Educational Data Mining

Data mining in education

“…EDM analyze data generated by any type of information system supporting learning or education (in schools, colleges, universities, and other academic or professional learning institutions providing traditional and modern forms and methods of teaching, as well as informal learning). These data7 are not restricted to interactions of individual students with an educational system (e.g., navigation behavior, input in quizzes and interactive exercises) but might also include data from collaborating students (e.g., text chat), administrative data (e.g., school, school district, teacher), demographic data (e.g., gender, age, school grades), student affectivity (e.g., motivation, emotional states), and so forth. These data have typical characteristics such as multiple levels of hierarchy (subject, assignment, question levels), context (a particular student in a particular class encountering a particular question at a particular time on a particular date), fine grained (recording of data at different resolutions to facilitate different analyses, e.g., recording data every 20 second), and longitudinal (much data recorded over many sessions for a long period of time, e.g., spanning semester and year‐long courses).…”

“…There are a number of popular methods within EDM 5,7,13,29. Some of them are widely acknowledged to be universal across types of data mining, such as prediction, clustering, outlier detecting, relationship mining, SNA, process mining, and text mining.…”

“…The first and most popular review of EDM research was presented in a journal,5 and was followed by a more theoretical paper (Baker and Yacef, 2009) and a more complete review 6. A first and wide‐ranging book chapter review was oriented to the application of DM in e‐learning,12 a second and shorter book chapter was more oriented to ITS13 and a third book chapter was the most generic but the shortest 7. Finally, a recent report was published by the US Office of Educational Technology about how to enhance teaching and learning through EDM and LA 10…”

Estudio sobre Estilos de Aprendizaje mediante Minería de Datos como apoyo a la Gestión Académica en Instituciones Educativas

“…En la literatura se encuentran diversos estudios que varían en relación a las técnicas de minería de datos, a la elaboración de modelos, a la población, a las formas de adquirir los datos y a la aplicación de test de identificación de estilos de aprendizaje. Los objetivos más comunes en los estudios son el análisis de la deserción y el fracaso escolar en la formación superior mediante la aplicación de algoritmos de agrupamiento y de predicción (Puello & Fernández, 2013) (Scheuer & McLaren, 2012) (Vera, Morales, & Soto, 2012) (Valía, et al, 2017) (Carmona, Vergara, Oviedo, Amon, & Vélez, 2018).…”

Complex network models, graph mining and information extraction from real-world systems

“…text chat), administrative data (like school, school district, teacher), and demographic data (like gender, age, school grades). Some discussions on educational data mining can be found in [88,159,160,166]. Databases of educational institutes, where the data is produced by complex administration systems, contain the administration of the daily work of teachers and students, like descriptions of the lessons including the equipment and educational methods that were used, the areas of competence that have been developed, the students who participated and their marks and level, among other things.…”