Les travaux de recherche de cette thèse concernent principalement l‘aide à la navigation individualisée dans un hypermédia épistémique. Nous disposons d‘un certain nombre de ressources qui peut se formaliser à l‘aide d‘un graphe acyclique orienté (DAG) : le graphe des épistèmes. Après avoir cerné les environnements de ressources et de parcours, les modalités de visualisation et de navigation, de traçage, d‘adaptation et de fouille de données, nous avons présenté une approche consistant à corréler les activités de conception ou d‘édition à celles dédiées à l‘utilisation et la navigation dans les ressources. Cette approche a pour objectif de fournir des mécanismes d‘individualisation de la navigation dans un environnement qui se veut évolutif. Nous avons alors construit des prototypes appropriés pour mettre à l‘épreuve le graphe des épistèmes. L‘un de ces prototypes a été intégré à une plateforme existante. Cet hypermédia épistémique baptisé HiPPY propose des ressources et des parcours portant sur l‘apprentissage du langage Python. Il s‘appuie sur un graphe des épistèmes, une navigation dynamique et un bilan de connaissances personnalisé. Ce prototype a fait l‘objet d‘une expérimentation qui nous a donné la possibilité d‘évaluer les principes introduits et d‘analyser certains usages. / This research work mainly concerns means of assistance in individualized navigation through an epistemic hypermedia. We have a number of resources that can be formalized by a directed acyclic graph (DAG) called the graph of epistemes. After identifying resources and pathways environments, methods of visualization and navigation, tracking, adaptation and data mining, we presented an approach correlating activities of design or editing with those dedicated to resources‘ use and navigation. This provides ways of navigation‘s individualization in an environment which aims to be evolutive. Then, we built prototypes to test the graph of epistemes. One of these prototypes was integrated into an existing platform. This epistemic hypermedia called HiPPY provides resources and pathways on Python language. It is based on a graph of epistemes, a dynamic navigation and a personalized knowledge diagnosis. This prototype, which was experimented, gave us the opportunity to evaluate the introduced principles and analyze certain uses.

abstract: Computer supported collaborative learning (CSCL) has made great inroads in classroom teaching marked by the use of tools and technologies to support and enhance collaborative learning. Computer mediated learning environments produce large amounts of data, capturing student interactions, which can be used to analyze students’ learning behaviors (Martinez-Maldonado et al., 2013a). The analysis of the process of collaboration is an active area of research in CSCL. Contributing towards this area, Meier et al. (2007) defined nine dimensions and gave a rating scheme to assess the quality of collaboration. This thesis aims to extract and examine frequent patterns of students’ interactions that characterize strong and weak groups across the above dimensions. To achieve this, an exploratory data mining technique, differential sequence mining, was employed using data from a collaborative concept mapping activity where collaboration amongst students was facilitated by an interactive tabletop. The results associate frequent patterns of collaborative concept mapping process with some of the dimensions assessing the quality of collaboration. The analysis of associating these patterns with the dimensions of collaboration is theoretically grounded, considering aspects of collaborative learning, concept mapping, communication, group cognition and information processing. The results are preliminary but still demonstrate the potential of associating frequent patterns of interactions with strong and weak groups across specific dimensions of collaboration, which is relevant for students, teachers, and researchers to monitor the process of collaborative learning. The frequent patterns for strong groups reflected conformance to the process of conversation for dimensions related to “communication” aspect of collaboration. In terms of the concept mapping sub-processes the frequent patterns for strong groups reflect the presentation phase of conversation with processes like talking, sharing individual maps while constructing the groups concept map followed by short utterances which represents the acceptance phase. For “joint information processing” aspect of collaboration, the frequent patterns for strong groups were marked by learners’ contributing more upon each other’s work. In terms of the concept mapping sub-processes the frequent patterns were marked by learners adding links to each other’s concepts or working with each other’s concepts, while revising the group concept map. / Dissertation/Thesis / Masters Thesis Computer Science 2015

abstract: EMBRACE (Enhanced Moved By Reading to Accelerate Comprehension in English) is an IPad application that uses the Moved By Reading strategy to help improve the reading comprehension skills of bilingual (Spanish speaking) English Language Learners (ELLs). In EMBRACE, students read the text of a story and then move images corresponding to the text that they read. According to the embodied cognition theory, this grounds reading comprehension in physical experiences and thus is more engaging. In this thesis, I used the log data from 20 students in grades 2-5 to design a skill model for a student using EMBRACE. A skill model is the set of knowledge components that a student needs to master in order to comprehend the text in EMBRACE. A good skill model will improve understanding of the mistakes students make and thus aid in the design of useful feedback for the student.. In this context, the skill model consists of vocabulary and syntax associated with the steps that students performed. I mapped each step in EMBRACE to one or more skills (vocabulary and syntax) from the model. After every step, the skill level is updated in the model. Thus, if a student answered the previous step incorrectly, the corresponding skills are decremented and if the student answered the previous question correctly, the corresponding skills are incremented, through the Bayesian Knowledge Tracing algorithm. I then correlated the students’ predicted scores (computed from their skill levels) to their posttest scores. I evaluated the students’ predicted scores (computed from their skill levels) by comparing them to their posttest scores. The two sets of scores were not highly correlated, but the results gave insights into potential improvements that could be made to the system with respect to user interaction, posttest scores and modeling algorithm. / Dissertation/Thesis / Masters Thesis Computer Science 2016

