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Effectiveness of digital learning on students’ higher order thinking skills

10.11591/ijere.v13i5.29449
Hamidah Mat , Siti Salina Mustakim , Fazilah Razali , Norliza Ghazali , Asnul Dahar Minghat
In the realm of education, learning and instructional activities play a crucial role in cultivating lasting and meaningful comprehension among science students. This research aims to evaluate the effectiveness of the i-Genius module in enhancing students’ performance in science. The i-Genius module’s development adhered to the ADDIE model, and two specific research questions were formulated: i) is there a statistically significant difference in mean scores between the experimental and control groups? and ii) to what extent can i-Genius contribute to students’ conceptual evolution compared to traditional methods? To address these questions, a sequential mixed-method approach involving interviews, pre-tests, and post-tests was implemented in two distinct schools in the Seremban District. The experimental group comprised 35 participants, and the control group also included 35 students with similar characteristics. Student performance, assessed through pre-test and post-test mean scores, revealed that students exposed to i-Genius achieved significantly higher scores than those exposed to traditional methods in the post-test (t(68)=8.37, p<0.05). This study’s implications lie in its practical application within the school context, offering an alternative instructional tool for teaching science and presenting an instructional model to guide teachers in formulating strategies that encourage problem-posing within the science curriculum.
Volume: 13
Issue: 5
Page: 2817-2824
Publish at: 2024-10-01

Chinese business English undergraduates’ speaking proficiency: a developed-module effect

10.11591/ijere.v13i5.29624
Ke Hu , Asmaa AlSaqqaf
The rapid growth of English in the world has resulted in its vital role in diverse domains. However, the outcomes of learners' speaking skills remain controversial. The objective of the study was to investigate the effect of the developed teaching speaking module on the speaking proficiency of 96 Chinese English as a foreign language (EFL) business English undergraduates at the target applied university in China. This module integrates content-based instruction (CBI) and a three-stage skill development approach, namely awareness-raising, appropriation, and autonomy (3As approach). A quantitative study with a pretest and posttest quasi-experiment design was carried out at an applied university within an interventional period of 10 weeks, employing pretests and posttests as data collection tools. The quantitative data were analyzed using the SPSS 29 version suggesting that this teaching-speaking module yields significantly improved speaking proficiency among Chinese EFL undergraduates in business English than the traditional classroom has done. These findings shed a favorable light on the development of curriculum and instruction for teaching English for specific purposes (ESP) and the education of English majors in China.
Volume: 13
Issue: 5
Page: 3579-3586
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

System availability assessment and optimization of a series-parallel system using a genetic algorithm

10.11591/ijeecs.v36.i1.pp153-162
Priya Chaudhary , Shikha Bansal
To optimize the operational availability of the series-parallel system and provide useful insights for maintenance planning, the study attempts to investigate the availability of a ball mill unit. These four different components make up the ball mill production system: “drum,” “ring-gear,” “gearbox,” and “electric motor.” There is a chain mechanism connecting all four components. The “ring gear” and “electric motor” components are composed of two independent units, one of which serves the desired purpose and the other is maintained in cold standby. The “drum” and “gearbox” of the components each contain only one unit. Therefore, a novel mathematical model is designed and implemented in this work by assuming arbitrary repair rates and exponentially distributed failure rates using the Markov process and Chapman-Kolmogorov equations. This study explored the availability with a normalization method and used genetic algorithm techniques to optimize ball mill availability. Putting this article into practice is of great benefit when developing an appropriate maintenance program. Through this, the study achieves maximum production. To investigate the behavior of several performance characteristics of the ball mill production system, numerical results and corresponding graphs are also specifically created for specific values of subsystem parameters, such as failure rate, and repair rate to increase the system’s overall efficiency.
Volume: 36
Issue: 1
Page: 153-162
Publish at: 2024-10-01

Geographic information system-based spatio-temporal detection and mapping of COVID–19 hot/cold spots in Oman

