Student affect can play a profoundly important role in students' post-school lives. Understanding students' affective states within online learning environments in particular has become an important matter of research, as digital tutoring systems have the potential to intervene at the moment that students are struggling and becoming frustrated, bored or disengaged. However, despite the importance of assessing students' affective states, there is no clear consensus about what emotions are most important to assess, nor how these emotions can be best measured. This dissertation investigates students’ self-reports of their emotions and causal attributions of those emotions collected while they are solving math problems within a mathematics tutoring system. These self-reports are collected in two conditions: through limited choice Likert response and through open response text boxes. The conditions are combined with students’ cognitive attributions to describe epistemic (neither purely affective nor purely cognitive) emotions in order to explain the relationship between observable student behaviors in the MathSpring.org tutoring system and student affect. These factors include beliefs, expectations, motivations, and perceptions of ability and control. A special emphasis of this dissertation is on analyzing the role of causal attributions for the events and appraisals of the learning environment, as possible causes of student behaviors, performance, and affect.

abstract: Paper assessment remains to be an essential formal assessment method in today's classes. However, it is difficult to track student learning behavior on physical papers. This thesis presents a new educational technology—Web Programming Grading Assistant (WPGA). WPGA not only serves as a grading system but also a feedback delivery tool that connects paper-based assessments to digital space. I designed a classroom study and collected data from ASU computer science classes. I tracked and modeled students' reviewing and reflecting behaviors based on the use of WPGA. I analyzed students' reviewing efforts, in terms of frequency, timing, and the associations with their academic performances. Results showed that students put extra emphasis in reviewing prior to the exams and the efforts demonstrated the desire to review formal assessments regardless of if they were graded for academic performance or for attendance. In addition, all students paid more attention on reviewing quizzes and exams toward the end of semester. / Dissertation/Thesis / Masters Thesis Computer Science 2017

Data de aprova??o ausente. / Submitted by Jos? Henrique Henrique (jose.neves@ufvjm.edu.br) on 2017-05-11T18:00:35Z No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) rodrigo_magalhaes_mota_santos.pdf: 605428 bytes, checksum: a0c6c2c74fb7252604442e7b79b71d5d (MD5) / Approved for entry into archive by Rodrigo Martins Cruz (rodrigo.cruz@ufvjm.edu.br) on 2017-05-16T17:13:39Z (GMT) No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) rodrigo_magalhaes_mota_santos.pdf: 605428 bytes, checksum: a0c6c2c74fb7252604442e7b79b71d5d (MD5) / Made available in DSpace on 2017-05-16T17:13:39Z (GMT). No. of bitstreams: 2 license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) rodrigo_magalhaes_mota_santos.pdf: 605428 bytes, checksum: a0c6c2c74fb7252604442e7b79b71d5d (MD5) Previous issue date: 2016 / A tecnologia, presente cada vez mais no ambiente educacional, tem contribu?do para o aumento da oferta de cursos ? dist?ncia. Grande parte dos cursos ofertados nesta modalidade utilizam os Ambientes Virtuais de Aprendizagem (AVA). Estes ambientes ganham espa?o no cotidiano dos educadores devido ao f?cil manuseio e a grande diversidade de ferramentas disponibilizadas. Tais ferramentas permitem, de forma geral, a administra??o de cursos totalmente ? dist?ncia com oferta de m?ltiplas m?dias e recursos (f?runs de discuss?o, chats, dentre outros) para intera??es entre professores e alunos. Tais intera??es criam enormes volumes de dados que podem ser analisados atrav?s da aplica??o de t?cnicas de Minera??o de Dados Educacionais. Com a aplica??o destas t?cnicas pode-se realizar a previs?o de desempenho acad?mico que pode ter grande utilidade para Institui??es de Ensino no sentido de auxili?-las a tomar, de forma antecipada, decis?es pedag?gicas que possam ajudar os estudantes. Este trabalho apresenta um estudo de m?todos como Sele??o de Atributos utilizando a abordagem Wrapper e Classificador em Cascata, ainda n?o empregados em trabalhos correlatos pesquisados, que visam melhorar os resultados obtidos pelas t?cnicas de Minera??o de Dados Educacionais utilizadas na previs?o de desempenho acad?mico de estudantes. Os resultados experimentais indicam uma melhora no desempenho dos algoritmos classificadores utilizados (alguns alcan?ando a not?vel marca de 90,2% de acur?cia), bem como apontam quais os recursos utilizados no AVA possuem maior influ?ncia no desempenho dos estudantes. / Disserta??o (Mestrado Profissional) ? Programa de P?s-Gradua??o em Educa??o, Universidade Federal dos Vales do Jequitinhonha e Mucuri, 2016. / The technology, which is being increasingly used in the educational environment, has contributed for the popularity of distance courses. Much of the courses offered in this mode uses the so-called Virtual Learning Environments (VLE). These environments are gaining ground in the daily lives of educators due to its easy handling and the wide variety of available tools. These tools allow, in general, the administration of fully distance courses with multiple media and resources (forums, chats, among others) for interactions between teachers and students. These interactions create huge volumes of data that can be analyzed through the application of Educational Data Mining techniques. Such techniques can be used to academic performance prediction that can be very useful for education institutions in order to help them to take, in advance, pedagogical decisions that can help students. This work presents a study of methods as Feature Selection using the Wrapper approach and Classifier Cascade that were not employed in other works, with the aim to improve the results obtained by Educational Data Mining techniques used in the academic performance prediction. Results showed an improvement in the performance of classifiers (some obtaining the remarkable mark of 90.2% in accuracy results), as well as pointed out what the resources used in VLE that have greater influence on student performance.

Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2018-05-02T13:28:09Z No. of bitstreams: 1 DANILO ABREU SANTOS - DISSERTAÇÃO PPGCC 2015..pdf: 2955839 bytes, checksum: 45290d85cdffbae0320f29fc5e633cb6 (MD5) / Made available in DSpace on 2018-05-02T13:28:09Z (GMT). No. of bitstreams: 1 DANILO ABREU SANTOS - DISSERTAÇÃO PPGCC 2015..pdf: 2955839 bytes, checksum: 45290d85cdffbae0320f29fc5e633cb6 (MD5) Previous issue date: 2015-08-24 / A modalidade de educação online tem crescido significativamente nas últimas décadas em todo o mundo, transformando-se em uma opção viável tanto àqueles que não dispõem de tempo para trabalhar a sua formação acadêmica na forma presencial quanto àqueles que desejam complementá-la. Há também os que buscam ingressar no ensino superior por meio do Exame Nacional do Ensino Médio (ENEM) e utilizam esta modalidade de ensino para complementar os estudos, objetivando sanar lacunas deixadas pela formação escolar. O ENEM é composto por questões objetivas (subdivididas em 4 grandes áreas: Linguagens e Códigos; Matemática; Ciências Humanas; e Ciências Naturais) e a questão subjetiva (redação). Segundo dados do Ministério da Educação (MEC), mais de 50% dos candidatos que fizeram a prova do ENEM em 2014 obtiveram desempenho abaixo de 500 pontos na redação. Esta pesquisa utilizará recomendações pedagógicas baseadas no gênero textual utilizado pelo ENEM, visando prover uma melhoria na escrita da redação dissertativa. Para tanto, foi utilizado, como ferramenta experimental, o ambiente online de aprendizagem MeuTutor. O ambiente possui um módulo de escrita de redação, no qual é utilizada para correção dos textos elaborados pelos alunos, a metodologia de avaliação por pares, cujo pesquisas mostram que os resultados avaliativos são significativos e bastante similares aos obtidos por professores especialistas. Entretanto, apenas apresentar a pontuação da redação por si só, não garante a melhora da produção textual do aluno avaliado. Desta forma, visando um ganho em performance na produção da redação, foi adicionado ao MeuTutor um módulo de recomendação pedagógica baseado em 19 perfis resultados do uso de algoritmos de mineração de dados (DBScan e Kmeans) nos microdados do ENEM 2012 disponibilizado pelo MEC. Estes perfis foram agrupados em 6 blocos que possuíam um conjunto de tarefas nas áreas de escrita, gramática e coerências e concordância textual. A validação destas recomendações foi feita em um experimento de 3 ciclos, onde em cada ciclo o aluno: escreve a redação; avalia os seus pares; realiza a recomendação pedagógica que foi recebida. A partir da análise estatística destes dados, foi possível constatar que o modelo estratégico de recomendação utilizado nesta pesquisa, possibilitou um ganho mensurável na qualidade da produção textual. / Online education has grown significantly in recent years throughout the world, becoming a viable option for those who don’t have the time to pursuit traditional technical training or academic degree. In Brazil, people seek to enter higher education through the National Secondary Education Examination (ENEM) and use online education to complement their studies, aiming to remedy gaps in their school formation. The ENEM consists of objective questions (divided into 4 main areas: languages and codes; Mathematics; Social Sciences, and Natural Sciences), and the subjective questions (the essay). According to the Brazilian Department of Education (MEC), more than 50% of the candidates who took the test (ENEM) in 2014, obtained performance below 500 points (out of a 1000 maximum points) for their essays. This research uses educational recommendations based on the five official correction criteria for the ENEM essays, to improve writing. Thus, this research used an experimental tool in an online learning environment called MeuTutor. The mentioned learning environment has an essay writing/correction module. The correction module uses peer evaluation techniques, for which researches show that the results are, significantly, similar to those obtained by specialists’ correction. However, to simply display the scores for the criteria does not guarantee an improvement in students’ writing. Thus, to promote that, an educational recommendation module was added to MeuTutor. It is based on 19 profiles obtained mining data from the 2012 ENEM. It uses the algorithms DBSCAN and K-Means, and grouped the profiles into six blocks, to which a set of tasks were associated to the areas of writing, grammar and coherence, and textual agreement. The validation of these recommendations was made in an experiment with three cycles, where students should: (1) write the essay; (2) evaluate their peers; (3) perform the pedagogical recommendations received. From the analysis of these data, it was found that the strategic model of recommendation used in this study, enabled a measurable gain in quality of textual production.