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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 10 Documents
 1 
Optimizing University Admissions: A Machine Learning Perspective Aga Maulana; Teuku Rizky Noviandy; Novi Reandy Sasmita; Maria Paristiowati; Rivansyah Suhendra; Erkata Yandri; Justinus Satrio; Rinaldi Idroes
Journal of Educational Management and Learning Vol. 1 No. 1 (2023): August 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v1i1.46

Abstract

The university admission process plays a pivotal role in shaping the future of aspiring students. However, traditional methods of admission decisions often fall short in capturing the holistic capabilities of individuals and may introduce bias. This study aims to improve the admission process by developing and evaluating machine learning approach to predict the likelihood of university admission. Using a dataset of previous applicants' information, advanced algorithms such as K-Nearest Neighbors, Random Forest, Support Vector Regression, and XGBoost are employed. These algorithms are applied, and their performance is compared to determine the best model to predict university admission. Among the models evaluated, the Random Forest algorithm emerged as the most reliable and effective in predicting admission outcomes. Through comprehensive analysis and evaluation, the Random Forest model demonstrated its superior performance, consistency, and dependability. The results show the importance of variables such as academic performance and provide insights into the accuracy and reliability of the model. This research has the potential to empower aspiring applicants and bring positive changes to the university admission process.
Interactive Learning for Water Pollution Awareness: A Game-Based Approach Siti Fatimah; Ida Farida; Yulia Sukmawardani
Journal of Educational Management and Learning Vol. 1 No. 1 (2023): August 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v1i1.52

Abstract

This research explores the potential of interactive educational games as a tool to enhance environmental literacy, with a specific focus on water pollution issues. The study introduces a designed game encompassing various interactive modules, such as word games, drag-and-drop tasks, multiple-choice questions, evaluations, and an environmental literacy survey. The validation test was carried out by three validators consisting of material expert lecturers and media experts. The average rcount value for validation test results across material aspects, language aspects, and display (media) aspects was calculated as follows: 0.85, 0.92, and 0.84, resulting in an overall rcount value of 0.87. This overall value signifies high validity and strong interpretational significance. Furthermore, the feasibility test was carried out on 15 chemistry education students who had taken environmental chemistry courses. The average rcount of the feasibility test results from all aspects obtained a percentage value of 87%. This study highlights the importance of game design, evaluating long-term impacts, and integrating interactive games into educational curricula.
Augmented Reality and Student Learning: Analysis of Mental Models of Salt Hydrolysis at SMAN 51 Jakarta, Indonesia Anisa Umayah; Maria Paristiowati; Hanhan Dianhar; Nur Azizah Putri Hasibuan
Journal of Educational Management and Learning Vol. 1 No. 1 (2023): August 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v1i1.53

Abstract

This study aimed to ascertain students' mental models while learning about salt hydrolysis through augmented reality (AR). The study comprised 36 participants from Public High School 51 in Jakarta. A descriptive qualitative approach was adopted for this research, employing various data collection methods such as written drawings, interviews, classroom observations, teacher notes, student worksheets, and final tests. In categorizing students' mental models, three main types emerged: scientific, synthetic, and initial mental models. The findings revealed that 7.20% of students fell into the initial mental model category, 53.90% exhibited synthetic mental models, and 38.90% demonstrated scientific mental models. Notably, incorporating AR into salt hydrolysis learning predominantly influenced the development of synthetic mental models. The study's results also indicated that the utilization of AR positively enhanced students' spatial abilities in understanding submicroscopic representations.
Student Perspectives on the Role of Artificial Intelligence in Education: A Survey-Based Analysis Ghazi Mauer Idroes; Teuku Rizky Noviandy; Aga Maulana; Irvanizam Irvanizam; Zulkarnain Jalil; Lensoni Lensoni; Andi Lala; Abdul Hawil Abas; Trina Ekawati Tallei; Rinaldi Idroes
Journal of Educational Management and Learning Vol. 1 No. 1 (2023): August 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v1i1.58

Abstract

Artificial intelligence (AI) has emerged as a powerful technology that has the potential to transform education. This study aims to comprehensively understand students' perspectives on using AI within educational settings to gain insights about the role of AI in education and investigate their perceptions regarding the advantages, challenges, and expectations associated with integrating AI into the learning process. We analyzed the student responses from a survey that targeted students from diverse academic backgrounds and educational levels. The results show that, in general, students have a positive perception of AI and believe AI is beneficial for education. However, they are still concerned about some of the drawbacks of using AI. Therefore, it is necessary to take steps to minimize the negative impact while continuing to take advantage of the advantages of AI in education.
Impact of Teacher Certification on Teacher Motivation and Performance in State Senior High Schools in Ternate City, Indonesia Muhammad Ridha Albaar; Acim Acim; Abubakar Abdullah
Journal of Educational Management and Learning Vol. 1 No. 1 (2023): August 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/jeml.v1i1.59

Abstract

This study aims to analyze the impact of the teacher certification on teacher’s motivation and performance in State High Schools in Ternate City, Indonesia. This study uses a quantitative approach with a survey method. The sample used in this study was 193 teachers who were selected by proportionate random sampling. The data was analyzed using path analysis supported by descriptive statistical analysis. The results indicate that the teacher certification has a direct effect on teacher's motivation and performance. Therefore, improving the application of teacher certification, and achievement motivation can improve performance.
Using the Flipped Classroom Model to Prevent Sexual Violence in Special Needs Children Mutiawati Mutiawati; Andy Syahputra; Nelly Nelly; Desita Ria Yusian; Soraya Lestari; 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 Fetty Nuritasari; Lailatul Qomariyah; Dayriqoh Agustin; Ismi Malika Mulkis; Moh Zayyadi
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 Teuku Rizky Noviandy; Ghalieb Mutig Idroes; Irsan Hardi; Talha Bin Emran; Zahriah Zahriah; Souvia Rahimah; Andi Lala; Rinaldi Idroes
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 Rinaldi Idroes; Muhammad Subianto; Zahriah Zahriah; Razief Perucha Fauzie Afidh; Irvanizam Irvanizam; Teuku Rizky Noviandy; Dimas Rendy Sugara; Waliam Mursyida; Teuku Zhilalmuhana; Ghalieb Mutig Idroes; Aga Maulana; 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 Aga Maulana; Ghazi Mauer Idroes; Pati Kemala; Nur Balqis Maulydia; Novi Reandy Sasmita; Trina Ekawati Tallei; Hizir Sofyan; Asep Rusyana
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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