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Digitalization of educational plays for quality education

10.11591/ijece.v14i5.pp5443-5457
Younes Alaoui Soulimani , Lotfi Elaachak , Mohammed Bouhorma
Repetitive tests on a learning material help schoolchildren to memorize and to learn this material. Psychologists call this phenomenon the testing effect. Skilled teachers use learning plays to embed routine tests in an engaging way. To widespread this practice, we propose a framework to digitize learning plays embedding routine tests into educational videogames. We have identified the smallest set of game design elements required to build an educational videogame out of a learning play. We have used the self-determination theory to group game design elements, and to define a breakdown structure for engagement engineering. This structure helps select the appropriate design elements for an engagement driver. We have applied the framework to digitize a learning play. We have tested the digital play with 238 schoolchildren who considered it as a video game. The video game tested a proposed pattern to create challenges allowing an engaging flow experience. The pattern increased responses (9%) and created time distortion (24%). Delivering rewards following variable schedules reduced errors (49%) and increased time distortion (16%). This research explores how to digitize learning plays into engaging educational video games and how to design engaging video games to remediate missed learning.
Volume: 14
Issue: 5
Page: 5443-5457
Publish at: 2024-10-01

Dynamic voltage restoration using neural networks for grid-connected wind turbine

10.11591/ijece.v14i5.pp5018-5029
Kaoutar Dahmane , Brahim Bouachrine , Belkasem Imodane , Abdellah El Idrissi , Mohamed Benydir , Mohamed Ajaamoum , M'hand Oubella
Wind energy is being integrated into the grid as a renewable energy source to meet the world's electricity needs. Grid-connected wind turbines are often disrupted by grid fault problems. Fault ride-through (FRT) ability has become the most important grid connection necessity for wind energy conversion systems (WECS). In the event of a voltage dip fault, the low voltage ride-through (LVRT) capacity is an imperative key to successful grid integration. This paper proposes a dynamic voltage restorer (DVR) controlled through an artificial neural network (ANN) to improve the LVRT capability of a grid-connected wind turbine (WT) based permanent magnet synchronous generator (PMSG). The DVR injects series voltage into the system through a series-connected transformer. The DVR can then restore the voltage to the pre-fault value. The injection transformer is connected to the line linking the PMSG-based wind turbine output to the utility grid. Design and simulation of the low voltage ride-through applied to symmetrical and asymmetrical fault conditions were performed in MATLAB/Simulink software. Simulation results approve that the performance of the technique fully demonstrates its effectiveness and practicality.
Volume: 14
Issue: 5
Page: 5018-5029
Publish at: 2024-10-01

Identification, validity, and reliability of the 21st-century workplace skills for on-the-job training practicum

10.11591/ijere.v13i5.28919
John Paul P. Miranda , Roque Francis B. Dianelo , Almer B. Gamboa , Joseph Alexander Bansil , Agnes R. Regala , Laharni S. Simpao
This study aimed to develop and evaluate an instrument for measuring 21st-century workplace skills in on-the-job training (OJT) trainees. A literature review identified 20 key skills, which became the foundation of the instrument. Its validity and reliability were tested through a pilot study involving 20 OJT supervisors, employing methods like the item-level content validity index (I-CVI) and Cronbach’s alpha. The results confirmed the instrument’s effectiveness, indicating the need for further research with larger and more diverse samples to enhance its applicability. This tool is crucial in improving OJT programs by enabling supervisors to assess and support trainees more effectively. Additionally, it serves as a resource for employers and educators in appraising potential hires and graduates, and assists institutions in refining their curricula.
Volume: 13
Issue: 5
Page: 3485-3492
Publish at: 2024-10-01

13-level modular multilevel inverter application for the exhaust fan drive control of Thu Thiem Road tunnel

10.11591/ijece.v14i5.pp5008-5017
An Thi Hoai Thu Anh , Tran Hung Cuong
The ventilation system plays a vital role in ensuring the safety of people and means of transportation. Fresh air is created in the tunnel mainly thanks to the exhaust fans arranged at the top of the tunnels. The drive motor for the exhaust fan in the Thu Thiem Road tunnel has a power of 560 kW and operates at a voltage of 6 kV. The paper proposes a 13-level modular multilevel inverter (MMC) with the improved nearest level modulation (NLM) method to ensure the quality of voltage output from the voltage source inverter-fed exhaust fan drive motor. This is a novel combination aimed at transforming electrical power at high voltage levels, high power, and enhancing operational efficiency and the lifespan of semiconductor components within the inverter when operating continuously and over extended durations. The theoretical research results verified through MATLAB/Simulink software with simulation parameters collected from the exhaust fan motor of Thu Thiem Road tunnel, Vietnam show total harmonic distortion of the current in operation with 13 levels is 1.23%, while that of the current in operation with 7 levels is 10.1%; total harmonic distortion (THD) of the voltage with 13 levels is 5.33%, while that of the voltage with 7 levels is 11.37%.
Volume: 14
Issue: 5
Page: 5008-5017
Publish at: 2024-10-01

