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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 37 Documents
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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.
Institutional Barriers and Strategic Enablers: A Knowledge Management Scale Development Study in Indonesian Higher Education Risana Rachmatan; Muhammad Syamsul Maarif; Muhammad Adam; Hizir Sofyan
Journal of Educational Management and Learning Vol. 4 No. 1 (2026): May 2026
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v4i1.369

Abstract

Knowledge management (KM) is increasingly important for Indonesian universities, which face rapid technological change and heightened demands for transparency, quality, and competitiveness. This study developed and validated a measurement instrument to identify key barriers and enablers of KM implementation in Indonesian higher education. A 22-item scale was developed for the local context and organized into six aspects: trust in individuals, trust in management, reward system, organizational process, IT, and technical support. The scale was produced in two versions: one for lecturers/education staff and one for students. Content validity was evaluated using the Content Validity Ratio (CVR) and the Content Validity Index (CVI) with expert panels (eight raters), followed by a pilot administration to 60 respondents (30 lecturers/staff and 30 students). The scale demonstrated strong content validity (mean I-CVI 0.87–0.91), acceptable item discrimination (lecturer/staff: 0.471–0.834; student: 0.250–0.785), and high internal consistency (Cronbach’s alpha: 0.952 for lecturer/staff; 0.923 for students). These results indicate that the instrument is robust for diagnosing KM enablers and barriers in Indonesian universities and can support targeted policies and interventions. Future studies should expand validation across diverse institutions nationwide.
Enhancing Teacher Performance Through Coaching: A Study on Differentiated Learning in Primary Schools Nuti Handayani; Hizir Sofyan; Niswanto Niswanto
Journal of Educational Management and Learning Vol. 4 No. 1 (2026): May 2026
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v4i1.276

Abstract

This study examines the role of teacher coaching programs in enhancing the effectiveness of differentiated learning in primary schools, focusing on SD Negeri Langgo and SD Negeri 2 Peukan Pidie. Using a qualitative research approach with a descriptive method, the study explores how structured teacher coaching influences instructional strategies, teacher competence, and student learning outcomes. Data were collected through semi-structured interviews, classroom observations, and document analysis, followed by thematic analysis. The findings indicate that teacher coaching programs significantly improved instructional quality, although the coaching approaches varied between schools. SD Negeri Langgo implemented individual mentoring, while SD Negeri 2 Peukan Pidie emphasized peer discussions and collaborative learning communities. Both approaches helped teachers adapt their teaching methods to better support students with diverse learning needs. However, challenges such as limited professional training, time constraints, resource shortages, and varying student motivation hindered full implementation of differentiated instruction. Despite these challenges, the study found that differentiated learning positively impacted student engagement, literacy, and numeracy skills. Students demonstrated higher levels of participation and improved academic performance following the enhancement of teacher coaching programs. The study highlights the importance of continuous professional development, institutional support, and access to instructional resources to sustain effective differentiated learning practices. The study concludes that teacher coaching is essential in fostering effective differentiated instruction. It recommends expanding teacher training programs, providing better resource allocation, and integrating motivation-enhancing strategies to improve student learning experiences. Future research should explore long-term effects of differentiated learning and strategies for enhancing student motivation in diverse classrooms
Enhancing Elementary Students’ Computational Thinking through 'Code4Kids' Coding-Based Worksheets Rafni Fajriati; Martines Martines; Rizki Julia Utama
Journal of Educational Management and Learning Vol. 4 No. 1 (2026): May 2026
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v4i1.373

Abstract

This study investigates the early-stage development and feasibility of the Code4Kids worksheet, an unplugged coding-based instructional resource designed to enhance computational thinking (CT) and digital literacy among fourth-grade elementary students. An exploratory Research and Development (R&D) approach was employed, focusing on needs analysis, prototype development, and expert validation. Data were collected through classroom observations, teacher interviews, and structured expert evaluations to examine contextual readiness, instructional design alignment, and pedagogical suitability. The needs analysis revealed limited student digital literacy, insufficient structured CT materials, and low teacher confidence in implementing coding-related instruction. Based on these findings, a theory-informed worksheet prototype integrating decomposition, pattern recognition, abstraction, and algorithmic thinking was developed. Expert validation results (mean score = 4.6/5) and positive teacher feedback indicate that the prototype is developmentally appropriate, pedagogically coherent, and feasible for classroom implementation in low-resource settings. The study concludes that unplugged, worksheet-based coding materials can serve as an accessible entry point for integrating CT in elementary education. These findings provide an empirical foundation for future classroom trials and broader implementation of the Code4Kids model.
A Data-Driven Classification of Student Productivity Based on Academic Performance, Lifestyle Patterns, and Digital Habits Teuku Rizky Noviandy; Hizir Sofyan; Yosza Dasril; Rinaldi Idroes
Journal of Educational Management and Learning Vol. 4 No. 1 (2026): May 2026
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v4i1.433

Abstract

Student productivity is influenced by various factors, including academic habits, lifestyle characteristics, and digital distraction behaviors. The increasing use of digital technologies, such as smartphones, social media, and online gaming, has created new challenges for maintaining student focus and academic performance. Therefore, understanding and predicting student productivity levels is important for supporting effective educational management and student success. This study aims to classify student productivity levels using machine learning techniques based on academic, behavioral, and digital distraction variables. The study utilized the Student Productivity & Digital Distraction Dataset obtained from Kaggle, consisting of 20,000 student records. The productivity score was transformed into five productivity categories, namely very low, low, medium, high, and very high productivity. Four machine learning algorithms, including Decision Tree (DT), and K-Nearest Neighbors (KNN), Gradient Boosting (GB), and Random Forest (RF) were evaluated using accuracy, precision, recall, F1-score, and confusion matrix analysis. The results showed that RF achieved the best performance with an accuracy of 81.15%, precision of 81.35%, recall of 81.15%, and F1-score of 81.23%, outperforming GB, DT, and KNN. The findings indicate that ensemble learning methods are more effective in modeling the complex relationships among academic habits, lifestyle factors, digital distraction, and student productivity. Furthermore, the study demonstrates the potential of machine learning as a decision-support tool for educational management, enabling the identification of students with different productivity levels and supporting data-driven interventions to improve academic outcomes.
Online Gaming, Digital Entertainment, and Academic Performance among Students: A Review Teuku Rizky Noviandy; Rinaldi Idroes
Journal of Educational Management and Learning Vol. 4 No. 1 (2026): May 2026
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v4i1.434

Abstract

Online gaming and digital entertainment have become increasingly integrated into students’ daily lives through smartphones, computers, gaming platforms, video streaming, and social media. This review paper examines the relationship between online gaming, digital entertainment, and academic performance among students. The discussion highlights that digital entertainment can produce both positive and negative effects depending on duration, timing, content type, self-regulation, and learning context. Moderate and purposeful use may support cognitive skills, social interaction, motivation, and digital literacy. In contrast, excessive or problematic use may contribute to distraction, reduced study time, poor sleep quality, lower learning engagement, and weaker academic outcomes. Quantitative findings from previous studies show that many students engage frequently in online gaming and digital entertainment, including long weekly gaming time, mobile gaming as a major source of entertainment, and frequent social media checking. Overall, the reviewed literature suggests that the relationship between digital entertainment and academic performance is conditional rather than absolute. Healthy digital habits, effective time management, and guidance from parents, teachers, and educational institutions are important to help students balance entertainment and academic responsibilities.

Page 4 of 4 | Total Record : 37

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