Browsing by Author "Sigua Loja, Edisson Fernando"

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FlipMyLearning: a tool for monitoring and predicting learner behavior in moodle
(Institute of Electrical and Electronics Engineers (IEEE), 2021) Sigua Loja, Edisson Fernando; Maldonado Mahauad, Jorge Javier; Aguilar Yaguana, Bryan Alexander
The development of technology has meant that in the last two decades Information and Communication Technologies have become more and more involved in the teaching process and have tried to change traditional learning models. With the support of modern technology, platforms have been developed and perfected that encourage the adoption of a new virtual learning paradigm. These platforms store student and teacher interactions with course resources in database engines, information that can be very relevant, but in many cases has not been processed in a way that is useful for use by teachers and students. Therefore, this study aims to implement and evaluate a dashboard for student behavior analysis and dropout prediction in Moodle. The tool will help teachers to know what students do before, during and after a class mediated by virtual platforms. In addition, it will also help students manage their learning process and easily and effectively monitor their progress in the course. Given the analytical nature of the research, exploratory analysis of the Moodle data model, evaluation of existing visualizations and design of the tool based on Moodle architecture were used to develop a dashboard of visualizations and dropout prediction. As a result, FlipMyLearning was implemented, a plugin for the Moodle platform that allows the teacher to monitor the learning process of students for informed decision making. The developed plugin contains visualizations for both the teacher and the student, divided into different sections, each oriented to monitor different aspects of the course. The research conducted shows that the visualizations generated were useful for both teachers and students who participated in the evaluation process. In addition, variables such as time spent, number of sessions and indicators related to cognitive depth and social breadth are useful variables to identify groups of students at risk of dropping out.
Implementación y evaluación de un dashboard para el análisis del comportamiento de los estudiantes y predicción en Moodle
(Universidad de Cuenca, 2021-04-16) Sigua Loja, Edisson Fernando; Aguilar Yaguana, Bryan Alexander; Maldonado Mahauad, Jorge Javier
The development of technology has meant that in the last two decades Information and Communication Technologies have become more and more involved in the teaching process and have tried to change traditional learning models. With the support of modern technology, platforms have been developed and perfected that encourage the adoption of a new virtual learning paradigm. These platforms store student and teacher interactions with course resources in database engines, information that can be very relevant, but in many cases has not been processed in a way that is useful for use by teachers and students. Therefore, this study aims to implement and evaluate a dashboard for student behavior analysis and dropout prediction in Moodle. The tool will help teachers to know what students do before, during and after a class mediated by virtual platforms. In addition, it will also help students manage their learning process and easily and effectively monitor their progress in the course. Given the analytical nature of the research, exploratory analysis of the Moodle data model, evaluation of existing visualizations and design of the tool based on Moodle architecture were used to develop a dashboard of visualizations and dropout prediction. As a result, FlipMyLearning was implemented, a plugin for the Moodle platform that allows the teacher to monitor the learning process of students for informed decision making. The developed plugin contains visualizations for both the teacher and the student, divided into different sections, each oriented to monitor different aspects of the course. The research conducted shows that the visualizations generated were useful for both teachers and students who participated in the evaluation process. In addition, variables such as time spent, number of sessions and indicators related to cognitive depth and social breadth are useful variables to identify groups of students at risk of dropping out