Design of a high-speed 7.2 Gbps/lane receiver for MIPI D-PHY interface utilizing 18 nm FinFET technology

10.11591/ijece.v14i5.pp4956-4969
Trang Hoang , Anh Nam Ha
This study presents an advanced design for a high-speed receiver tailored for the MIPI D-PHY Interface, capable of handling data rates up to 7.2 Gbps per lane. The design is developed using 18 nm fin field-effect transistor (FinFET) technology and is rigorously simulated under varying process, voltage, and temperature conditions (PVTs) to ensure robustness. The architecture of the receiver integrates several key components: differential pair sensing, a folded cascode continuous time linear equalization (CTLE), a single-ended operational amplifier, and a cross-coupled stage. Operating at a supply voltage of 0.72 V in the worst-case scenario, our CTLE achieves a peaking gain of 17.77 dB at 4.26 GHz. The design demonstrates a maximum jitter of 19.63 ps at an offset voltage of ±2 mV. Notably, the power efficiency of our receiver is optimized to 0.85 mW/Gb/s, totaling 6.1 mW, with dual supply voltages of 1.98 and 0.88 V. This work contributes to the field by offering a highly efficient solution for fast data transmission with reduced power consumption and enhanced signal integrity.
Volume: 14
Issue: 5
Page: 4956-4969
Publish at: 2024-10-01

Educational management in improving the quality of teachers in senior high schools

10.11591/ijere.v13i5.29844
Muhammad Ihsan Dacholfany , Nyoto Suseno , Harlinda Syofyan , Muhammad Rijal Fadli
Teachers play a crucial role in improving the quality of education, an essential aspect of national education development, which applies universally in various cultures and countries. As national education standards and needs increase, educational development becomes a necessity. This study seeks to investigate the enhancement of teacher quality dimensions to elevate national education standards, employing a case study approach with mixed methods. The sampling involved random selection from five senior high schools in Lampung, comprising 190 students and 10 teachers. Data collection was carried out through questionnaires, interviews, and document analysis. The analysis of questionnaire data utilized structural equation modeling (SEM) through Lisrel 8.50 assistance. The qualitative phase encompassed data reduction, presentation, conclusion drawing, and verification. Results indicate that the model fits well with a Satorra-Bentler scaled Chi-square value of 189.190, a p-value coefficient of 0.070 (>0.05), root mean square error of approximation (RMSEA) of 0.030 (<0.080), and comparative fit index (CFI)≥0.90, suggesting its acceptability. The assessment of teachers’ professional management in enhancing education quality focuses on preparation, implementation, and evaluation, offering significant potential for improving senior high school teachers’ quality.
Volume: 13
Issue: 5
Page: 3100-3110
Publish at: 2024-10-01

Enhancing radar signal processing through LVQ-Kalman fusion: a tsunami prediction perspective

10.11591/ijeecs.v36.i1.pp282-289
Shobha Shobha , Nalini Narasimhaiah
In radar signal processing, the pursuit of precise prediction algorithms motivates the exploration of innovative methodologies. This study introduces a pioneering fusion of learning vector quantization (LVQ)- Kalman, merging LVQ with the advanced Kalman filter. The primary aim is to enhance adaptability and robustness, vital in weather monitoring and military surveillance. LVQ, known for its efficacy in pattern recognition and prediction, adjusts prototype vectors iteratively based on input data, ideal for radar signal intricacies. Various LVQ types are incorporated, tailored meticulously for specific radar applications. The Kalman filter, originally for aerospace, excels in tracking and predicting dynamic systems, seamlessly integrated to address uncertainties in radar data. By combining LVQ’s pattern recognition with the Kalman filter’s adaptability, the fusion aims to create a versatile system navigating radar data intricacies. Applications range from airborne target tracking to weather analysis and military surveillance. The integrated approach offers adaptability and robustness, vital for real-world implementations, particularly in tsunami detection. Future research may explore deep learning to further enhance adaptability. This fusion technique presents significant potential for advancing radar signal processing, promising accurate and adaptive systems, especially in critical applications like tsunami detection.
Volume: 36
Issue: 1
Page: 282-289
Publish at: 2024-10-01

Development of diagnostic competencies of educational psychologist in professional training

