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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 5 Documents
 1 
Search results for , issue "Vol. 1 No. 2 (2023): December 2023" : 5 Documents clear
Using the Flipped Classroom Model to Prevent Sexual Violence in Special Needs Children Mutiawati, Mutiawati; Syahputra, Andy; Nelly, Nelly; Yusian, Desita Ria; Lestari, Soraya; Rusyidah, Rusyidah; Saudah, Saudah
Journal of Educational Management and Learning Vol. 1 No. 2 (2023): December 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v1i2.107

Abstract

The Flipped Classroom learning is designed to develop a future learning model for Special Needs Children (SNC). This article investigates students' perceptions of the impact of learning transitions on the prevention and handling of sexual violence in integrated children with disabilities using gender mainstreaming principles and teacher beliefs. This research utilizes a mixed methods approach within a concurrent design structure that combines primary research using quantitative surveys with semi-structured qualitative interviews. The delivery of sex abuse material through traditional methods such as lectures or tutorials is replaced with flipped Classroom learning through instructional videos. This study found that the transition was generally well-received by students with SNC in inclusive schools. Engaged students tended to perform well in the flipped Classroom learning environment. However, scaffolding in the form of teacher beliefs and gender mainstreaming to prepare students for the transition to flipped Classroom learning is key to promoting knowledge acquisition, performance, engagement, collaboration, and overall positive student experiences.
Boosting Students' Representation Ability in Mathematics Using Numbered Heads Together Nuritasari, Fetty; Qomariyah, Lailatul; Agustin, Dayriqoh; Mulkis, Ismi Malika; Zayyadi, Moh
Journal of Educational Management and Learning Vol. 1 No. 2 (2023): December 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v1i2.108

Abstract

This study aims to examine the efficacy of the Numbered Heads Together learning model in enhancing students' proficiency in whole number calculations. Utilizing a classroom action research methodology, the research was structured into two main cycles, preceded by an initial pre-cycle phase. Each cycle comprises four phases: planning, acting, observing, and reflecting. Data were primarily collected through tests, complemented by student interviews to enrich the test findings. The gathered data were processed and analyzed using qualitative descriptive methods. The participants were nine fifth-grade students from SDN Panglegur 1 Pamekasan, Madura, Indonesia who had previously engaged with integer arithmetic operations. The findings reveal that the Numbered Heads Together model not only significantly improved students' academic performance but also positively influenced their engagement, responsibility, discipline, and confidence in interactive learning scenarios. This improvement was evident from the pre-cycle phase through to the second cycle, with student performance increasing from 33% in the pre-cycle to 56% in the first cycle, and further to 78% in the second cycle.
Does Online Education Make Students Happy? Insights from Exploratory Data Analysis Noviandy, Teuku Rizky; Idroes, Ghalieb Mutig; Hardi, Irsan; Emran, Talha Bin; Zahriah, Zahriah; Rahimah, Souvia; Lala, Andi; Idroes, Rinaldi
Journal of Educational Management and Learning Vol. 1 No. 2 (2023): December 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v1i2.124

Abstract

This study investigates the impact of online education on student happiness. Utilizing a dataset of 5715 students sourced from Bangladesh, we employed an exploratory data analysis to analyze the quantitative data. The key finding is that there is a prevalent trend of dissatisfaction with online education among Bangladeshi students, regardless of demographic factors like age, gender, education level, preferred device for access, or type of academic institution. The dissatisfaction trend highlights the need of continuous improvements and targeted interventions are essential to ensure online education not only enables academic success, but also supports the overall wellbeing and happiness of students in the context of a developing country.
Digital Transformations in Vocational High School: A Case Study of Management Information System Implementation in Banda Aceh, Indonesia Idroes, Rinaldi; Subianto, Muhammad; Zahriah, Zahriah; Afidh, Razief Perucha Fauzie; Irvanizam, Irvanizam; Noviandy, Teuku Rizky; Sugara, Dimas Rendy; Mursyida, Waliam; Zhilalmuhana, Teuku; Idroes, Ghalieb Mutig; Maulana, Aga; Nurleila, Nurleila; Sufriani, Sufriani
Journal of Educational Management and Learning Vol. 1 No. 2 (2023): December 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v1i2.128

Abstract

This study examines the digital transformation in vocational education through the implementation of a Management Information System (MIS) in Banda Aceh, Indonesia. Focused on enhancing educational administration and decision-making, the study provides insightful analysis on the integration of MIS in State Vocational High School (SMK), specifically SMKN 1 and SMKN 3 in Banda Aceh. A purposive sampling method was employed for usability testing. The questionnaire-based usability test revealed high reliability and positive user responses across multiple indicators. Data analysis affirmed the system's high user satisfaction, effectiveness, and ease of use. Despite limitations, the study highlights the significant potential of well-designed MIS in improving operational efficiency and user satisfaction in educational settings. Future research directions include expanding the sample size, conducting longitudinal studies, incorporating qualitative methods, and exploring the impact on educational outcomes, to enhance the generalizability and depth of understanding regarding the role of MIS in education.
Leveraging Artificial Intelligence to Predict Student Performance: A Comparative Machine Learning Approach Maulana, Aga; Idroes, Ghazi Mauer; Kemala, Pati; Maulydia, Nur Balqis; Sasmita, Novi Reandy; Tallei, Trina Ekawati; Sofyan, Hizir; Rusyana, Asep
Journal of Educational Management and Learning Vol. 1 No. 2 (2023): December 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v1i2.132

Abstract

This study explores the application of artificial intelligence (AI) and machine learning (ML) in predicting high school student performance during the transition to university. Recognizing the pivotal role of academic readiness, the study emphasizes the need for tailored interventions to enhance student success. Leveraging a dataset from Portuguese high schools, the research employs a comparative analysis of six ML algorithms—linear regression, decision tree, support vector regression, k-nearest neighbors, random forest, and XGBoost—to identify the most effective predictors. The dataset encompasses diverse attributes, including demographic details, social factors, and school-related features, providing a comprehensive view of student profiles. The predictive models are evaluated using R-squared, Root Mean Square Error, and Mean Absolute Error metrics. Results indicate that the Random Forest algorithm outperforms others, displaying high accuracy in predicting student performance. Visualization and residual analysis further reveal the model's strengths and potential areas for improvement, particularly for students with lower grades. The implications of this research extend to educational management systems, where the integration of ML models could enable real-time monitoring and proactive interventions. Despite promising outcomes, the study acknowledges limitations, suggesting the need for more diverse datasets and advanced ML techniques in future research. Ultimately, this work contributes to the evolving field of educational AI, offering practical insights for educators and institutions seeking to enhance student success through predictive analytics.

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