Submitted by Silvana Teresinha Dornelles Studzinski (sstudzinski) on 2017-03-03T16:51:21Z No. of bitstreams: 1 Sandro José Ribeiro da Silva_.pdf: 1958508 bytes, checksum: 927c5b673859ca465e35998f946b5a64 (MD5) / Made available in DSpace on 2017-03-03T16:51:21Z (GMT). No. of bitstreams: 1 Sandro José Ribeiro da Silva_.pdf: 1958508 bytes, checksum: 927c5b673859ca465e35998f946b5a64 (MD5) Previous issue date: 2016-11-03 / Nenhuma / O desenvolvimento de habilidades de programação de sistemas computacionais é uma necessidade crescente, devido ao amplo uso de recursos computacionais nas mais diversas áreas. Ao mesmo tempo, é conhecida a deficiência existente quanto à quantidade de profissionais sendo graduados nesta área. Alguns estudos indicam dificuldades dos estudantes e ao mesmo tempo falta de metodologias adequadas como possíveis elementos contribuindo para este contexto, corroborando a necessidade de desenvolvimento de pesquisas sobre o aprendizado de linguagens de programação. Entre as possíveis soluções para este problema de motivação, o desenvolvimento de um ambiente gamificado como ferramenta de ensino para linguagens de programação vem sendo explorado em projetos de pesquisa e também em opções comerciais. Uma das deficiências observadas nestas inciativas é justamente a falta de suporte aos professores para acompanhamento da evolução dos alunos. Buscando atender esta necessidade, o presente trabalho propõe um ambiente de apoio ao ensino de lógica de programação cujo diferencial é a inclusão de recursos de análise do comportamento dos alunos, voltados para o apoio ao professor. Desta forma, o trabalho proposto alia aos jogos eletrônicos o monitoramento on-line de suas etapas, através do uso de técnicas de mineração de dados educacionais. Com base em um framework para Gamificação, foi definido e desenvolvido um ambiente Web para ensino da linguagem Lua, com aspectos de Gamificação e Mineração de Dados Educacionais. Este ambiente foi utilizado em avaliações com alunos do ensino técnico, tendo sido observados resultados promissores nos aspectos motivacionais. As avaliações envolvendo a identificação de vantagens geradas para os professores com uso dos dados sobre o comportamento dos alunos também foram positivas e indicam um bom potencial para esta abordagem. / The development of computer systems programming skills is a growing necessity, due to the wide use of computational resources in different areas. At the same time, it is known the deficiency with respect to the amount of professionals being graduated in this area. Some studies indicates difficulties of students and lack of adequate methodologies as possible elements contributing to this context, supporting the need to develop research on learning programming languages. As a possible solution to this problem of motivation, the development of a gamified environment as a teaching tool for programming languages is being explored in research projects and also commercial options. One of the deficiencies observed in these initiatives is precisely the lack of support to teachers to follow up of the evolution of students, which consists in one of the differentials of the proposed work. In this way, the work integrates to electronic games the online monitoring through the use of educational data mining techniques. Based on the framework for gamification, has been defined and developed a web environment to the Lua language teaching, with aspects of gamification and education data mining. This environment has already been tested preliminarily with technical education students, being observed promising results. A new stage of development and testing is foreseen to deepening the identification of advantages generated for teachers with the use data on the behavior of students.

This research examined teachers' online behaviors while using a digital library service--the Instructional Architect (IA)--through three consecutive studies. In the first two studies, a statistical model called latent class analysis (LCA) was applied to cluster different groups of IA teachers according to their diverse online behaviors. The third study further examined relationships between teachers' demographic characteristics and their usage patterns. Several user clusters emerged from the LCA results of Study I. These clusters were named isolated islanders, lukewarm teachers, goal-oriented brokerswindow shoppers, key brokers, beneficiaries, classroom practitioners, and dedicated sticky users. In Study II, a cleaning process was applied to the clusters discovered in Study I to further refine distinct user groups. Results revealed three clusters, key brokers, insular classroom practitioners, and ineffective islanders. In Study III, the integration of teacher demographic profiles with clustering results revealed that teaching experience and technology knowledge affected teachers' effectiveness in using the IA. The implication, contributions, and limitation of this research are discussed.

