Metodologías de análisis de los big data en las plataformas educativas

La proliferación de nuevas plataformas educativas por Internet y el avance de la educación online ha abierto nuevas posibilidades de análisis debido al gran volumen de datos generados y almacenados en los servidores. Los usuarios dejan trazas de su actividad, y esta actividad posibilita nuevos análisis del comportamiento de estudiantes y de los contenidos compartidos, difícilmente realizables en la educación cara a cara tradicional. Este trabajo aporta un resumen de las diversas metodologías aplicables a los grandes volúmenes de datos generados por las plataformas educativas, clasificables dentro de los Big Data, así como los diversos campos en los que podrían aplicarse y las mejoras que podrían introducir en el desarrollo de las propias herramientasConsejería de Economía, Innovación, Ciencia y Empleo, Junta de Andalucía (Proyecto de Excelencia referencia P12-SEJ-328)

Using Smartpens to Examine and Influence the Relationship between Homework Habits and Academic Achievement in Introductory Engineering Courses

This dissertation examines students’ homework behaviors and their relationship to academic achievement in introductory engineering courses. Much of the prior work examining the relationship between homework and achievement has relied on student self-reports of homework effort. Our results demonstrate that such self-reports are problematic. Instead, we avoid this methodological shortcoming by using smartpens to objectively measure students’ learning activities in an unobtrusive manner and with a high level of fidelity. This dissertation examines how much, how frequently, and when students work on their homework assignments, and if these factors are related to achievement. This dissertation also examines if informing students of their homework behavior influences them to change that behavior and improve achievement. This work makes four major contributions. First, we developed quantitative measures of student homework behavior that are related to academic achievement. Second, we demonstrate that self-reported measures of student homework effort are problematic. Third, we show that measures of homework effort early in a course are nearly as effective at predicting achievement as measures from the entire course. This result suggests that student behavior does not change significantly over a course. Finally, we show that informing students of their homework behaviors, and providing suggestions for improving those behaviors, is an insufficient motivator to change behaviors and improve achievement. This result suggests a two-stage model of metacognition for study behaviors, requiring both monitoring (i.e., being aware of how one is studying) and regulation (i.e., adjusting how one studies based on feedback) to affect changes in behavior.This work makes both applied and methodological contributions to educational research. In contrast to existing research, our results demonstrate a strong and consistent relationship between students’ homework behaviors and academic achievement. Additionally, this work shows that students’ homework behaviors are established early in a course, and tend to remain relatively constant throughout a course.This work highlights the potential of educational data mining and smartpen technology for educational research. Our results confirm the unreliability of studies employing self-reports. Our studies also speak to the value of replication in education research