European exploration of what are today eastern Senegambia and southwestern Mali. The travelogues of Raffenel and Gallieni are analysed with regard to how spatial information was negotiated, under what conditions it was transmitted and what problems arose. The dependence of the two Frenchmen on African informants is demonstrated, and in some cases it can be shown that they incorporated African spatial knowledge into their monographs and maps.

When the need arises to transmute complex and theoretical ideas into practice or communicate them to the uninitiated, there often exists a gap in the levels of understanding between the parties involved. This research examined knowledge transfer between practitioners within the information technology (IT) domain of interest by asking the following questions: what is the relationship between knowledge spaces for IT experts and novices; and what factors impact knowledge transfer among knowledge spaces for IT experts and novices? This study conducted interviews with a range of IT professionals to identify knowledge states that resulted in knowledge spaces for both experts and novice practitioners. A conceptual model was developed to examine the knowledge transfer process between expert and novice practitioners and identify factors affecting both the knowledge space and the knowledge states. The model also takes in consideration external factors such as organization and culture and organizational environment. The results from the study show that leadership and executive skillsets play a major role in characterizing the expert knowledge state. The results also show that knowledge transfer between and among groups was primarily impacted by interest and awareness factors among experts. Among novices, the largest barriers were cultural in nature (e.g., no management support or requirement) and environmental, such as the lack of aptitude for learning, lack of job/role experience from a new staff member, or existing staff with a hostile attitude.

Knowledge Space Theory (KST) links in several ways to Formal Concept Analysis (FCA). Recently, the probabilistic and statistical aspects of KST have been further developed by several authors. We review part of the recent results, and describe some of the open problems. The question of whether the outcomes can be useful in FCA remains to be investigated.

This thesis focuses on the creation of a systems architecture to help inform development of next generation knowledge-building environments. The architectural model consists of three components: an infrastructure layer, a discourse layer, and a visualization layer. The Knowledge Space Visualizer (KSV), which defines the top visualization layer, is a prototypic system for showing reconstructed representations of discourse-based artifacts and facilitating assessment in light of patterns of interactivity of participants and their ideas. The KSV uses Latent Semantic Analysis to extend techniques from Social Network Analysis, making it possible to infer relationships among note contents. Thus idea networks can be studied in conjunction with social networks in online discourse. Further, benchmark corpora can be used to determine knowledge advances, and systems of interactivity leading to them. Results can then provide feedback to students and teachers to support them in obtaining continually higher level achievements. In addition to visual representations, the KSV provides quantitative network metrics such as degree and density. Data drawn from 9- and 10-year-old students working on a six-week unit on optics were used to illustrate some of the functionality of the KSV. Three studies show ways in which new visualizations can be used: (a) to highlight relationships among notes, (b) as a way of tracking the development of discourse over time, and (c) as an assessment tool. Implications for the design of knowledge building environments, assessment tools, and design-based research are discussed.

This thesis focuses on the creation of a systems architecture to help inform development of next generation knowledge-building environments. The architectural model consists of three components: an infrastructure layer, a discourse layer, and a visualization layer. The Knowledge Space Visualizer (KSV), which defines the top visualization layer, is a prototypic system for showing reconstructed representations of discourse-based artifacts and facilitating assessment in light of patterns of interactivity of participants and their ideas. The KSV uses Latent Semantic Analysis to extend techniques from Social Network Analysis, making it possible to infer relationships among note contents. Thus idea networks can be studied in conjunction with social networks in online discourse. Further, benchmark corpora can be used to determine knowledge advances, and systems of interactivity leading to them. Results can then provide feedback to students and teachers to support them in obtaining continually higher level achievements. In addition to visual representations, the KSV provides quantitative network metrics such as degree and density. Data drawn from 9- and 10-year-old students working on a six-week unit on optics were used to illustrate some of the functionality of the KSV. Three studies show ways in which new visualizations can be used: (a) to highlight relationships among notes, (b) as a way of tracking the development of discourse over time, and (c) as an assessment tool. Implications for the design of knowledge building environments, assessment tools, and design-based research are discussed.