This work is about the creation of a tool for pedagogical recommendation which objective is to provide teachers, from web-based courses, personalized pedagogical recommendations generated based on the mining results of their students’ educational data. In order to guide this creation, we propose the Pedagogical Recommendation Process that counts on the coordinated work and cooperation of the Human Intelligence (domain specialists) and the Artificial Intelligence (computational tools). The process is constituted of four steps that occur in a sequential and cyclic way, starting with “Detect Practices”, where we detect if there are actions affecting the teaching and learning process. Is the next step, “Discover Patterns”, we use educational data mining techniques, based on predefined mining scenarios, to find patterns with pedagogical significance for the practices detected. In the following step, “Recommend”, it is where appropriate recommendations are offered, given the students’ current pedagogical situation. Finally, the “Monitor and Evaluate” step, where it is analyzed whether the students were positively affected by the recommendations and if they were relevant. The proposed tool was used in a case study with real data provided by a Spanish language course with 200 students enrolled, who produced more than 700 megabytes of information contained in, approximately, 1220000 triples. As results we were able to detected practices and the patterns associated to them, which were used to create recommendations, evaluated (relevance) by specialists in the educational/pedagogical domain and made available for the final users (teachers) to suggest them to their students. / A presente dissertação trata da criação de uma ferramenta para a recomendação pedagógica cujo objetivo é prover aos professores de cursos baseados na web, recomendações pedagógicas personalizadas geradas com base nos resultados da Mineração dos Dados Educacionais de seus alunos. Para orientar essa criação propomos o Processo de Recomendação Pedagógica, o qual conta com o trabalho conjunto e coordenado da Inteligência Humana (especialistas nos domínios envolvidos) e da Inteligência Artificial (ferramentas computacionais). O processo é constituído de quatro etapas que ocorrem de forma cíclica e sequencial, iniciando com “Detectar Práticas”, onde detectamos se existem ações afetando o processo de ensino e aprendizagem. Na etapa seguinte, “Descobrir Padrões”, utilizamos as técnicas de Mineração de Dados Educacionais, por meio de Cenários de Mineração predefinidos, para encontrar padrões de interesse pedagógico acerca das práticas detectadas. Na próxima etapa, “Recomendar”, são oferecidas recomendações apropriadas a atual situação pedagógica do aluno. Finalmente a etapa “Monitorar e Avaliar”, onde acompanhamos e analisamos se os alunos foram afetados positivamente pelas recomendações e se estas foram relevantes. A ferramenta de recomendação proposta foi utilizada em um estudo de caso, com dados reais provenientes de um curso de língua Espanhola com 200 alunos que produziram mais de 700 megabytes de informações dispostas em, aproximadamente, 1220000 triplas. Como resultados, fomos capazes de detectar práticas e os padrões associados a elas, que foram utilizados na criação de recomendações, avaliadas (relevância) por especialistas no domínio educacional/pedagógico, e disponibilizadas para que os usuários finais (professores) as ofereçam a seus alunos.

Submitted by Rodrigo Yoshio Tamae (rytamae@yahoo.com.br) on 2018-05-09T20:50:34Z No. of bitstreams: 1 tamae_ry_dr_prud.pdf: 8732958 bytes, checksum: adaebfe74540ed474f93e923e81fb527 (MD5) / Rejected by ALESSANDRA KUBA OSHIRO ASSUNÇÃO (alessandra@fct.unesp.br), reason: Solicitamos que realize correções na submissão seguindo as orientações abaixo: Números de página aparecem duas vezes nas folhas a partir da 140. Agradecemos a compreensão. on 2018-05-10T19:58:40Z (GMT) / Submitted by Rodrigo Yoshio Tamae (rytamae@yahoo.com.br) on 2018-05-10T20:25:22Z No. of bitstreams: 1 tamae_ry_dr_prud.pdf: 8721951 bytes, checksum: 02e3bd0d2ad16ca569a7507cc1c1583d (MD5) / Approved for entry into archive by ALESSANDRA KUBA OSHIRO ASSUNÇÃO (alessandra@fct.unesp.br) on 2018-05-11T11:39:17Z (GMT) No. of bitstreams: 1 tamae_ry_dr_prud.pdf: 8721951 bytes, checksum: 02e3bd0d2ad16ca569a7507cc1c1583d (MD5) / Made available in DSpace on 2018-05-11T11:39:18Z (GMT). No. of bitstreams: 1 tamae_ry_dr_prud.pdf: 8721951 bytes, checksum: 02e3bd0d2ad16ca569a7507cc1c1583d (MD5) Previous issue date: 2018-03-16 / Esta pesquisa de doutorado está vinculada ao Programa de Pós-Graduação em Educação da Faculdade de Ciências e Tecnologia da Universidade Estadual Paulista "Júlio de Mesquita Filho" (FCT/Unesp), campus de Presidente Prudente-SP, na linha de pesquisa "Processos Formativos, Ensino e Aprendizagem", nas áreas de Educação a distância (EaD) e Formação de Professores. O grande avanço das Tecnologias Digitais da Informação e da Comunicação (TDIC) tem provocado inúmeras mudanças em todas as áreas da ciência. Na Educação ocorre a ampla adoção e utilização dos Ambientes Virtuais de Aprendizagem (AVA), os quais podem contribuir para a utilização de TDIC, metodologias ativas de aprendizagem e que favorecem a abordagem Construcionista, Contextualizada e Significativa (CCS). A abordagem CCS é aquela em que o cursista utiliza a tecnologia como instrumento para produzir algo que parte da sua vivência e realidade, e ao se deparar com os conceitos curriculares, o professor atua como mediador para ajudá-lo a formalizar esses conceitos. Nesse contexto, a Internet e os dispositivos móveis passaram a ser utilizados em escala crescente, e tem contribuído para a proliferação de grande quantidade de dados em formato digital que, por sua vez, ainda são pouco utilizados para gerar a descoberta de conhecimento em contextos educacionais. É onde destaca-se a área de mineração de dados educacionais (MDE), que consiste no desenvolvimento de métodos e técnicas orientados a explorar tais dados digitais para melhor compreender o comportamento dos cursistas e em quais condições eles aprendem. Assim, "como utilizar técnicas de MDE para identificar indícios da abordagem CCS nos cursos da modalidade híbrida?" é a questão que norteia esta pesquisa de doutorado, pois mesmo professores qualificados para atividades docentes, muitas vezes, não possuem proficiência suficiente quanto ao uso de recursos computacionais, tais como linguagens de programação e ferramentas de banco de dados, e muito menos, quanto ao uso de técnicas de mineração de dados aplicadas à contextos educacionais. A pesquisa fez uso tanto da abordagem quantitativa quanto qualitativa, com base no delineamento metodológico Ex Post Facto ou Pesquisa não-experimental, pois o estudo foi realizado após a conclusão dos fatos. Para responder as questões norteadoras, o curso de Educação Especial na Perspectiva Inclusiva do programa Redefor/Unesp foi analisado a partir das categorias CCS (contexto do cursista, espiral de aprendizagem e ciclo de ações, aprendizagem em rede, papel do professor e formalização de conceitos) definidas com base nas indicações de Schlünzen (2000; 2015), Santos (2015) e Valente (2005). Foi utilizado o modelo de mineração de dados proposto por Fayad, Piatetsky-Shapiro e Smyth (1996) e as fases que consomem maior esforço repetitivo possibilitaram o mapeamento de padrões a serem seguidos, e para minimizar os esforços e maximizar os resultados, foi proposto e implementado um protótipo de software denominado EDMXP (Educational Data Mining eXPeriment) em linguagem de programação Java para o suporte às atividades de seleção, pré-processamento, mineração e análise de dados. As tarefas de mineração de dados utilizadas foram as de agrupamento e classificação representadas pelos algoritmos Simple KMeans, VSM e J48. Os resultados foram compilados em uma linguagem que possibilita aos profissionais de Educação melhor compreenderem os resultados (tabelas e gráficos), além de um quadro de indicadores de desempenho (dashboard). Ao final, foi possível constatar que a MDE pode ser um fator transformador em Educação a partir do momento que possibilita que se tome decisões com base em dados e em fatos, e não apenas de forma intuitiva ou por meio de experiências vivênciadas. Representa, portanto, uma nova forma de fazer e pensar a Educação. / This doctoral research is bound to the Graduate Program in Education of the Faculty of Science and Technology of the São Paulo State University "Júlio de Mesquita Filho" (FCT / Unesp), Campus of Presidente Prudente-SP, in the research line "Formative Processes, Teaching and Learning", in the areas of Distance Education (D-Learning) and Teacher Training. The great advance of the Digital Technologies of Information and Communication (DTIC) has caused fullness changes in all areas of science. In Education there is a widespread adoption and use of Virtual Learning Environment (VLE), which can contribute to the use of DTIC, active learning methodologies and favoring the Constructionist, Contextualized and Significative (CCS) approach. The CCS approach is that in which student uses technology as an instrument to produce something that arise in your own experience and reality, and when he came across with curricular concepts, teacher acts as mediator to help him to formalize these concepts. In these context, the Internet and mobile devices started to be used on a growing scale and have contributed to the proliferation of large amounts of data in digital format, which in turn are little used to generate the knowledge discovery in educational contexts. It's where stands out the area of Educational Data Mining (EDM), which consists in the development of methods and techniques designed to exploit such digital data to better understand students behavior's and in what conditions they learn. Thus, "how to use EDM techniques to identify evidence of CCS approach in hybrid mode courses?" it's the issue that guides this doctoral research, because even qualified teachers for teaching activities often lack sufficient proficiency in the use of computational resources, such as programming languages and database tools, much less regarding the use of data mining techniques applied to educational contexts. The research made use of both quantitative and qualitative approach, based on the methodological design Ex Post Facto or non-experimental research, once this study was conducted after the completion of the facts. To answer the leading questions, the Special Education course in the Inclusive Perspective of the Redefor / Unesp program was analyzed from the CCS categories (student's context, learning spiral and cycle of actions, learning network, teacher role and concepts formalization) defined according to the indications of Schlünzen (2000; 2015), Santos (2015) and Valente (2005). It was used the data mining model proposed by Fayad, Piatetsky-Shapiro and Smyth (1996) and the phases that consume most repetitive effort allowed the mapping of patterns to be followed, and to minimize efforts and to maximize results, was proposed and implemented a software prototype named EDMXP (Educational Data Mining eXPeriment) in Java programming language to support selection, preprocessing, mining and data analysis activities. The data mining tasks used were clustering and classification tasks represented by the Simple KMeans, VSM and J48 algorithms. The results were compiled in a language that enables Education professionals to better understand results (tables and graphs), as well as a dashboard of performance indicators. Finally, it was possible to verify that EDM can be a transforming factor in Education from the moment that allows decisions based on data and facts, and not only in an intuitive way or by lived experiences. It represents, therefore, a new way of doing and thinking Education.

