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Impact of hybrid education in higher education: a systematic review

10.11591/ijere.v14i6.29524
Victor Hugo Herencia-Escalante , William Jesús Cardenas-Zedano , Jimena Angelica Etchart-Puza , Sergio Arturo Rojas Chacaltana
In recent times, educational initiatives such as hybrid education have positioned themselves as important approaches to ensure the continuity of education during a period as complicated as the COVID-19 pandemic. In this context, the objective of this article is to explore the rise and development of hybrid education worldwide in recent years as a viable alternative within higher education institutions, through a systematic review of the literature applying the preferred reporting items for systematic reviews and meta-analyses (PRISMA) method. From this review, it is observed that hybrid education has experienced significant progress during the COVID-19 pandemic, given the transition to virtuality that was experienced and the rise of new digital technologies that prove useful for this approach. At the same time, the interest shown by both students and teachers in adopting this new approach instead of a purely face-to-face or virtual one has become evident, although there are still several challenges to overcome before it can be properly implemented.
Volume: 14
Issue: 6
Page: 4353-4363
Publish at: 2025-12-01

Readiness and motivation in digital civic engagement among tertiary students

10.11591/ijere.v14i6.35395
Mary Ann C. Abril , Johnrey N. Manzanal , Glenda M. Dimaano , Francisco V. Aguirre , Melvin V. Babol
This study addresses the challenge of enhancing civic engagement in higher education institutions by examining the predictive relationships between readiness, motivations, and digital civic engagement of tertiary students. Employing the quantitative, correlational research approach, data collected through survey questionnaires were analyzed using Minitab statistical software. Significant insights came from the 2,205 tertiary students selected through multistage random sampling, following strictly ethical standards to ensure data privacy and anonymity. As found, a moderate level of readiness underscores the need for targeted interventions, particularly to improve the civic behavior of students. Understanding and values motives emerged as dominant drivers of engagement, indicating the strong desire of students for personal growth and learning. While respondents occasionally exhibit digital engagement, it leans toward civic activities over political involvement, focusing primarily on information consumption rather than active collaboration. Emerging as a significant predictor of engagement, developing tailored initiatives to enhance readiness is crucial to positively influencing civic participation. Higher education institutions may use these findings to develop digital interventions that serve as catalysts for long-term civic engagement among students and engage in further research on other predictive factors.
Volume: 14
Issue: 6
Page: 4812-4820
Publish at: 2025-12-01

The impact of work concerns on teaching effectiveness: evidence from Chinese private universities

10.11591/ijere.v14i6.35367
Liang Mingyu , Mohd Khairuddin Abdullah , Connie Shin
Understanding how young teachers cope with work concerns is crucial for improving teaching quality in Chinese private higher education. This study investigates the relationship between different stages of such concerns and teacher effectiveness of young lecturers in private universities. These lecturers often face workload pressure andlack of career supports, which may influence their effectiveness and professional development. This research involved 416 full-time lecturers under the age of 40 from Shandong Province. The sample was determined using Krejcie and Morgan’s formula and selected through a multi-stage sampling method. Private universities were stratified into four categories, one university from each category was purposively selected, and participants were randomly sampled. Data were gatheredthrough a structured questionnaire adapted from the stages of concern (SoC) and the school teacher effectiveness questionnaire (STEQ). Pearson correlation, multiple regression, and structural equation modeling (SEM) were conducted for analysis. The results show that task concerns and impact concerns significantly influenced teacher effectiveness across instructional planning and strategies, assessment, and learning environment. In contrast, self-concerns showed weaker influence. These findings suggest that work concerns reflect not only stress but also deeper professional motivation, pointing to the need for more purposeful supports to increase teacher effectiveness and career growth.
Volume: 14
Issue: 6
Page: 4604-4613
Publish at: 2025-12-01

University students’ perceptions on developing constructivist learning approach in classroom settings

