Uma ferramenta baseada em grafo para identificação e visualização de trilhas de aprendizagem

Abstract

The Educational area has received great benefits with the use of internet and digital media. Along with new technologies, Virtual Learning Environments (VLE) emerged, in which students and teachers can interact synchronously and asynchronously. The Distance Education began to use the VLE as the main tool to mediate the relationship between students, teachers and content. However, it also has been used as a support tool in blended learning and even the presential teaching. Virtual Learning Environments collect data about users that can be useful to define the learner's profile, his/her behavior and identify their difficulties and needs. Thus, the teacher or the AVA itself can personalize learning and maintain a real-time monitoring. One way to perform a learner monitoring is by observing the actions that he/she takes within the system, these actions make paths that are known as learning paths. In this sense, a Systematic Review of the Literature (SLR) to identify how these paths are represented in VLEs was held. The advantage in the use of learning paths is the transparent monitoring, i.e. no information is requested directly from the learner, the data collected tend to match as close as possible to their actual situation. This work, therefore, describes a model for identification and representation of learning paths that presents a view of actions performed in the LMS Moodle. The model extracts the data stored in database records and uses the structure of graphs as a basis for modeling the information about the students learning paths and present them visually to teachers and tutors. In order to validate the model, a plugin that uses Web standards was developed for Moodle, where its possibilities of use were investigated and validated through analysis of several real scenarios and an application questionnaire based on the Technology Acceptance Model (TAM). The proposed model was applied in two courses offered by the Institute of Computing (ICOMP) during the second academic semester of the year 2015 and proved promising in carrying out the monitoring of learners through a visual tool that aids in behavior analysis through the learning paths.