10.11591/ijece.v14i5.pp5779-5801
Yaseen Al-Mulla , Mohammed Al–Muqaimi , Ahsan Ali , Taif Al-Badi , Krishna Parimi , Anusha Chowdary
Infected COVID-19 patients, especially after March 11, 2020, grew drastically in Oman. Hence, a variety of measures were issued to restrict all social gatherings, commercial activities, and mandating preventative health practices. This study aimed to i) understand distribution patterns and impact of decisions and responses at the spread of confirmed cases; ii) highlight and verify most concentrated regions with infections; and iii) overview spatial changes of cases overtime. The analysis was carried out using inverse–distance-weighted interpolation and hotspot (Getis–Ord GI*) techniques. Results showed a substantial relationship between spatial structure of COVID–19 and population distribution and density. COVID–19 has increased by 11.5% weekly in the capital, which were locked down since April 2020. However, after health quarantine was lifted on May 29, 2020, weekly cases surged in the capital. Al-Batinah-North and Dhofar recorded an increase of 32.1% and 30.5%, respectively, after restrictions had eased. The analysis illustrated that spread of COVID–19 was shifting from Northeast to Southeast and later shifted back to the Northeast of the country at the end of year 2022. This study is beneficial for pertinent organizations to perform detailed studies for developing and monitoring disease systems and dominating relevant factors.
Volume: 14
Issue: 5
Page: 5779-5801
Publish at: 2024-10-01

Enhanced accuracy estimation model energy import in on-grid rooftop solar photovoltaic

10.11591/ijece.v14i5.pp5970-5983
Alfin Sahrin , Imam Abadi , Ali Musyafa
Installing rooftop solar photovoltaic (PV) with an on-grid system benefits consumers because it can reduce imports of electrical energy from the grid. This study aims to model the estimation of energy imports generated from on-grid rooftop solar PV systems. This estimation model was carried out in 20 provincial capitals in Indonesia. The parameters used are weather conditions, orientation angle, and energy generated from the on-grid rooftop solar PV system. The value of imported energy is divided into three combinations based on the azimuth angle direction, which describes the type and shape of the roof of the building (one-direction, two-directions, and three-directions). Modeling was done using machine learning with neural network (NN), linear regression, and support vector machine. A comparison of the machine learning algorithm results is NN produces the smallest root mean square error (RMSE) value of the three. Model enhancement uses a grid search cross-validation (GSCV) to become the GSCV-NN model. The RMSE results were enhanced from 53.184 to 44.389 in the one-direction combination, 145.562 to 141.286 in the two-direction combination, and 81.442 to 76.313 in the three-direction combination. The imported energy estimation model on the on-grid rooftop solar PV system with GSCV-NN produces a more optimal and accurate model.
Volume: 14
Issue: 5
Page: 5970-5983
Publish at: 2024-10-01

Artificial neural network-based intelligent sensor-based electronic nose for food applications

10.11591/ijeecs.v36.i1.pp163-173
Manjunath Managuli , Kalimuthu Bagyalakshmi , Francis Rosy Shiny Malar , Jebaraj Jency Rubia , Samat Iderus
Food commerce, especially for the general public, is greatly impacted by the capacity to identify and recognize chemical samples for food applications. Every chemical sample response has a unique, distinguishing smell. These advancements highlight the method of an artificial neural networks (ANN) to distinguish the distinctive fragrance from the reaction of substances. The categorization of various smell patterns has diminished confidence in ANN technology. Using an ANN technique and a sensor-based e-nose system for food applications, each chemical’s identification has been done commercially. The system comprises a 5-gas sensor selection that recognizes chemical talk while allowing for an improvement in permitting while falling gas is planned outside. To build a model of a different signal reaction, individual sensors are equally collected and merged into the innovation -favored sensor array. Demonstrates how it is related to the chemical test. The e-nose categorization has been tested with five different chemical samples and five different sensor classes. The e-nose approach, which comprises five sensors, can classify each chemical reaction model, starting with the results. With more sensors being employed, the classification accuracy of the precise chemical reaction improves. These data demonstrate that the ANN-based e-nose method promises a successful classification system for chemical sample responses for a characteristic odor sample.
Volume: 36
Issue: 1
Page: 163-173
Publish at: 2024-10-01

Enhanced fault identification in grid-connected microgrid with SVM-based control algorithm