10.11591/ijere.v14i6.35117
Cuc Thi Doan , Tuan Van Vu , Ai Nhan Nguyen
This study investigated tertiary students’ perceptions regarding constructivist learning in the context of higher education in Vietnam. It aimed to examine the general perceptions of university students towards constructivist learning and the effects of constructivist learning on students’ learning outcomes. It also examined the conditions that make students more likely to embrace or resist these approaches. The study evaluated the engagement of students in problem-solving activities through the use of constructivist learning methods. A mixed-methods approach was employed, combining both quantitative and qualitative data. Specifically, the study involved a survey of 384 students from Hanoi Law University, using a researcher-made Likert-scale questionnaire and semi-structured interviews of 20 students from the sample. While descriptive and inferential statistics were used to analyze the quantitative data, the qualitative data were thematically analyzed for common themes and patterns. The results indicate that although the participants acknowledge the benefits of constructivist methods, particularly in fostering critical thinking and problem-solving, there is still uncertainty about their ability to engage in a self-directed learning approach. The findings suggest that while the constructivist approach has been recognized, practical efforts have not been made in teaching practices, teacher training, and assessment methods to create an interactive, student-centered learning environment in Vietnam.
Volume: 14
Issue: 6
Page: 4264-4275
Publish at: 2025-12-01

New qualitative perspective in students’ English presentation skills in China-developing a student-based module

10.11591/ijere.v14i6.33708
Lifang Sun , Hanita Hanim Ismail , Azlina Abdul Aziz
Since English is the world’s lingua franca, English learners need to master communication skills to succeed in their respective fields. However, Chinese college students face the problem of separation between learning and using what they learned in the traditional English classrooms. This study aims to explore the university students’ needs of English presentation learning. The research questions are: i) What are the students’ language needs to improve an English presentation? ii) What are the skills needed when doing an English presentation? and iii) What are the students’ preferences in English presentation class? The researchers conducted focus-group interviews (FGI) which were participated by 30 students and semi-structured interview for five teachers to understand the students’ real needs and preferences in the process of learning English speaking. Three themes were generated by axial coding from the interview data: i) English language needs; ii) presentation skills’ needs; and iii) students’ preferences.The findings can help the teacher design the English-speaking class more effective and have adjustments according to students’ real productions using production-oriented approach in English presentation teaching.
Volume: 14
Issue: 6
Page: 5174-5186
Publish at: 2025-12-01

Enhancing character strengths and resilience in primary education: an online Quranic stories-based program

10.11591/ijere.v14i6.34091
Samir Ahmed Zekary , Gomaa Zakaria Saleh , Ashraf Ragab Ibrahim , Elsayed Atef El-Hashimi , Mustafa Mohamed Yussuf , Mohamed Ali Nemt-allah
This study investigated the effectiveness of an online Quranic stories-based program in enhancing character strengths and resilience among primary school students. Using a randomized controlled design, 64 students (aged 11-13 years) from Housh Eissa School in Egypt were assigned to experimental (n=33) and control (n=31) groups. The intervention involved ten online sessions on five major Quranic civilizations, measuring character strengths and resilience using the validated 23-item character strengths test and ego-resiliency scale. Data were collected at three time points: pre-intervention, post-intervention, and two-month follow-up. The results demonstrated significant improvements in the experimental group across all 23-character strength dimensions (p<.01, partial η² ranging from .116 to .529) and resilience (F=34.245, partial η²=.529). Notable enhancements were observed in judgment (F=11.775, partial η²=.279), self-control (F=10.269, partial η²=.252), and beauty appreciation (F=10.824, partial η²=.262). These improvements were maintained during the follow-up period, with the experimental group consistently outperforming the control group. The study suggests that online Quranic stories-based interventions can effectively enhance character strengths and resilience in primary school students, demonstrating a promising approach for character education.
Volume: 14
Issue: 6
Page: 4992-5002
Publish at: 2025-12-01

