Predictive Model of Student Academic Performance from LMS data based on Learning Analytics

Benjamin Maraza-Quispe, Enrique Damian Valderrama-Chauca, Lenin Henry Cari-Mogrovejo, Jorge Milton Apaza-Huanca

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

The present research aims to implement a predictive model in the KNIME platform to analyze and compare the prediction of academic performance using data from a Learning Management System (LMS), identifying students at academic risk in order to generate timely and timely interventions. The CRISP-DM methodology was used, structured in six phases: Problem analysis, data analysis, data understanding, data preparation, modeling, evaluation and implementation. Based on the analysis of online learning behavior through 22 behavioral indicators observed in the LMS of the Faculty of Educational Sciences of the National University of San Agustin. These indicators are distributed in five dimensions: Academic Performance, Access, Homework, Social Aspects and Quizzes. The model has been implemented in the KNIME platform using the Simple Regression Tree Learner training algorithm. The total population consists of 30,000 student records from which a sample of 1,000 records has been taken by simple random sampling. The accuracy of the model for early prediction of students' academic performance is evaluated, the 22 observed behavioral indicators are compared with the means of academic performance in three courses. The prediction results of the implemented model are satisfactory where the mean absolute error compared to the mean of the first course was 3. 813 and with an accuracy of 89.7%, the mean absolute error compared to the mean of the second course was 2.809 with an accuracy of 94.2% and the mean absolute error compared to the mean of the third course was 2.779 with an accuracy of 93.8%. These results demonstrate that the proposed model can be used to predict students' future academic performance from an LMS data set.

Idioma originalInglés
Título de la publicación alojadaProceedings of 2021 13th International Conference on Education Technology and Computers, ICETC 2021
EditorialAssociation for Computing Machinery
Páginas13-19
Número de páginas7
ISBN (versión digital)9781450385114
DOI
EstadoPublicada - 22 oct. 2021
Evento13th International Conference on Education Technology and Computers, ICETC 2021 - Virtual, Online, China
Duración: 22 oct. 202125 oct. 2021

Serie de la publicación

NombreACM International Conference Proceeding Series

Conferencia

Conferencia13th International Conference on Education Technology and Computers, ICETC 2021
País/TerritorioChina
CiudadVirtual, Online
Período22/10/2125/10/21

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Publisher Copyright:
© 2021 ACM.

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