O uso de modelos multiníveis é uma alternativa interessante para analisar dados que estão estruturados de forma hierárquica, pois permite a obtenção de diferentes estimativas de parâmetros relativos a grupos distintos e, ao mesmo tempo, leva em consideração a dependência entre as observações em um mesmo grupo. Neste trabalho, desenvolvemos e aplicamos modelos de regressão multiníveis simétricos, a fim de fornecer alternativas ao modelo usual, sob normalidade. Além disso, apresentamos uma breve análise de diagnóstico e estudo de simulação. Como motivação, consideramos dados educacionais, a fim de avaliar se o número de reprovações no histórico escolar do aluno e a infraestrutura da escola são variáveis relevantes que afetam o baixo desempenho dos alunos do ensino básico na disciplina de Matemática / The use of multilevel models is an interesting alternative to analyze data that is structured in a hierarchical manner, since it allows the obtention of different parameters estimates for distinct groups and, at the same time, it takes into account the dependence of observations in the same group. In this dissertation, we develop and apply symmetrical multilevel regression models, for the purpose of providing alternatives to the usual model, under normality. Furthermore we present a brief diagnostics analysis and a simulation study. As motivation, we consider educational data in order to assess whether the number of failures in school history of students and the school infrastructure are important variables that affect the low performance of elementary school students in Mathematics

<p>CAL (Computer Assisted Learning) programs are widespread today in schools and families due to the effectiveness of CAL programs in improving students’ learning and task performance. The flourishing of CAL programs in education has brought large amounts of students’ learning data including log data, performance data, mouse movement data, eye movement data, video data, etc. These data can present students’ learning or problem-solving processes and reflect underlying cognitive processes. These data are valuable resources for educators to comprehend students’ learning and difficulties. However, few data analysis methods can analyze and present CAL data for educators quickly and clearly. Traditional video analysis methods can be time-consuming. Current visualization analysis methods are limited to simple charts or visualizations of a single data type. In this dissertation, I propose a visual learning analytic approach to analyze and present students' problem-solving data from CAL programs. More specifically, a visualization system was developed to present students’ problem-solving data, including eye movement, mouse movement, and performance data, to help educational researchers understand student problem-solving processes and identify students’ problem-solving strategies and difficulties. An evaluation experiment was conducted to compare the visualization system with traditional video analysis methods. Seven educational researchers were recruited to diagnose students’ problem-solving patterns, strategies, and difficulties using either the visualization system or video. The diagnosis task loads and evaluators’ diagnosis processes were measured and the evaluators were interviewed. The results showed that analyzing student problem-solving tasks using the proposed visualization method was significantly quicker than using the video method. In addition, diagnosis using the visualization system can achieve results at least as reliable as the video analysis method. Evaluators’ preferences between the two methods are summarized and illustrated in the dissertation. Finally, the implications of the visual analytic approach in education and data visualization areas are discussed. </p>

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / This research proposes a prediction model that might help reducing the dropout rate of university students in Peru. For this, a three-phase predictive analysis model was designed which was combined with the stages proposed by the IBM SPSS Modeler methodology. Bayesian network techniques was compared with decision trees for their level of accuracy over other algorithms in an Educational Data Mining (EDM) scenario. Data were collected from 500 undergraduate students from a private university in Lima. The results indicate that Bayesian networks behave better than decision trees based on metrics of precision, accuracy, specificity, and error rate. Particularly, the accuracy of Bayesian networks reaches 67.10% while the accuracy for decision trees is 61.92% in the training sample for iteration with 8:2 rate. On the other hand, the variables athletic person (0.30%), own house (0.21%), and high school grades (0.13%) are the ones that contribute most to the prediction model for both Bayesian networks and decision trees.

A multitude of data is being produced from the increase in instructional technology, e-learning resources, and online courses. This data could be used by educators to analyze and extract useful information which could be beneficial to both instructors and students. Educational Data Mining (EDM) extracts hidden information from data contained within the educational domain. In data mining, hybrid method is the combination of various machine learning techniques. Through this dissertation, the novel use of machine learning hybrid techniques was explored in EDM using three educational case studies. First, in consideration for the importance of students’ attention, on and off-task data to analyze the attention behavior of the students were collected. Two feature selection techniques, Principal Component Analysis and Linear Discriminant Analysis, were combined to improve the classification accuracies for classifying the students’ attention patterns. The relationship between attention and learning was also studied by calculating Pearson’s correlation coefficient and p-value. Our examination was then shifted towards academic performance as it is important to ensuring a quality education. Two different 2D- Convolutional Neural Network (CNN) models were concatenated and produced a single model to predict students’ academic performance in terms of pass and fail. Lastly, the importance of using machine learning in online learning to maintain academic integrity was considered. In this work, primarily a traditional machine learning algorithms were used to predict the cheaters in an online examination. 1D CNN architecture was then used to extract the features from our cheater dataset and the previously used machine learning model was applied on extracted features to detect the cheaters. Such type of hybrid model outperformed the original traditional machine learning model and CNN model when used alone in terms of classification accuracy. The three studies reflect the use of machine learning application in EDM. Classification accuracy is important in EDM because different educational decisions are made based on the results of our model. So, to increase the accuracies, a hybrid method was employed. Thus, through this dissertation it was successfully shown that hybrid models can be used in EDM to improve the classification accuracies.