Optimized passive and active shielding of magnetic induction generated by ultra-high-voltage overhead power lines

10.11591/ijece.v15i6.pp5144-5161
Salah-Eddine Houicher , Rabah Djekidel , Sid Ahmed Bessidek
This paper presents computational modeling to assess and limit the magnetic induction levels emitted by an extra-high-voltage (EHV) overhead transmission line of 750 kV using the fundamental principle of Biot-Savart law in magnetostatics. An optimization technique based on the grey wolf optimizer (GWO) algorithm is employed to determine the appropriate location of the passive and active loop conductors, and the associated parameters to shielding to achieve better compensation of magnetic induction in an interest zone. The resulting magnetic induction of the ultra high voltage (UHV) overhead power line exhibits a crest value of 27.78 μT at the middle of the right-of-way, which can be considered unacceptable by strict protection standards. Generally, the magnetic compensation loops optimally located under the phase conductors of the power transmission system reduce the magnetic induction levels along the transmission line corridor. The passive loop attenuates the maximum magnetic induction by a rate of 29.7%. Therefore, the performance of the active loop is better; it provides a greater reduction with a rate reaching 53.24%. The simulation results were tested with those derived by the elliptical polarization process. An excellent concordance was found, which made it possible to ensure the adopted method.
Volume: 15
Issue: 6
Page: 5144-5161
Publish at: 2025-12-01

Leveraging IoT with LoRa and AI for predictive healthcare analytics

10.11591/ijict.v14i3.pp1156-1162
Pillalamarri Lavanya , Selvakumar Venkatachalam , Immareddy Venkata Subba Reddy
Progress in mobile technology, the internet, cloud computing, digital platforms, and social media has substantially facilitated interpersonal connections following the COVID-19 pandemic. As individuals increasingly prioritise health, there is an escalating desire for novel methods to assess health and well-being. This study presents an internet of things (IoT)-based system for remote monitoring utilizing a long range (LoRa), a low-cost and LoRa wireless network for the early identification of health issues in home healthcare environments. The project has three primary components: transmitter, receiver, and alarm systems. The transmission segment captures data via sensors and transmits it to the reception segment, which then uploads it to the cloud. Additionally, machine learning (ML) methods, including convolutional neural networks (CNN), artificial neural networks (ANN), Naïve Bayes (NB), and long short-term memory (LSTM), were utilized on the acquired data to forecast heart rate, blood oxygen levels, body temperature patterns. The forecasting models are trained and evaluated using data from various health parameters from five diverse persons to ascertain the architecture that exhibits optimal performance in modeling and predicting dynamics of different medical parameters. The models' accuracy was assessed using mean absolute error (MAE) and root mean square error (RMSE) measures. Although the models performed similarly, the ANN model outperformed them in all conditions.
Volume: 14
Issue: 3
Page: 1156-1162
Publish at: 2025-12-01

Modeling chemical kinetics of geopolymers using physics informed neural network

10.11591/ijict.v14i3.pp822-829
Blesso Abraham , Thirumalaivasal Devanathan Sudhakar
Using a physics informed neural network for the analysis of geopolymers as an alternate material for cement can be a viable approach, as neural networks are capable of modeling complex, nonlinear relationships in data, which can be beneficial for representing the dynamics of chemical properties. If you have a substantial amount of theoretical data, a neural network can learn patterns and relationships in the data, even when the underlying system dynamics are not well-defined or are difficult to model analytically. A welltrained neural network can generalize from the training data to make predictions for unseen scenarios, which can be useful for real-time analysis of the material.
Volume: 14
Issue: 3
Page: 822-829
Publish at: 2025-12-01

Digital literacy and cybersecurity in higher education: the unseen power of academic librarians

