TY - JOUR
T1 - A predictive model implemented in knime based on learning analytics for timely decision making in virtual learning environments
AU - Maraza-Quispe, Benjamín
AU - Valderrama-Chauca, Enrique Damián
AU - Cari-Mogrovejo, Lenin Henry
AU - Apaza-Huanca, Jorge Milton
AU - Sanchez-Ilabaca, Jaime
N1 - Publisher Copyright:
© 2022 by the authors.
PY - 2022/2
Y1 - 2022/2
N2 - 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.
AB - 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.
KW - Academic
KW - Environments
KW - KNIME
KW - Learning
KW - Learning analytics
KW - Model
KW - Performance
KW - Prediction
KW - Virtual
UR - http://www.scopus.com/inward/record.url?scp=85121593658&partnerID=8YFLogxK
U2 - 10.18178/ijiet.2022.12.2.1591
DO - 10.18178/ijiet.2022.12.2.1591
M3 - Artículo
AN - SCOPUS:85121593658
VL - 12
SP - 91
EP - 99
JO - International Journal of Information and Education Technology
JF - International Journal of Information and Education Technology
SN - 2010-3689
IS - 2
M1 - 1591
ER -