10.11591/ijeecs.v36.i1.pp115-126
Divya Shoba Nair , Thankappan Nair Rajeev , Sindhura Miraj
The penetration of renewable energy sources, electric vehicles (EVs) and load dynamics, and network complexities often lead to nuisance tripping in grid-connected microgrids. Traditional protection methods fail to discriminate fault and other dynamic volatilities in the system. The paper presents a novel two-level adaptive relay algorithm to avoid nuisance tripping in a grid-connected microgrid under varying grid dynamics. The novelty of the adaptive relay algorithm is that nuisance tripping is eliminated by precisely determining normal system-level dynamics at the first level using a phase deviation reference block. The first level determines the necessity for activating the second level, which consists of a detection scheme combining a multiclass support vector machine (SVM) and discrete wavelet transform (DWT). The hybrid DWT-SVM methodology ensures effective fault diagnosis, adapting to variations in energy sources, load fluctuations, and fault scenarios. Real-time hardware-in-the-loop (HIL) simulation validates the system’s effectiveness in dynamic microgrid environments. Extensive experiments on scenarios, including faults, fluctuations in renewable energy generation, and intermittent simulations of EV charging and capacitor switching, were conducted to test the efficacy of the adaptive relay algorithm. Finally, experiments using OPAL-RT HIL real-time simulator and the Raspberry Pi microcontroller validated the adaptive relay algorithm in a grid-connected microgrid under varying grid dynamics.
Volume: 36
Issue: 1
Page: 115-126
Publish at: 2024-10-01

Digital transformation technologies for conveyor belts predictive maintenance: a review

10.11591/ijeecs.v36.i1.pp639-646
Pediredla Veni Santoshi Anusha , Swapna Peravali , Dodda Venkata Rama Koti Reddy
The availability of condition-monitoring data has increased due to internet of things (IoT) technologies, providing information on various parameters like vibration, temperature, current, and voltage. Cloud computing and big data facilitate the prevention of failures and estimation of remaining useful life through advanced mathematical models and artificial intelligence (AI) techniques. These enable prompt and suitable maintenance actions. This article conducts a systematic review of digital transformation technologies, including cloud computing, edge computing, AI, machine learning (ML), and TinyML, in predictive maintenance for conveyor belt systems. This article reviews how these digital transformation technologies improve predictive maintenance strategies for conveyor belts. The systematic review summarizes the results and challenges of various methodologies used in conveyor belt systems and suggests areas for further research. This paper aimed to serve as a useful resource for researchers, practitioners, and industry professionals seeking insights into current predictive maintenance technologies for conveyor belt systems. The takeaways of the review are expected to ignite discussion on efficient and proactive maintenance strategies and promote the development of innovative solutions for ensuring the reliability and longevity of conveyor belt systems in the digital era.
Volume: 36
Issue: 1
Page: 639-646
Publish at: 2024-10-01

Identification of faults in rotating machines using high precision FBG vibration sensor: a case study on PM schemes

10.11591/ijeecs.v36.i1.pp535-547
Dipak Ranjan Nayak , Nilam N. Ghuge , Ambarish G. Mohapatra , Pramod Sharma , Narayan Nayak , Satyapriya Satapathy , Ashish Khanna
Predictive maintenance (PM) is a data-driven approach to performing proactive maintenance by analyzing the condition of the equipment in any industrial setting. The high-precision sensors are widely adapted to meticulously analyze critical maintenance conditions using such a data-driven approach. In a similar context, a fiber brag grating (FBG) sensor is a passive and high-precision sensor that is widely used in industries where conventional sensors are not preferred. Broadly, this article presents four sub elements of the proposed integrated system such as the design of the sensor element, signal processing scheme (SPS), machine learning (ML) model for predicting anomalies, and decision support system (DSS) to suggest maintenance actions. Also, this article highlights an experimental case study on vibration monitoring and analysis of real-time signals for making proactive maintenance decisions. An FBG vibration sensor of center wavelength 1,550 nm is designed and utilized to acquire real-time vibration signatures of a rotating machine under test. A piezoelectric vibration sensor is used with the FBG sensor to compare the vibration response obtained during the test. Pre-processing of raw signals is performed using a moving average filter (MAV) followed by a low pass filter to nullify the effect of noise. To obtain proactive maintenance decisions, a DSS model is prepared by considering the processed vibration signatures. Various maintenance conditions are tested during the experimental analysis and detailed results analysis are presented.
Volume: 36
Issue: 1
Page: 535-547
Publish at: 2024-10-01