Submitted by Marília Sousa (mariliamariafeitoza@gmail.com) on 2018-07-26T12:25:36Z No. of bitstreams: 3 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Dissertação Marília.pdf: 1106096 bytes, checksum: 5f4d3a102f590e08a72c6af9ef02d2e4 (MD5) folha de aprovação.pdf: 114224 bytes, checksum: 83acb0aa4ff29dd5cc1364b9b391ac77 (MD5) / Approved for entry into archive by Secretaria PPGI (secretariappgi@icomp.ufam.edu.br) on 2018-07-26T18:20:47Z (GMT) No. of bitstreams: 3 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Dissertação Marília.pdf: 1106096 bytes, checksum: 5f4d3a102f590e08a72c6af9ef02d2e4 (MD5) folha de aprovação.pdf: 114224 bytes, checksum: 83acb0aa4ff29dd5cc1364b9b391ac77 (MD5) / Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2018-07-27T12:39:14Z (GMT) No. of bitstreams: 3 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Dissertação Marília.pdf: 1106096 bytes, checksum: 5f4d3a102f590e08a72c6af9ef02d2e4 (MD5) folha de aprovação.pdf: 114224 bytes, checksum: 83acb0aa4ff29dd5cc1364b9b391ac77 (MD5) / Made available in DSpace on 2018-07-27T12:39:15Z (GMT). No. of bitstreams: 3 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Dissertação Marília.pdf: 1106096 bytes, checksum: 5f4d3a102f590e08a72c6af9ef02d2e4 (MD5) folha de aprovação.pdf: 114224 bytes, checksum: 83acb0aa4ff29dd5cc1364b9b391ac77 (MD5) Previous issue date: 2017-09-29 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / The present work introduces the Educational Data Mining and an experiment involving prediction of partial exams. The experiment uses data of the Introduction to Computer Programming course of the Federal University of Amazonas and seeks to classify the students according to their grade, in a maximum of three classes: satisfactory, unsatisfactory and without concept (dropout students). As conclusion, there is a quantitative analysis with the predictive data. / O presente trabalho tem o intuito de apresentar a Mineração de Dados Educacionais e um experimento envolvendo previsão de provas parciais. O experimento é realizado através dos dados da disciplina de Introdução à Programação de Computadores da Universidade Federal do Amazonas e busca classificar os alunos de acordo com as notas obtidas, em no máximo três classes: satisfatório, insatisfatório e sem conceito (alunos evadidos). Como conclusão, tem-se uma análise quantitativa com os dados da previsão.

