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Exploring gender bias, belonging, and prejudice among IT students in higher education

10.11591/ijere.v14i4.33291
Ruth G. Luciano
This study investigates gender discrimination, inclusion, and belonging among information technology (IT) students in higher education, with specific objectives to assess their sense of belonging, classroom participation, and perceptions of gender bias. The research aims to identify persistent issues related to gender equity and propose actionable solutions to foster inclusivity. Using a descriptive research design, the study surveyed 180 student-leaders, purposively selected to represent various year levels and provide diverse insights into academic and extracurricular experiences. Data were collected through a structured questionnaire adapted and modified to fit the study’s context. The instrument included Likert-scale items focusing on dimensions such as belonging, teaching environment, gender discrimination, and prejudice. Findings reveal that while most students feel respected by peers and professors, subtle forms of gender bias continue to affect female students, limiting role models and occasionally undermining their confidence and engagement. Practical implications highlight the need for gender-sensitive curricula, mentorship programs, and policy adjustments to create a more inclusive academic environment. Recommendations include increasing female representation in leadership roles, conducting gender sensitivity training, and diversifying course materials. These strategies aim to enhance gender equity, support student success, and establish a culture of inclusion within IT education.
Volume: 14
Issue: 4
Page: 3224-3233
Publish at: 2025-08-01

Performance analysis and comparison of machine learning algorithms for predicting heart disease

10.11591/ijai.v14.i4.pp2849-2863
Neha Bhadu , Jaswinder Singh
Heart disease (HD) is a serious medical condition that has an enormous effect on people's quality of life. Early as well as accurate identification is crucial for preventing and treating HD. Traditional methods of diagnosis may not always be reliable. Non-intrusive methods like machine learning (ML) are proficient in distinguishing between patients with HD and those in good health. The prime objective of this study is to find a robust ML technique that can accurately detect the presence of HD. For this purpose, several ML algorithms were chosen based on the relevant literature studied. For this investigation, two different heart datasets the Cleveland and Statlog datasets were downloaded from Kaggle. The analysis was carried out utilizing the Waikato environment for knowledge analysis (WEKA) 3.9.6 software. To assess how well various algorithms predicted HD, the study employed a variety of performance evaluation metrics and error rates. The findings showed that for both the datasets radio frequency is a better option for predicting HD with an accuracy and receiver operating characteristic (ROC) values of 94% and 0.984 for the Cleveland dataset and 90% and 0.975 for the Statlog dataset. This work may aid researchers in creating early HD detection models and assist medical practitioners in identifying HD.
Volume: 14
Issue: 4
Page: 2849-2863
Publish at: 2025-08-01

Integrating random forest and genetic algorithms for improved kidney disease prediction

10.11591/ijai.v14.i4.pp2797-2804
Bommanahalli Venkatagiriyappa Raghavendr , Anandkumar Ramappa Annigeri , Jogipalya Shivananjappa Srikantamurthy , Gururaj Raghavendrarao Sattigeri
This work offers a novel method for predicting chronic kidney disease (CKD) by combining random forest (RF) classification with genetic algorithm (GA) to optimize important parameters. The dataset comprises 1,659 patients with 51 clinical parameters. The suggested method emphasizes the optimization of random state values, test size, and essential hyperparameters, such as the number of trees in the forest, the least number of samples needed at a leaf node, and the smallest number of samples necessary to split an internal node. The optimization process is conducted in two stages: the first stage optimizes the random state and test size, while the second stage focuses on hyperparameters. Through extensive simulations over 50 runs, the study demonstrates that the optimized model achieves an accuracy ranging from 0.9451 to 0.9738. The results indicate a maximum increase in accuracy of 2.09%, showcasing the effectiveness of the GA-RF integrated approach in enhancing model performance. This work provides valuable insights into the impact of parameter optimization on machine learning (ML) models, particularly in medical diagnostics, and offers a robust framework for developing highly accurate predictive models.
Volume: 14
Issue: 4
Page: 2797-2804
Publish at: 2025-08-01

Using the ResNet-50 pre-trained model to improve the classification output of a non-image kidney stone dataset