10.11591/ijere.v13i5.29522
Ryszhamal Aralbaeva , Khapiza Naubaeva , Nurlan Abishev , Zhumagul Taszhurekova , Zhadra Zhexembayeva
In the era of globalization, enhancing the development of diagnostic competencies among educational psychologists in Kazakhstan is a crucial aspect of improving higher education quality. The purpose of the study is to substantiate and implement the methodology for the development of diagnostic competencies of future educational psychologists in higher educational institutions of Kazakhstan as a guarantee of their readiness to carry out professional activities. The methodological approach involves developing methods for fostering diagnostic competencies, employing empirical techniques such as comparison, systematization, classification, and generalization of theoretical data, conducting surveys, testing, and modeling training methods for future educational psychologists. An experimental study conducted on the premises of Zhetysu University named after I. Zhansugurov has developed methodological tools to increase the readiness of future educational psychologists for the development of diagnostic competencies in professional training, namely: the implementation of readiness components with selected methods for their formation. After conducting an experimental study, promising areas for improving the methodology of readiness of future educational psychologists for the development of diagnostic competencies in Kazakhstan were established.
Volume: 13
Issue: 5
Page: 3393-3401
Publish at: 2024-10-01

A novel comprehensive investigation for enhancing cluster analysis accuracy through ensemble learning methods

10.11591/ijece.v14i5.pp5802-5812
H. N. Lakshmi , Thaduri Venkata Ramana , LNC Prakash K , L. Kiran Kumar Reddy , Kachapuram Basava Raju
Ensemble learning stands out as a widely embraced technique in machine learning. This research explores the application of ensemble learning, including ensemble clustering, to enhance the precision of cluster analysis for datasets with multiple attributes and unclear correlations. Employing a majority voting-based ensemble clustering approach, specific techniques such as k-means clustering, affinity propagation, mean shift, BIRCH clustering, and others are applied to defined datasets, leading to improved clustering results. The study involves a comprehensive comparative analysis, contrasting ensemble clustering outcomes with those of individual techniques. The process of improving cluster identification accuracy encompasses data collection, pre-processing to exclude irrelevant elements, and the application of standard clustering algorithms. The task includes defining the optimal number of groups before comparing clustering models. Additionally, a combined model is constructed by merging BIRCH clustering and mean shift clustering, leveraging their advantages to enhance overall clustering strength and accuracy. This research contributes to advancing ensemble learning and ensemble clustering methodologies, offering improved accuracy, and uncovering hidden patterns in complex datasets.
Volume: 14
Issue: 5
Page: 5802-5812
Publish at: 2024-10-01

Biometric classification system for dorsal finger creases utilizing multi-block circular shift combination local binary pattern

10.11591/ijece.v14i5.pp5234-5243
Imran Riaz , Ahmad Nazri Ali , Haidi Ibrahim , Ilyas Ahmad Huqqani
In recent years, there has been a growing interest in biometric recognition based on finger dorsal patterns, making it a significant area of research. This paper introduces a biometric classification system that utilizes dorsal finger middle creases. The viability of this trait is assessed through the implementation of a new method known as multi block circular shift combination local binary pattern (MBCSC-LBP). The MBCSC-LBP approach involves dividing the image into multiple blocks to enhance robustness and capture both local and global information, thereby extracting discriminative features. These features from each block are then concatenated to form a comprehensive feature vector. To evaluate the accuracy of the proposed MBCSC-LBP feature extractor, a support vector machine (SVM) with a linear kernel is utilized. The classification accuracy achieved by this method is 96.22% indicating a promising performance.
Volume: 14
Issue: 5
Page: 5234-5243
Publish at: 2024-10-01

Evaluating integrated training for juvenile criminal justice system at the national police education and training center

10.11591/ijere.v13i5.28604
Irfan Abraham , Ahmad Ridwan , Dinny Devy Triana
This study critically assesses the integrated technical training program for the juvenile criminal justice system, conducted by the national police education and training institute in Indonesia. The research employs a mixed-method approach, utilizing an explanatory sequential design and applying the Kirkpatrick evaluation model, involving 62 participants. Content validity is maintained through expert input, with the Aiken v formula employed for result analysis. Credibility is affirmed through a focus group discussion. The study aims to evaluate the program’s impact on the rights of Indonesian children in legal conflicts. Findings indicate participants’ satisfaction and high motivation at level 1 (reaction), while levels 2, 3, and 4 reveal a positive impact on protecting the rights of children involved in legal conflicts. The research highlights the need for a larger sample size and a more extended measurement period for future investigations. Objectives encompass assessing program impact, learning outcomes, behavioral changes, external organizational impact, and examining the program’s practical contribution to educational research and evaluation, guiding recommendations for future improvements.
Volume: 13
Issue: 5
Page: 3056-3067
Publish at: 2024-10-01

Towards global citizenship–role of cross border higher education across the ASEAN region