Sistemas educacionais possuem diversas funcionalidades capazes de apoiar a interação entre alunos e professores de maneira dinâmica, síncrona e assíncrona. Uma das formas de monitorar a eficácia do processo educacional e por meio da utilização dos dados armazenados nesses sistemas como fonte de informação. Pesquisas em Learning Analytics, Academic Analytics e Mineração de Dados Educacionais, buscam explorar os dados de sistemas educacionais utilizando processamento analítico e técnicas de mineração de dados. No entanto, há uma serie de fatores que dificultam a gestão eficiente do processo educacional a partir dos dados de sistemas educacionais. A transformação de dados provenientes de diferentes tipos de sistemas educacionais, como Sistemas de Gestão de Aprendizagem e Sistemas Acadêmicos, e uma tarefa complexa devido a natureza heterogênea dos dados. Dados provenientes desses sistemas podem ser analisados considerando diferentes stakeholders, sob varias perspectivas e níveis de granularidade. Neste cenário, um modelo de referência para a descoberta de conhecimento a partir de dados de sistemas educacionais, denominado Modelo de Referência de Dados Educacionais (EDRM), foi desenvolvido neste trabalho. O EDRM e um modelo dimensional no formato star schema, estruturado em um Data Warehouse, projetado para ser uma fonte única de dados integrados e correlacionados voltada a tomada de decisão. Assim, e possível armazenar dados de diversas fontes, combina-los e, por fim, realizar analises que levem as instituições a desenvolver uma melhor compreensão, rastrear tendências e descobrir lacunas e ineficiências acerca do processo educacional. Neste trabalho, o EDRM foi validado por meio de um estudo de caso, utilizando bases de dados reais coletadas de diferentes sistemas educacionais. Os resultados mostram que o EDRM e eficiente em tarefas com diferentes objetivos, utilizando processamento analítico e mineração de dados. / Educational systems support dynamic, synchronous and asynchronous interaction between students and educators. Researches in Learning Analytics, Academic Analytics and Educational Data Mining explore data from educational systems for knowledge discovery through analytical processing, statistical analysis and data mining. However, there are some factors that hinder an efficient management of the educational process. The transformation of data from different kinds of educational system, as Learning Management Systems and Student Information Systems, can be even more difficult due to data heterogeneity. Data from these systems can be analyzed considering different stakeholders, under different perspectives and under different granularities. Motivated by this scenario, in this work we propose Modelo de Referência de Dados Educacionais (EDRM), a reference data model for knowledge discovery in data from educational systems. EDRM is an analytical model structured under a Data Warehouse architecture following a multidimensional data model. EDRM is projected for being an resource of integrated and correlated data focused in decision taking in the educational process. EDRM was developed considering a deep analysis of data and functionalities from different educational systems. In this sense, data from different kinds of systems and sources can be used unified, integrated and consistently. This allows institutions to better comprehend their data, as well as discover patterns, gaps and inefficiencies about their educational process. In this work, EDRM was validated in a case study using real-world databases from different educational systems. The results indicate that EDRM is efficient in tasks with different objectives, using Learning Analytics and Educational Data Mining techniques, and analyzing different perspectives.

Pour rester concurrentiel et faire face à la complexité du développement des systèmes complexes, il est nécessaire d’avoir de bons ingénieurs système expérimentés sur le marché. De ce fait, Les ingénieurs système ainsi que les enseignants-chercheurs doivent mettre en place des stratégies et des méthodologies didactiques pour préparer les futurs ingénieurs systèmes. L’idée est d’assurer aux étudiants ingénieurs une préparation approfondie à l’ingénierie système (IS) et un savoir-faire quasi-professionnel fondés sur des expériences pratiques. Cette thèse répond à la nécessité de l’évaluation des étudiants, dans l’optique de vérifier leur maîtrise des processus du développement d’un système complexe tout le long de son cycle de vie. Nous nous intéressons particulièrement au problème de l’évaluation des connaissances acquises des apprenants lors de l’apprentissage actif de l’IS dans un environnement E-learning collaboratif en se fondant sur l’approche d’acquisition de connaissances. Pour cela, nous avons élaboré et mis en œuvre nos travaux de recherche dans le cadre d’une démarche d’E-apprentissage par projet, approche pédagogique qui facilite l’apprentissage en ligne de l’IS basé sur des projets. Dans ce contexte, nous avons décidé de piloter notre environnement Eapprentissage par des projets initiés à partir des processus normalisés en IS. Nous avons également guidé la formalisation de ces processus standardisés par l’intégration des modèles de compétences d’IS afin de soutenir le développement professionnel d’un système. Lors de la phase d’apprentissage, différents étudiants collaborent à distance. Ce partage d’information est fondé généralement sur des échanges formels ou informels. L’intérêt de l’acquisition de ces connaissances, issues des compte-rendus écrits, des retours d’expérience et des erreurs rectifiées, est d’aider à étudier et évaluer les expériences et les activités des étudiants pour favoriser l’apprentissage actif et collaboratif de l’IS. Notre proposition est une solution permettant l’évaluation des connaissances acquises fondée sur des outils sémantiques. La solution s’adresse