Sustainable e-Learning by Data Mining—Successful Results in a Chilean University

Abstract

People are increasingly open to using online education mainly to break the distance and time barriers of presential education. This type of education is sustainable at all levels, and its relevance has increased even more during the pandemic. Consequently, educational institutions are saving large volumes of data containing relevant information about their operations, but they do not know why students succeed or fail. The Knowledge Discovery in Databases (KDD) process could support this challenge by extracting innovative models to identify the main patterns and factors that could affect the success of their students in online education programs. This work uses the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology to analyze data from the Distance Education Center of the Universidad Católica del Norte (DEC-UCN) from 2000 to 2018. CRISP-DM was chosen because it represents a proven process that integrates multiple methodologies to provide an effective meta-process for data knowledge projects. DEC-UCN is one of the first centers to implement online learning in Chile, and this study analyses 18,610 records in this period. The study applies data mining, the most critical KDD phase, to find hidden data patterns to identify the variables associated with students’ success in online learning (e-learning) programs. This study found that the main variables explaining student success in e-learning programs are age, gender, degree study, educational level, and locality.

Did the Pandemic Affect the Quality and Level of Knowledge and Performance (Case Study)

“…The students from Master's degrees are older than bachelor students, and they in most cases are students who have studied in Faculty of Technics and Technology before. That results are with synchrony to Sánchez et al, which found that the variables explaining student e-learning success are age, gender, degree of study, educational level, and locality [11]. The effectiveness of virtual learning varies amongst age groups [12].…”

Quantitative Analysis and Prediction of Academic Performance of Students Using Machine Learning

“…Furthermore, machine learning is helpful for other complex problems of education evaluation; (4) Machine learning is an excellent tool for evaluating students' academic performance and understanding educational law and learning behavior. A machine learning-based academic performance model is helpful to guide the education process and update the learning strategy; (5) This study illustrated the quantitative model of academic performance based on machine learning and the collected dataset. Due to the complexity of education behavior, this study only investigated some machine learning technology.…”

“…The early identification of students at risk of underperformance, provision of assistance, and enhancement of the quality of teaching and learning necessitates using quantitative analysis and assessment for academic performance. In order to reveal the complex relationship between academic performance and its influencing factors, data mining was applied to the education system, and Education Data Mining (EDM) was defined and developed in the last decades [5,6]. EDM, a specialized branch within the field of data mining, focuses on analyzing educational data to gain insights into teaching and learning patterns in the education system.…”

Early detection of student degree-level academic performance using educational data mining

“…In classification, educational data is fed to an algorithm specifically designed to infer or predict the value of an attribute (class label) based on the patterns or relationships discovered within certain other attributes (predictor variables). Classification has been applied at various levels of granularity to address an ever-increasing set of problems within the educational domain such as inferring a student’s emotional state ( Dmello et al, 2008 ), predicting student drop-outs ( Agrusti, Bonavolontà & Mezzini, 2019 ; Márquez-Vera et al, 2016 ; Delen, 2010 ), developing recommender systems ( Mimis et al, 2019 ; Erdt, Fernandez & Rensing, 2015 ), predicting student retention ( Shafiq et al, 2022 ), examining the use of learning materials uploaded in an e-learning platform ( Valsamidis et al, 2011 ), and to identify patterns associated with student success in e-learning platforms ( Sánchez et al, 2023 ). A key application area has been predicting student academic outcomes ( Xiao, Ji & Hu, 2022 ; Nahar et al, 2021 ; Viberg et al, 2018 ; Romero & Ventura, 2020 ; Fernandes et al, 2019 ).…”