10.11591/ijai.v14.i4.pp3182-3191
Kazeem Oyebode , Anne Ngozi Odoh
Kidney stone detection based on urine samples seems to be a cost-effective way of detecting the formation of stones. Urine features are usually collected from patients to determine if there is a likelihood of kidney stone formation. There are existing machine learning models that can be used to classify if a stone exists in the kidney, such as the support vector machine (SVM) and deep learning (DL) models. We propose a DL network that works with a pre-trained (ResNet-50) model, making non-image urine features work with an image-based pre-trained model (ResNet-50). Six urine features collected from patients are projected onto 172,800 neurons. This output is then reshaped into a 240 by 240 by 3 tensors. The reshaped output serves as the input to the ResNet-50. The output of this is then sent into a binary classifier to determine if a kidney stone exists or not. The proposed model is benchmarked against the SVM, XGBoost, and two variants of DL networks, and it shows improved performance using the AUC-ROC, Accuracy and F1-score metrics. We demonstrate that combining non-image urine features with an image-based pre-trained model improves classification outcomes, highlighting the potential of integrating heterogeneous data sources for enhanced predictive accuracy.
Volume: 14
Issue: 4
Page: 3182-3191
Publish at: 2025-08-01

Assessing education students’ self-directed learning experiences for optimal online learning

10.11591/ijere.v14i4.32610
Marisol Jane M. Beray , Angelica M. Cubero , Robegine G. Casidsid , Rhessan Jade B. Codenera
The advancement of technology enabled the success implementation of online platforms and distance education. Despite its advantages, learners are faced with challenges in the implementation. The study focused on assessing the self-directed learning experiences of education students at Caraga State University Cabadbaran campus, Cabadbaran City, Philippines, in online learning environments. It aimed to assess the self-directed learning experiences concerning learners’ learning motivation, learning style, and technology preparedness. The participants of the study were 154 education students at the College of Industrial Technology and Teacher Education utilizing the descriptive survey research design. The study revealed that students are accountable for their learning, completing activities despite distractions and setting attainable goals. Further analysis showed that learners favor audio-visual learning modalities and have strong self-management skills. However, they may feel less motivated when instructors are not available online to supervise them and felt intimidated when using technological devices. It is suggested that the university should continue promoting and implementing self-directed learning approaches when necessary and offer support like training and resources that cater to learners’ preferences and needs, ensuring a consistent and inclusive application of self-directed learning methodologies for all the participants.
Volume: 14
Issue: 4
Page: 2781-2789
Publish at: 2025-08-01

The degree of exercise of academic freedom and its relationship to job satisfaction: ‎Al-Yarmouk University model

10.11591/ijere.v14i4.31445
Khawla Mahmoud Al Alawneh , Nidaa Izhiman , Shaima Mokhemer Yahya , Sara Mohammad El-Freihat
Academic freedom in Jordanian universities is the subject of debate and research, due to the policies of university education imposed within the country. However, academic freedom has been researched in Jordan for more than two years. This study aimed to find out the level of academic freedom practiced by faculty members at Yarmouk University and its relationship to job satisfaction from a point of view, as there was recently a difference in views on the exercise of academic freedom or the imposition of educational policies in Jordanian universities according to the views of academics. About 317 members of the university were selected in a simple random way. The correlational descriptive approach and a two-part questionnaire were used: the first part measures the degree of academic freedom exercised by faculty members in three areas (freedom of expression, research, and teaching). The second part measures the level of job satisfaction. The results indicated that the degree of academic freedom exercised by faculty members was high, as well as the level of job satisfaction, as it was found that faculty members were significantly satisfied with their jobs. There was also a correlation between the degree of academic freedom exercised by faculty members and the level of job satisfaction of faculty members. These results shed positive light on the policies adopted in the country to educate university students in order to continue to keep pace with global developments and global trends in university education and work to amend some university education policies with the concerned authorities.
Volume: 14
Issue: 4
Page: 2822-2831
Publish at: 2025-08-01

Learning styles and academic performance: a correlational study among engineering university students