aux étudiants et aux enseignants de la plateforme. D’une part, elle permet le suivi et l’évaluation des étudiants d’une manière intelligente et d’autre part, elle permet l’accompagnement de l’enseignant lors de la définition de son projet. Tout d’abord, nous présentons une méthode d’évaluation mixte qui combine le raisonnement à partir de cas et le processmining afin de fournir des représentations visuelles qui aident l’enseignant dans son processus d’évaluation ainsi que la définition de son scénario d’apprentissage. Nous exploitons des techniques d’analyse de réseaux sociaux pour étudier les interactions des apprenants. Ensuite, nous proposons un processus d’annotation sémantique des travaux des étudiants. Le processus a pour but de guider l’enseignant dans son processus d’évaluation. De plus, afin d’enrichir la mise en œuvre de l’évaluation des connaissances, nous introduisons le concept d’évaluation par les pairs afin de promouvoir la réflexivité et la confiance en soi de l’apprenant. Enfin, les différentes contributions sont illustrées au moyen d’une étude de cas. A la lumière de cette étude de cas, nous avons montré que les hypothèses de recherche sont valides. / To remain competitive and to deal with the complexities of developing complex systems, it is necessary to have skilled and experienced system engineers on the labor market. As a result, system engineers, teachers and researchers must put in place strategies and didactic methodologies to prepare future systems engineers. The idea is to ensure engineering students with depth preparation for system engineering (SE) and a quasiprofessional expertise based on practical experiences. This thesis meets the need for student evaluation, to verify their knowledge of the processes of developing a complex system throughout its lifecycle. We are particularly interested in the problem of evaluating the knowledge acquired by learners during the active learning of the SE in a collaborative Elearning environment based on the knowledge acquisition approach. For this, we have developed and implemented our research as part of an E-learning project approach. This is a pedagogical approach that facilitates online learning of SE based on projects. We decided to pilot our E-learning environment with projects initiated from standard SE processes. We also conducted a formalization of these standardized processes by integrating SE competencies models to support the professional development of a system. During the learning phase, different students collaborate remotely. This sharing of information is generally based on formal or informal exchanges. The interest of the acquisition of this knowledge, resulting from written reports, feedback and rectified errors, is to help study and evaluate the experiences and activities of students to promote active learning and collaboration of the SE. Our proposal is a solution for the evaluation of acquired knowledge based on semantic tools. The solution is addressed to students and teachers of the platform. On the one side, it allows the monitoring and evaluation of students in an intelligent way. On the other side, it allows to support the teacher during the definition of his project. First, we present a mixed assessment method that combines reasoning from cases and process-mining to provide visual representations that help the teacher in his assessment process as well as the definition of his learning scenario. We use social network analysis techniques to study learner interactions. Then, we propose a semantic annotation process of student works. The purpose of this process is to guide the teacher in his assessment process. Furthermore, we introduce the concept of peer review in order to promote the reflexivity and self-confidence of learner. Finally, the different contributions are illustrated by a case study. Based on this case study, we show that the research hypotheses are valid.

In der Netzwerkgesellschaft des 21. Jahrhunderts gilt die kollaborative Verteilung und Nutzung von Information und Wissen als Schlüsselstrategie für den webbasierten Informations- und Wissenstransfer. Durch die technologischen Möglichkeiten werden technische Zugangsbarrieren weitestgehend überwunden und traditionelle Formen der Wissensvermittlung durch moderne webbasierte Lernumgebungen ergänzt. Der Umgang mit kollaborativen Lehr- und Lernszenarien im dynamischen Informations- und Wissenstransfer bildet die Grundlage für den soziokulturellen Fortschritt innerhalb der Bildungsforschung. Der Schwerpunkt dieser Arbeit lag auf der strukturellen und relationalen Analyse sozialer Organisationsstrukturen innerhalb von Wissensnetzwerken. Ziel war es, Einflussfaktoren offenzulegen, die sich auf das Innovations- und Distributionspotential von Information und Wissen innerhalb von kollaborativen Wissensnetzwerken auswirken. Es wurden dazu Interaktionsprozesse von Teilnehmern innerhalb von Diskussionsforen am Beispiel der Lernplattform OPAL – dem aktuell populärsten Lernmanagementsystem in der Hochschulbildung Sachsens, Deutschland – untersucht. Unter der Annahme, dass soziale Interaktion besonders im Umgang mit kollaborativen Medien den Bildungsablauf und der Aufbau von Wissensnetzwerken die Lehr- und Lernprozesse beeinflusst, wurden in dieser Arbeit die strukturellen Bedingungen des kollaborativen Wissensnetzwerkes in OPAL exploriert und soziale Rollenkonstrukte relational identifiziert, um die Auswirkungen kollaborativer Aktivitäten auf den Informations- und Wissenstransfer in Wissensnetzwerken zu erklären. Es wurden vornehmlich beziehungsorientierte kommunikationstheoretische Modelle zugrunde gelegt und relationale Forschungsmethoden wie SNA (Social Network Analysis) und DNA (Dynamic Network Analysis) angewandt, um eine Basis für die weiterführende Implementierung sozial vernetzter Lehr- und Lernstrategien in der Bildungsforschung zu schaffen. […] / In