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Contact Name
Rengga Prakoso Nugroho
Contact Email
rengganugroho@teknologipendidikan.or.id
Phone
+626285748250120
Journal Mail Official
research@teknologipendidikan.or.id
Editorial Address
Direktorat Program Akademik dan Kependidikan PT. Inovasi Teknologi Pembelajaran Lumbang, Desa Sawocangkring Sidoarjo, Jawa Timur Indonesia
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Kab. sidoarjo,
Jawa timur
INDONESIA
Journal of Educational Technology Studies and Applied Research
ISSN : 30629454     EISSN : 30629454     DOI : https://doi.org/10.70125/jetsar
The focus and scope accepted in JETSAR is the ethical study and application of theory, research, and practices to advance knowledge, improve learning and performance, and empower learners through strategic design, management, implementation, and evaluation of learning experiences and environments using appropriate processes and resources. The Research Section specifically welcomes qualitative, quantitative and mixed-methods studies that examine the application of theory, technology, design within the educational environment. The context of the area ranges from basic education (K-12), higher education to adult learning (e.g. professional and vocational training). Manuscripts that address trends and issues, literature reviews are also included in this section. This section publishes well-written articles that highlight research aspects, the application of theory in learning practices and provide a comprehensive source of research in the field of Educational Technology. The development section publishes research on the design, implementation, evaluation and management of various learning technologies and learning environments. Empirical evidence-based technology evaluations, as well as the results of trials of innovative technologies for learning are welcome in the Development section. The research or study should address the impact, implications, future plans and empirical evidence that relates the results to the conclusions on the technology that has been developed. In addition to these two sections, Journal of Educational Technology on Studies and Applied Research also publishes special issues in accordance with emerging trends and issues in the educational technology environment.
Articles 31 Documents
Academic Dropout Prediction Using Institutional Data: A Comparative Study of Machine Learning Methods Leonardo Gomes de Melo; Lineu Alberto Cavazani de Freitas; Anderson Luiz Ara Souza
Journal of Educational Technology Studies and Applied Research Vol 2 No 3 (2026): [6] - 2026 April
Publisher : Teknologi Pendidikan ID

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70125/v93ga685

Abstract

The rise in academic dropout rates at universities across Brazil is notable, especially in the post-COVID-19 pandemic period. At the Federal University of ParanĂ¡ (UFPR), the problem escalated after 2019, reaching peaks in 2021 and 2022, underscoring the urgency for targeted intervention. This study aims to compare different Machine Learning methods - Logistic Regression (LR), Decision Trees (DT), Random Forest (RF), Stochastic Gradient Boosting (GBM), Support Vector Machines (SVM), and Neural Networks (NN) - using institutional data in undergraduate courses from UFPR (2009-2024). The fitted models are based on academic performance and course-related variables, specifically the Academic Performance Index (IRA), the Proportion of Failed Courses (PropRep), the course's academic type (Degree), and its area of knowledge (THE Area). Results showed that while all models achieved similar predictive performance (MCC ~ 0.70 | Precision > 0.90), in general, the Logistic Regression model offers high interpretability and easiness to implementation. The LR model is proposed as the most robust and transparent predictive tool for the university's management.

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