10.11591/ijere.v14i6.34916
Mohammad Fazli Baharuddin , Abdurrahman Jalil , Zahari Mohd Amin , Fadhilnor Rahmad , Shamila Mohamed Shuhidan
The increasing reliance on digital technologies in higher education has amplified the need for students to develop digital literacy and cybersecurity awareness. However, many undergraduate students lack the competencies required for responsible and secure digital engagement, posing significant risks in the digital landscape. Academic librarians, as key facilitators of information literacy, are uniquely positioned to address these challenges, yet their roles in promoting digital literacy and cybersecurity awareness remain underexplored. The study addresses the following key issues: how do academic librarians play their roles on undergraduate students’ digital literacy and cyber security awareness; what are the challenges related to library initiatives; and, perhaps most importantly, what are the strategies do librarians employ to improve it? Using a qualitative research methodology, data were collected through interviews with six academic librarians and analyzed using thematic analysis. The findings reveal that academic librarians play critical roles in fostering digital literacy and cybersecurity by teaching information literacy, promoting ethical online behavior, and enhancing students’ digital safety practices. Challenges identified include limited resources, diverse digital skill levels among students, and difficulties in maintaining student engagement. Librarians address these issues through strategies such as faculty collaboration, integrating digital literacy programs, employing interactive learning tools, and pursuing continuous professional development. This research offers actionable insights for integrating digital literacy and cybersecurity initiatives into library services, improving librarian training, and enhancing the sustainability and visibility of academic libraries within higher education institutions.
Volume: 14
Issue: 6
Page: 4404-4417
Publish at: 2025-12-01

Stability analysis and robust control of cyber-physical systems: integrating Jacobian linearization, Lyapunov methods, and linear quadratic regulator control via LMI techniques

10.11591/ijece.v15i6.pp5276-5285
Rachid Boutssaid , Abdeljabar Aboulkassim , Said Kririm , El Hanafi Arjdal , Youssef Moumani
Stability issues in cyber-physical systems (CPS) arise from the challenging effects of nonlinear dynamics relation to multi-input, multi-output systems. This research proposed a robust control framework that combines Jacobian linearization, Lyapunov stability analysis, and linear quadratic regulator (LQR) control via linear matrix inequalities (LMIs). The robust methodology does the following: it applies linearization on the dynamics of the CPS; it establishes the stability of the system using Lyapunov functions and LMIs; and it designs an LQR controller. The proposed framework was validated through a comparison between the behavior of a linearized and nonlinear model. The autonomous vehicle application showed: a settling time of 20 seconds; an overshoot of 3.8187%; and a steady-state error of 2.688×10⁻⁷. The proposed framework is robustly demonstrated and has applications to areas in automation and smart infrastructure. Future work includes optimizing the design of weighting matrices and developing adaptive control features.
Volume: 15
Issue: 6
Page: 5276-5285
Publish at: 2025-12-01

Smart wearable glove for enhanced human-robot interaction using multi-sensor fusion and machine learning

10.11591/ijece.v15i6.pp5162-5172
Nourdine Herbaz , Hassan El Idrissi , Hamza Sabir , Abdelmajid Badri
Hand gesture recognition (HGR) using flexible sensors (flex-sensor) and the MPU6050 sensor has proved to be a key area of research in human-machine interaction, with major applications in biasing, rehabilitation, and assisted robotics. This paper proposes a wearable intelligent glove designed to operate a robotics arm in real time, relying on multi-sensor fusion and machine learning methods to enhance the system's responsiveness and precision. The proposed system enables the intuitive reproduction of hand movements and precise control of the robotic arm. In the context of Industry 4.0 and internet of things (IoT), the classification of gestures is necessary for maintaining operational efficiency. To guarantee gesture recognition, data signals from the smart glove are collected and trained by a recurrent neural network (RNN), which achieves 98.67% accuracy for real-time classification of seven gestures. Beyond industrial applications, the wearable smart glove can be exploited in a recognized circuit of all systems, including rehabilitation exercises that involve recording the progression of muscular activity for the assessment of motor functions and serve as a tool for patient recovery.
Volume: 15
Issue: 6
Page: 5162-5172
Publish at: 2025-12-01