Phase structures-based hybrid approaches for defect detection in vials

10.11591/ijece.v14i5.pp5185-5199
C. R. Vishwanatha , V. Asha
Quality control and assurance in pharmaceutical vial manufacturing are paramount to ensure drug safety and efficacy. Defects such as cracks, bubbles, black spots, and wrinkles can compromise product quality and patient safety. This study proposes a novel methodology that integrates fast non-local means (FNLM) filtering with hybrid image processing techniques to detect these defects. Previous approaches have often struggled with subtle anomalies in texture and surface features. The proposed solution leverages phase structure analysis, utilizing phase stretch transform (PST) to effectively highlight subtle anomalies by extracting features sensitive to phase variations. These features are further refined using Gaussian filtering, with Otsu thresholding applied for precise segmentation and defect boundary identification. Morphological dilation enhances detection speed and accuracy, while region of interest (ROI) identification aids in localizing defects and facilitating decision-making. The system demonstrates significant improvements in quality control, achieving high performance metrics: precision (98.85%), recall (98.57%), accuracy (98.36%), specificity (98.0%), and F1-score (98.71%). It also achieves impressive AUC-ROC (98.18%) and AUC-PR (99.08%) values, demonstrating its robustness and suitability for defect detection in pharmaceutical vials.
Volume: 14
Issue: 5
Page: 5185-5199
Publish at: 2024-10-01

Predicting television programs success using machine learning techniques

10.11591/ijece.v14i5.pp5502-5512
Khalid El Fayq , Said Tkatek , Lahcen Idouglid
In the ever-evolving media landscape, television (TV) remains a coveted platform, compelling industry players to innovate amid intense competition. This study focuses on leveraging machine learning regression models to precisely predict TV program reach. Our objective is to assess the models' efficacy, revealing a standout performer with a mean absolute percent error of just under 8%. Significantly, we identify features exerting a substantial impact on predictions and explore the potential for model enhancement through expanded datasets. This research extends beyond statistical insights, offering actionable implications for TV channel managers. Empowered by these findings, managers can make informed decisions in program planning and scheduling, optimizing viewer engagement. The temporal analysis of evolving trends over time adds a nuanced layer to our study, aligning it with the dynamic nature of the media landscape. As television retains its dynamic force, our insights contribute not only to academic discourse but also provide practical guidance, enhancing the competitive edge of television channels.
Volume: 14
Issue: 5
Page: 5502-5512
Publish at: 2024-10-01

The acceptance model for camera simulators as a learning media for Indonesian vocational student

10.11591/ijere.v13i5.29011
Novian Anggis Suwastika , Maslin Masrom , Qori Qonita , Rahmat Yasirandi , Hilal Hudan Nuha
This study investigates the acceptance of camera simulator technology as a learning media by Indonesian vocational high school (VHS) students and examines the relationships among influencing factors. It proposes an acceptance model integrating the technology acceptance model (TAM) 3 and the unified theory of acceptance and use of technology (UTAUT). Ten factors impacting technology acceptance were identified, resulting in the formulation of 15 hypotheses regarding inter-construct relationships. In this empirical study, a quantitative approach was employed, distributing questionnaires to 200 students at Public Vocational High School 10 in Bandung, specializing in broadcasting and filmmaking programs. After analyzing 145 valid responses, the study progressed in two stages: the measurement model and the structural model. The evaluation of the measurement model confirmed the validity of all indicators and constructs, ensuring compliance with the established standards. In the structural model evaluation, one construct (computer anxiety) and four inter-construct relationships were excluded. This research enhances our understanding of factors influencing camera simulator technology acceptance among VHS students in Indonesia, shedding light on the complexities of their decision-making process in adopting this educational tool.
Volume: 13
Issue: 5
Page: 2996-3007
Publish at: 2024-10-01

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

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
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