10.11591/ijere.v13i5.29441
Bilal Khalid , Joanna Kurowska-Pysz
The Association of Southeast Asian Nations (ASEAN) region has been experiencing a vibrant economy with heavy investment in higher education as an economic development driver. Being home to over 630 million people and more than 7,000 higher education institutions (HEIs) and over 12 million students, it was critical to evaluate the role of cross-border higher education in the region. This study aimed to investigate the factors and motivations contributing to the success and efficacy of cross-border higher education in the ASEAN region. Additionally, it sought to examine the impact of cross-border education on the development of global citizenship. A quantitative study was conducted using secondary data from the repositories of the World Bank and UNESCO Institute for Statistics (UIS). Data was analyzed using descriptive visualizations and regression analysis. Results indicated variations in HEIs investments, with Brunei having the least. Many students moved from the ASEAN region to seek higher education in other regions, with Vietnam having the highest number of 137,022 students. The majority of these ASEAN countries have more than 10,000 higher education students' abroad. The United States, Australia, and Japan were the significant destinies of students from the ASEAN region. Government expenditure on tertiary education, gross domestic product (GDP), tertiary school enrolment, and GDP growth rate were found to have a significant influence on cross-border higher education mobility. Policy recommendations were the development of international collaborations, cross-border partnerships, and cross-national harmonization to enhance the partnership and mobility of higher education students in the ASEAN region.
Volume: 13
Issue: 5
Page: 3293-3305
Publish at: 2024-10-01

A review of machine vision pose measurement

10.11591/ijeecs.v36.i1.pp450-460
Wang Xiaoxiao , Ng Seng Beng , Rahmita Wirza O. K. Rahmat , Puteri Suhaiza Sulaima
This review paper provides a comprehensive overview of machine vision pose measurement algorithms. The paper focuses on the state-of-the-art algorithms and their applications. The paper is structured as follows: the introduction in provides a brief overview of the field of machine vision pose measurement. Describes the commonly used algorithms for machine vision pose measurement. Discusses the factors that affect the accuracy and reliability of machine vision pose measurement algorithms. Summarizes the paper and provides future research directions. The paper highlights the need for more robust and accurate algorithms that can handle varying lighting conditions and occlusion. It also suggests that the integration of machine learning techniques may improve the performance of machine vision pose measurement algorithms. Overall, this review paper provides a comprehensive overview of machine vision pose measurement algorithms, their applications, and the factors that affect their accuracy and reliability. It provides a valuable resource for researchers and practitioners working in the field of computer vision.
Volume: 36
Issue: 1
Page: 450-460
Publish at: 2024-10-01

A framework for 3D radiotherapy dose prediction using the deep learning approach

10.11591/ijece.v14i5.pp5524-5533
Lam Thanh Hien , Ha Manh Toan , Do Nang Toan
Cancer is known as a dangerous disease to humans with a very high death rate. There are a lot of cancer treatment methods that have been studied and applied in the world. One of the main methods is using radiation beams to kill cancer cells. This method, also known as radiotherapy, requires experts having a high level of skill and experience. Our work focuses on the 3D dose prediction problem in radiotherapy by proposing a framework aiming to create a medical intelligent system for this problem. To do that, we created a convolutional neural network based on ResNet and U-Net to generate the predicted radiation dose. To improve the quality of the training phase, we also applied some data processing techniques based on the characteristics of the 3D computed tomography (CT) data. The experiment used the dataset from patients who were cancer-treated with radiotherapy in the OpenKBP competition. The results achieved good evaluating metrics, the first is by the Dose-score and the second is by the dose-volume histogram (DVH) score. From the training result, we built the medical system supporting 3D dose prediction and visualizing the result as slices in heatmap form.
Volume: 14
Issue: 5
Page: 5524-5533
Publish at: 2024-10-01

First-year students’ mathematical skills: assessing and developing an innovative strategy for teaching mathematics

10.11591/ijere.v13i5.29182
Rogie Espia Padernal , Sylvino Venus Tupas
In order to succeed in advanced mathematics and other related courses while in college, students must have a strong foundation in the subject of mathematics. Thus, this study aimed to determine the level of students’ fundamental skills in mathematics in the areas of number sense, mathematical representation, spatial skills, estimation, patterns, and problem-solving when they are grouped according to the school of origin, strand, and program enrolled. Likewise, to test if there is a significant difference in fundamental mathematics skills when grouped according to the demographics. A descriptive comparative research design was utilized, and 278 first-year students participated. The results revealed that students performed better on their fundamental skills in mathematics in the area of patterns and performed lowest mean in spatial skills. Students who graduated from public schools performed well in all areas compared to private school students; in terms of program enrolled, fundamental skills in math varies. Furthermore, there is a significant difference in students’ fundamental skills in mathematics when grouped according to the school of origin and strand, otherwise in program enrolled. This study suggests that mathematics teachers must utilize a different teaching strategy suited to students’ needs following the learner-centered approach in teaching.
Volume: 13
Issue: 5
Page: 2890-2898
Publish at: 2024-10-01
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