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Journal of Educational Management and Learning
Published by PT. Heca Sentra Analitika
ISSN : -     EISSN : 30251117     DOI : https://doi.org/10.60084/jeml
Core Subject : Education,
Education Languange, Linguistic, Communication & Media Mathematics Other
Journal of Educational Management and Learning (JEML) is a prestigious peer-reviewed academic publication that focuses on original research articles and review articles in the field of education management and learning. JEML seeks to encourage interdisciplinary research that connects educational theories to practical applications and their impact on society. The scope of the Journal of Educational Management and Learning (JEML) may include, but is not limited to, the following areas: educational leadership and policy development, school governance and administration, curriculum development and assessment, educational technology and digital learning, teacher professional development, organizational behavior in educational institutions, educational innovation and entrepreneurship, quality assurance and accreditation in education, student engagement and motivation, education and social justice
Arjuna Subject : Umum - Umum
Articles 32 Documents
 1   2   3   4 
Developing Students’ Creative and Entrepreneurial Skills via Project-based STEM Physics Sartika, Vera; Halim, Abdul; Zainuddin, Zainuddin; Saminan, Saminan; Syukri, Muhammad
Journal of Educational Management and Learning Vol. 3 No. 2 (2025): November 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v3i2.324

Abstract

Physics is a complex subject and requires a high level of understanding. Based on interviews at SMAN 4 Langsa, there has never been a test of creative thinking skills, there has been no learning that develops these skills, and students have a low entrepreneurial spirit. This study aims to improve physics learning through the PjBL-STEM (Project-Based Learning-Science, Technology, Engineering, and Mathematics) model, in improving students' creative thinking skills and entrepreneurial spirit. The research design used in this study was a one-group pretest-posttest design involving 89 grade X students. Data collection was conducted through interviews, questionnaires, and tests developed based on indicators of creative thinking skills. The data were analysed using the percentage formula, N-gain, Normality, and Paired Sample t-test. The instruments used were the Creative Thinking Skills Test and the Entrepreneurship Questionnaire. The results showed that the average N-gain of 0.71 was categorised as high. Based on the N-gain results, it can be concluded that there is an increase between before and after treatment. The results of the entrepreneurship questionnaire showed 45% before treatment and 82% after treatment. From the results of this study, it can be concluded that the PjBL-STEM model can optimise physics learning by improving students' creative thinking skills and entrepreneurial spirit.
The Role of Study Habits, Parental Involvement, and School Environment in Predicting Student Achievement: A Machine Learning Perspective Noviandy, Teuku Rizky; Paristiowati, Maria; Isa, Illyas Md; Idroes, Rinaldi
Journal of Educational Management and Learning Vol. 3 No. 2 (2025): November 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v3i2.350

Abstract

This study explores the application of machine learning techniques to predict student achievement based on study habits, parental involvement, and school environment. Using a dataset from Kaggle comprising academic, behavioral, and contextual variables, four machine learning algorithms, namely K-Nearest Neighbors (KNN), Naïve Bayes, Support Vector Machine (SVM), and Random Forest, were implemented and evaluated. Model performance was evaluated using accuracy, precision, recall, F1-score, ROC curve, and Precision–Recall curves. Results show that all models effectively classified students into low- and high-achievement categories, with SVM achieving the highest accuracy (94.02%) and the strongest overall performance. The findings highlight the potential of machine learning-driven predictive analytics in educational settings, enabling early identification of at-risk students and supporting evidence-based interventions. By integrating diverse factors influencing academic performance, this study demonstrates how data-driven approaches can enhance educational management, inform policy, and promote equitable learning outcomes.

Page 4 of 4 | Total Record : 32

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