The overall goal of this work is to support student success in computer science. First, I introduce BitFit, an ungraded practice programming tool built to provide students with a pressure-free environment to practice and build confidence working through weekly course material. BitFit was used in an introductory programming course (CSC 110) at the University of Victoria for 5 semesters in 2015 and 2016. The contributions of this work are a number of studies done analyzing the log data collected by BitFit over those years. First, I explore whether patterns can be identified in log data to differentiate successful from unsuccessful students, with a specific focus on identifying students at-risk of failure within the first few weeks of the semester. Next, I separate out only those students who struggle early in the semester, and examine their changes in programming behaviour over time. The goal behind the second study is to differentiate between transient and sustained struggling, in an attempt better understand the reasons successful students are able to overcome early struggles. Finally, I combine survey data with log data to explore whether students understand whether their study habits are likely to lead to success. Overall, this work provides insight into the factors contributing to behavioural change in an introductory programming course. I hope this information can aid educators in providing supportive intervention aimed at guiding struggling students towards more productive learning strategies. / Graduate / 0984 / 0525 / 0710 / aestey@uvic.ca

Submitted by William Justo Figueiro (williamjf) on 2015-07-28T20:32:18Z No. of bitstreams: 1 27d.pdf: 4180188 bytes, checksum: 617cda1d8cedaa910bb66676e96c33d1 (MD5) / Made available in DSpace on 2015-07-28T20:32:18Z (GMT). No. of bitstreams: 1 27d.pdf: 4180188 bytes, checksum: 617cda1d8cedaa910bb66676e96c33d1 (MD5) Previous issue date: 2014-04-15 / Nenhuma / Aplicações que fazem uso de tecnologias como Mineração de Dados Educacionais (MDE) e Learning Analytics (LA) vêm sendo adotadas na mitigação da evasão escolar, disponibilizando informações sobre os alunos que são utilizadas em intervenções pedagógicas. Os trabalhos estudados sobre a implementação destas aplicações priorizam a descrição das técnicas empregadas e existem poucas avaliações da sua utilização em larga escala, além da falta de detalhamento sobre as causas da evasão. Este trabalho apresenta um estudo de fatores envolvidos no fenô- meno de evasão escolar e descreve a utilização de um sistema para MDE e LA durante 18 meses em cursos de graduação na modalidade de Educação a Distância. É ampliada a análise dos fatores tradicionalmente monitorados e utilizados nos sistemas de MDA e LA, com a inclusão de elementos associados ao papel exercido pelos docentes e pelo conjunto de aspectos metodológicos de cada instituição. O sistema possui como diferencial a flexibilidade na integração e utilização dos dados gerados no processo de mediação digital, o que permite que necessidades de diferentes ferramentas de apoio sejam disponibilizadas. Resultados positivos destacados são a identificação de perfis de alunos evasores e a realização de intervenções pedagógicas, com redução das médias da evasão. / Educational Data mining (EDM) and Learning Analytics (LA) applications have been adopted in mitigation of dropout, providing information about students who are employed in pedagogical interventions. The most papers about the implementation of these systems describe the techniques employed, there are few evaluations of their large-scale use, apart from the lack of detail about the causes of dropout. This work presents a study of factors involved in dropout and describes the use of a system for EDM and LA during 18 months for undergraduate courses in distance education. The analysis of the factors traditionally monitored and used in EDM and LA systems is extended, with the inclusion of elements associated with the role exercised by the teachers and by institutional methodological aspects. The system has flexibility in integration and use of data generated in the process of digital mediation, which allows different support tools to be available. Some results are the identification of evaders students profiles and the realization of pedagogical actions with reducing evasion.

abstract: Due to large data resources generated by online educational applications, Educational Data Mining (EDM) has improved learning effects in different ways: Students Visualization, Recommendations for students, Students Modeling, Grouping Students, etc. A lot of programming assignments have the features like automating submissions, examining the test cases to verify the correctness, but limited studies compared different statistical techniques with latest frameworks, and interpreted models in a unified approach. In this thesis, several data mining algorithms have been applied to analyze students’ code assignment submission data from a real classroom study. The goal of this work is to explore and predict students’ performances. Multiple machine learning models and the model accuracy were evaluated based on the Shapley Additive Explanation. The Cross-Validation shows the Gradient Boosting Decision Tree has the best precision 85.93% with average 82.90%. Features like Component grade, Due Date, Submission Times have higher impact than others. Baseline model received lower precision due to lack of non-linear fitting. / Dissertation/Thesis / Masters Thesis Computer Science 2019