the network society of the 21st century, a key strategy for web-based exchange of information and knowledge is their collaborative distribution and use. Technical hurdles of access are mostly being overcome with technological advances and traditional forms of passing on knowledge are being complemented by modern, e-learning environments. Within research into education, the foundation for socio-cultural progress is formed by involvement with collaborative teaching and learning scenarios in a dynamic exchange of information and knowledge. The emphasis of this work lay in the analysis of structures and relationships of social organisations within knowledge networks. The aim was to describe the exchange of information and knowledge in collaborative learning systems and to explore its influence on the potential for innovation and distribution of information and knowledge. A study was undertaken of the interaction of participants in discussion forums as exemplified by the learning platform OPAL – currently the most popular learning management system in secondary school education in Saxony, Germany. On the assumption that social interaction, particularly involving collaborative media, the progress of education and the construction of knowledge networks do influence teaching and learning processes, this work explored the structural conditions of OPAL's collaborative knowledge network and identified relationships between social role constructs in order to explain the effect of collaborative activities on the process of diffusion of information and knowledge in knowledge networks. Primarily the study was based on relationship oriented sociological models and communication theory models, and research methods for relationships, including SNA (Social Network Analysis) and DNA (Dynamic Network Analysis) were applied, so as to create a basis for further implementation of social network teaching and learning strategies in educational research. [...]

In der Netzwerkgesellschaft des 21. Jahrhunderts gilt die kollaborative Verteilung und Nutzung von Information und Wissen als Schlüsselstrategie für den webbasierten Informations- und Wissenstransfer. Durch die technologischen Möglichkeiten werden technische Zugangsbarrieren weitestgehend überwunden und traditionelle Formen der Wissensvermittlung durch moderne webbasierte Lernumgebungen ergänzt. Der Umgang mit kollaborativen Lehr- und Lernszenarien im dynamischen Informations- und Wissenstransfer bildet die Grundlage für den soziokulturellen Fortschritt innerhalb der Bildungsforschung. Der Schwerpunkt dieser Arbeit lag auf der strukturellen und relationalen Analyse sozialer Organisationsstrukturen innerhalb von Wissensnetzwerken. Ziel war es, Einflussfaktoren offenzulegen, die sich auf das Innovations- und Distributionspotential von Information und Wissen innerhalb von kollaborativen Wissensnetzwerken auswirken. Es wurden dazu Interaktionsprozesse von Teilnehmern innerhalb von Diskussionsforen am Beispiel der Lernplattform OPAL – dem aktuell populärsten Lernmanagementsystem in der Hochschulbildung Sachsens, Deutschland – untersucht. Unter der Annahme, dass soziale Interaktion besonders im Umgang mit kollaborativen Medien den Bildungsablauf und der Aufbau von Wissensnetzwerken die Lehr- und Lernprozesse beeinflusst, wurden in dieser Arbeit die strukturellen Bedingungen des kollaborativen Wissensnetzwerkes in OPAL exploriert und soziale Rollenkonstrukte relational identifiziert, um die Auswirkungen kollaborativer Aktivitäten auf den Informations- und Wissenstransfer in Wissensnetzwerken zu erklären. Es wurden vornehmlich beziehungsorientierte kommunikationstheoretische Modelle zugrunde gelegt und relationale Forschungsmethoden wie SNA (Social Network Analysis) und DNA (Dynamic Network Analysis) angewandt, um eine Basis für die weiterführende Implementierung sozial vernetzter Lehr- und Lernstrategien in der Bildungsforschung zu schaffen. […] / In the network society of the 21st century, a key strategy for web-based exchange of information and knowledge is their collaborative distribution and use. Technical hurdles of access are mostly being overcome with technological advances and traditional forms of passing on knowledge are being complemented by modern, e-learning environments. Within research into education, the foundation for socio-cultural progress is formed by involvement with collaborative teaching and learning scenarios in a dynamic exchange of information and knowledge. The emphasis of this work lay in the analysis of structures and relationships of social organisations within knowledge networks. The aim was to describe the exchange of information and knowledge in collaborative learning systems and to explore its influence on the potential for innovation and distribution of information and knowledge. A study was undertaken of the interaction of participants in discussion forums as exemplified by the learning platform OPAL – currently the most popular learning management system in secondary school education in Saxony, Germany. On the assumption that social interaction, particularly involving collaborative media, the progress of education and the construction of knowledge networks do influence teaching and learning processes, this work explored the structural conditions of OPAL's collaborative knowledge network and identified relationships between social role constructs in order to explain the effect of collaborative activities on the process of diffusion of information and knowledge in knowledge networks. Primarily the study was based on relationship oriented sociological models and communication theory models, and research methods for relationships, including SNA (Social Network Analysis) and DNA (Dynamic Network Analysis) were applied, so as to create a basis for further implementation of social network teaching and learning strategies in educational research. [...]

