Clickstream-based outcome prediction in short video MOOCs

In this paper, we present a data mining approach for analysing students’ clickstream data logged by an e-learning platform and we propose a machine learning procedure to predict course completion of students. For this, we used data from a short MOOC course which was motivated by the teachers of elementary schools. We show that machine learning approaches can accurately predict the course outcome based on clickstream data and also highlight patterns in data which provide useful insights to MOOC developers

Beyond Surveys: Analyzing Software Development Artifacts to Assess Teaching Efforts

This Innovative Practice Full Paper presents an approach of using software development artifacts to gauge student behavior and the effectiveness of changes to curriculum design. There is an ongoing need to adapt university courses to changing requirements and shifts in industry. As an educator it is therefore vital to have access to methods, with which to ascertain the effects of curriculum design changes. In this paper, we present our approach of analyzing software repositories in order to gauge student behavior during project work. We evaluate this approach in a case study of a university undergraduate software development course teaching agile development methodologies. Surveys revealed positive attitudes towards the course and the change of employed development methodology from Scrum to Kanban. However, surveys were not usable to ascertain the degree to which students had adapted their workflows and whether they had done so in accordance with course goals. Therefore, we analyzed students' software repository data, which represents information that can be collected by educators to reveal insights into learning successes and detailed student behavior. We analyze the software repositories created during the last five courses, and evaluate differences in workflows between Kanban and Scrum usage

Moodle-based data mining potentials of MOOC systems at the University of Szeged

In today's world virtual online educational platforms emerge literally on daily bases and many offer MOOC-based courses. With the appearance of MOOC, educational platforms have gained an additional boost, a new aspect in their evolutionary process, which has opened a new field of research thanking to the extraction of logging information within the frames of data mining. It has become clear that educators will be able to tailor their courses by merging the two previously mentioned fields and by carrying out MOOC-based data mining, targeting pedagogical aspects. This field of research seems promising and important, thus a faculty at the University of Szeged has created its own MOOC educational platform which has been set to facilitate data mining by implementing a wide range of logging algorithms. The data would be processed through a complex Artificial Intelligence program, which, in the short term, could reveal new and exciting pedagogical findings, while in the long run, the supervisors could put together a platform that would help and notify educators about relevant information. It would become possible to create adaptive educational materials, as well. This work aims at clarifying how such platforms function and what the steps of data collection and evaluation are

Um modelo preditivo no diagnóstico de aprendizagem de programação / A predictive model in the programming learning diagnosis

Inúmeras tecnologias são desenvolvidas como apoio ao processo de aprendizagem de programação. Todavia há uma grande carência de modelos eficazes nos projetos de tecnologias educacionais desta área. Para atender essa demanda, apresentamos neste artigo um modelo para criação de perfis de aprendizagem no diagnóstico de aprendizagem de programação. Esse modelo fornece os requisitos de um sistema online de monitoramento e controle dos componentes das habilidades através da criação de perfis com base nos históricos de aprendizagem. Através deste modelo visa-se alcançar progressos reais de aprendizagem na disciplina de programação de computadores.