Finding Association Rules in College Course Progression

Association Rules are a data mining technique that aims at finding patterns in data that explain how different elements of the data influence each other. In this project this technique is used to find associations that describe the trends of college course completions and results. This project used a dataset from the University of Évora for rule finding. This paper shows how the dataset had to be preprocessed first in order to be mined, and describes the techniques and algorithms used

Educational data mining applied to Moodle data from the University of Évora

E-Learning tem vindo a ganhar popularidade como forma de transmissão de conhecimentos a nível educacional graças aos avanços nas tecnologias, como por exemplo, a Internet. Instituições como universidades e empresas têm vindo a usar E-Learning para a transmissão de conteúdos educacionais para locais remotos estendendo o seu alcance a estudantes e colaboradores que estão fisicamente distantes. Sistemas chamados “Learning Management Systems”, como o Moodle, existem para organizar E-Learning. Eles oferecem plataformas online onde professores e educadores podem publicar conteúdo, organizar actividades, fazer avaliações, e outro tipo de ações relacionadas, de modo a que os estudantes possam aprender e serem avaliados. Estes sistemas geram e guardam muitos dados relacionados não só com o seu uso, mas também relacionados com notas de estudantes. Este tipo de dados são frequentemente chamados de Dados Educacionais. Métodos de Data Mining são aplicados a estes dados de modo a fazer suposições não triviais. As técnicas aplicadas tomam inspiração de projectos semelhantes no campo da Data Mining Educacional. Este campo consiste na aplicação de métodos de Data Mining a Dados Educacionais. Neste projecto, um repositório de dados do Moodle da Universidade de Évora foi explorado. Técnicas de aprendizagem supervisionada são aplicadas aos dados de modo a mostrar como é possível prever o sucesso de estudantes a partir do seu uso do Moodle. Métodos de aprendizagem não supervisionada são também aplicados de modo a mostrar como há divisões nos dados; Abstract: E-Learning has been rising in popularity as a way to deliver training due to the advancements of technologies, like the Internet. Institutions such as universities and companies have been making use of E-Learning to deliver training to remote locations extending their reach to students and employees who are physically distant. Systems called Learning Management Systems, like Moodle, exist to organize E-Learning. They provide online platforms where professors and educators can publish content, organize activities, perform evaluations, and so on, in order for students to learn and get evaluated. These systems generate and store lots of data regarding not only their usage, but also regarding the grades of students. This kind of data is often referred too as Educational Data. Data Mining techniques are applied to this data in order to make non trivial assumptions. The techniques applied take inspiration from similar projects within the field of Educational Data Mining. This field consists in applying Data Mining Techniques to Education Data. In this project, a data repository from the Moodle of the University of Évora is explored. Supervised learning techniques are applied to this data in order to show how it is possible to make predictions about the success of students based on their usage of Moodle. Unsupervised learning techniques are also applied in order to show how data is divisible

Data mining and statistical analysis of educational data

The advancement of technologies related with the internet has enabled E-Learning to gain popularity as a way of transmitting knowledge. Universities and Companies, among others institutions, have been using E-Learning to disseminate educational content to remote locations, reaching out students, researchers and employees who are physically distant. The Moodle platform is an example of a Learning Management Systems (LMS). LMS provide on-line platforms where teachers and trainers can publish contents organized into activities, conduct assessments, and other tasks so that the students involved can learn and be assessed. In addition, LMS generate and store large amounts of data, named Educational Data, not only from the users activities but also functional data. This work presents data mining techniques applied to Educational Data. From the Moodle data repository of the University of E´vora, we apply supervised learning techniques with the aim of predict the students success from their interaction with Moodle. We will also show interesting conclusions when unsupervised learning techniques are applied