Die Erforschung der Zusammenstellung kleiner Lerngruppen beschäftigt sich mit dem Problem, eine passende Gruppenzusammensetzung in einer Population von Lernern zu finden, die jeder Gruppe optimalen Nutzen bringen könnte. In letzter Zeit sind viele Studien zu diesem Problem der Kleingruppenzusammenstellung durchgeführt worden. Allerdings waren diese Forschungen nur selten auf den Kontext großer Lerner-Populationen ausgerichtet. Angesichts des zunehmenden Aufkommens von MOOCs muss jedoch das Problem der Gruppenzusammenstellung entsprechend erweitert betrachtet werden, und zwar mit neuen Forschungen, die den Kontext derartig großer Lerner-Populationen berücksichtigen. Anders als in Klassenzimmer-Settings könnte die beobachtete hohe Abbruchquote in MOOCs in einer Unterbesetzung der Gruppengröße resultieren und könnte somit viele Lerner dazu bringen, neue Gruppen zu bilden. Zusätzlich zur Gruppenzusammenstellung muss daher die Gruppenneuzusammenstellung als neues Thema in aktuellen Kontexten großer Lerner-Populationen ebenfalls erforscht werden. Die Untersuchungen der vorliegenden Arbeit gliedern sich in zwei Teile. Der erste Teil beschäftigt sich mit Gruppenzusammenstellung. In diesem Teil stelle ich einen diskreten-PSO Algorithmus zur Zusammenstellung kleiner Lerngruppen vor und vergleiche bislang bestehende Gruppenzusammenstellungs-Algorithmen unter den Gesichtspunkten Zeitaufwand und Gruppierungsqualität. Um Gruppenzusammenstellung in MOOCs anzuwenden wurde ein Gruppenzusammenstellungsexperiment in einem MOOC durchgeführt. Die Hauptergebnisse deuten darauf hin, dass die Gruppenzusammenstellung die Abbruchsquote reduzieren kann, jedoch lediglich einen sehr schwachen Bezug zur Lernperformanz der Lerner aufweist. Der zweite Teil beschäftigt sich mit Gruppenneuzusammenstellung. Die vorliegende Arbeit stellt eine datengesteuerte Herangehensweise vor, die umfassenden Gebrauch von Gruppeninteraktionsdaten macht sowie Gruppendynamik mit einbezieht. Mittels einer in einem Simulationsexperiment durchgeführten Evaluation zeigen sich die Vorteile dieses Verfahrens: Der Lerngruppenzusammenhalt wird verbessert und die Abbruchsquote im Vergleich zu einer Zufallsverteilung reduziert. Darüberhinaus wurde hier ein Gruppen-Lern-Werkzeug entwickelt und für die Praxis vorbereitet, das die Anforderungen des geforderten Ansatzes der Gruppenneuzusammenstellung erfüllt. / Small learning group composition addresses the problem of seeking such matching among a population of students that it could bring each group optimal benefits. Recently, many studies have been conducted to address this small group composition problem. Nevertheless, the focus of such a body of research has rarely been cast to large-scale contexts. Due to the recent come of MOOCs, the topic of group composition needs to be accordingly extended with new investigations in such large learning contexts. Different from classroom settings, the reported high drop-out rate of MOOCs could result in group’s incompletion in size and thus might compel many students to compose new groups. Thus, in addition to group composition, group re-composition as a new topic needs to be studied in current large-scale learning contexts as well. In this thesis, the research is structured in two stages. The first stage is group composition. In this part, I proposed a discrete-PSO algorithm to compose small learning groups and compared the existing group composition algorithms from the perspectives of time cost and grouping quality. To implement group composition in MOOCs, a group composition experiment was conducted in a MOOC. The main results indicate that group composition can reduce drop-out rate, yet has a very weak association with students’ learning performance. The second stage is to cope with group re-composition. This thesis suggests a data-driven approach that makes full use of group interaction data and accounts for group dynamics. Through evaluation in a simulation experiment, it shows its advantages of bringing us more cohesive learning groups and reducing the drop-out rate compared to a random condition. Apart from these, a group learning tool that fulfills the goals of the proposed group re-composition approach has been developed and is made ready for practice.