Simulation and experimental validation of modular multilevel converters capable of producing arbitrary voltage levels using the space vector modulation method

10.11591/ijece.v15i6.pp5234-5248
Tran Hung Cuong , Pham Chi Hieu , Pham Viet Phuong
Modular multilevel converters (MMC) used forDC-AC energy conversion are becoming popular to connect distributed energy systems to the power systems. There are many modulation methods that can be applied to the MMC. The space vector modulation (SVM) method can produce a maximum number of levels, i.e., 2N+1, in which N is the number of sub- modules (SMs) per branch of the MMC. The SVM method can generate rules to apply to MMCs with any number of levels. The goal of this proposal is to easily expand the number of voltage levels of the MMC when necessary while still ensuring the quality requirements of the system. The proposed SVM method only selects the three nearest vectors to generate optimal transition states, therefore making the computations simpler and more efficient. This has reduced the computational load when compared to the previously applied SVM methods. This advantage ensures an optimal switching process and harmonic quality which will significantly improve the effectiveness of the proposed method was demonstrated through simulations on MATLAB/Simulink and experimental tests on 13-levels voltage MMC converter system using a 309 field-programmable gate array (FPGA) kit.
Volume: 15
Issue: 6
Page: 5234-5248
Publish at: 2025-12-01

Fractional fuzzy based static var compensator control for damping enhancement of inter-area oscillations

10.11591/ijece.v15i6.pp5130-5143
Tarik Zabaiou , Khadidja Benayad
Over time, the insertion of flexible alternating current transmission system (FACTS) components in the power grid became primordial to maintain the overall system stability. This paper proposed an innovative approach called hybrid auxiliary damping control based wide-area measurements for the static var compensator (SVC). The presented controller is a fractional-order fuzzy proportional integral derivative (FOFPID). Its principal task is to damp inter-area low frequency oscillations (LFOs) and to improve the power system stability over the transient dynamics. Then, a metaheuristic grey wolf optimization (GWO) method is applied to adjust the controller’s gains. The SVC-based FOFPID control scheme is implemented in a two-area four- machine test system employing the rotor speed deviations of generators as input signal. A comparative analysis of the elaborated controller with the integer PID and the fractional-order PID (FOPID) is performed to emphasize its effectiveness under a three-phase perturbation. Furthermore, a load variation effect test is completed to attest the control strategy robustness. Based on dynamic simulation results and performance indices, the suggested controller shows its robustness and provides increased efficiency for inter- area oscillations damping.
Volume: 15
Issue: 6
Page: 5130-5143
Publish at: 2025-12-01

The evolution of routing in VANET: an analysis of solutions based on artificial intelligence and software-defined networks

10.11591/ijece.v15i6.pp5388-5400
Lewys Correa Sánchez , Octavio José Salcedo Parra , Jorge Gómez
This study explored the evolution of vehicular ad hoc networks (VANET) and focused on the challenges and opportunities for routing in these dynamic environments. Despite advancements in traditional protocols, a significant gap persists in the ability to adapt to highly mobile environments with variable traffic, which limits routing efficiency and quality of service. Emerging technologies, such as artificial intelligence (AI) and software- defined networks (SDN), are discussed that have the potential to revolutionize the management of VANET. Machine learning can be used to predict traffic, optimize routes, and adapt routing protocols in real-time. Furthermore, SDN can simplify routing management and enable greater flexibility in network configurations. A comprehensive overview of the convergence of AI and SDN is presented, and the potential complementarities between these technologies to address routing challenges in VANET are explored. Finally, the implications of efficient routing in VANET for road safety, traffic management, and the development of new applications are discussed, and future research lines are identified to address challenges such as scalability, data security, and computational efficiency in vehicular environments.
Volume: 15
Issue: 6
Page: 5388-5400
Publish at: 2025-12-01
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