Abstract—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.
Index Terms—Model, prediction, learning analytics, performance, academic, environments, virtual, learning, KNIME.
Benjamín Maraza-Quispe, Enrique Damián Valderrama-Chauca, Lenin Henry Cari-Mogrovejo, and Jorge Milton Apaza-Huanca are with Facultad de Ciencias de la Educación, Universidad Nacional de San Agustín de Arequipa, Arequipa-Perú, Peru (e-mail: firstname.lastname@example.org, email@example.com, firstname.lastname@example.org, email@example.com).
Jaime Sanchez-Ilabaca is with Facultad de Ciencias Físicas y Matemáticas, Universidad de Chile, Chile (e-mail: firstname.lastname@example.org).
Cite: Benjamín Maraza-Quispe, Enrique Damián Valderrama-Chauca, Lenin Henry Cari-Mogrovejo, Jorge Milton Apaza-Huanca, and Jaime Sanchez-Ilabaca, "A Predictive Model Implemented in KNIME Based on Learning Analytics for Timely Decision Making in Virtual Learning Environments," International Journal of Information and Education Technology vol. 12, no. 2, pp. 91-99, 2022.Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).