10.11591/ijere.v14i4.31476
Maribel Cardenas Yauri , Jorge Augusto Sánchez Ayte , Jacinto Joaquin Vertiz Osores , Zanhy Leonor Valencia Reyes
In a context of little explanation of the links between learning styles (LS) and academic performance (AP) in university students, a quantitative analysis of these relationships was proposed in mechanical and electrical engineering students from a public university in Lima, Peru. The Honey-Alonso learning styles questionnaire (CHAEA) was used to identify the students’ styles. Grades in various subjects were used as data on AP. Discriminant analysis and hierarchical clustering were applied to develop an explanatory model of the relationship. The findings revealed that most students were classified in a central level of AP. LS were distributed in order of relevance of contribution as: ‘theoretical’, ‘reflective’, ‘active’ and ‘pragmatic’. The ‘theoretical’ and ‘reflective’ styles showed greater affinity. Better clustering was observed in the ‘in process’ and ‘achievement’ levels of AP. The level of ‘starting’ was diffuse among students, preventing it from being clearly associated with any LS, although it is recommended not to dismiss it because it may represent students who require additional support from teaching staff. This research contributes a holistic view of the factors that influence university AP and highlights the importance of conducting further research in this field.
Volume: 14
Issue: 4
Page: 2999-3008
Publish at: 2025-08-01

Validity and reliability of the questionnaire of Chinese self-efficacy for Chinese as a second language learners in China

10.11591/ijere.v14i4.33568
Yulan Peng , Muhammad Zuhair Zainal
Self-efficacy is a pivotal predictor of academic success in second language learning. With the growing enrollment of international students in Chinese language programs in China, there is a pressing need for validated tools to assess self-efficacy among Chinese as a second language (CSL) learners. This study addresses this need by adapting and validating the English-Chinese questionnaire of Chinese self-efficacy (QCSE) from the original questionnaire of English self-efficacy (QESE). Using a cross-sectional design, data were collected from 174 international college students in Jiangxi Province for psychometric evaluation. Confirmatory factor analysis (CFA) assessed the instrument’s reliability and validity, with results showing a high overall Cronbach’s alpha of 0.942 and strong dimension-specific values (listening, speaking, reading, and writing) ranging from 0.915 to 0.927. All item outer loadings exceeded 0.7, and the average variance extracted (AVE) values were above 0.5, confirming the robustness of the QCSE. This validated instrument addresses a critical gap in CSL self-efficacy assessment and provides valuable insights for learners, educators, and policymakers.
Volume: 14
Issue: 4
Page: 2510-2520
Publish at: 2025-08-01

Gender differences in motivation and problem-solving in a physics course online problem-based learning

10.11591/ijere.v14i4.31105
Elnetthra Folly Eldy , Fauziah Sulaiman , Mohd Zaki Ishak , Lorna Uden , Jo-Ann Netto-Shek
Online learning has been crucial since COVID-19, yet its effectiveness, particularly in physics education, remains debated. Understanding students’ motivation and problem-solving abilities in online environments is critical. This paper examined and presented the gender difference in motivation and problem-solving skills using an integrated online problem-based learning (iON-PBL) in a physics course. Developed using analysis, design, development, implementation, and evaluation (ADDIE) mode, iON-PBL module of physics guided students through problem-solving activities over 13 weeks. A post-test–delayed post-test design was used to assess retention of motivation and problem-solving skills. The study involved 116 pre-university students from Universiti Malaysia Sabah (88 females, 28 males). Motivation was measured using the motivated strategies for learning questionnaire (MSLQ) (four components), and problem-solving skills were assessed with the problem-solving inventory (PSI) (three components). Data analysis was conducted using SPSS version 28. Findings showed a significant gender difference in the ‘cognitive strategy’ component of motivation at the post-test, favoring female students. However, this difference was not sustained in the delayed post-test. In contrast, no gender difference was found in problem-solving at the post-test, but females scored significantly higher in ‘personal control’ in the delayed post-test. These findings suggest that female students are more likely to maintain cognitive strategies and personal control in online learning. Educators should consider targeted strategies to support male students’ motivation and problem-solving development in virtual environments to foster gender equity. Educators should consider targeted strategies to support male students’ motivation and problem-solving development in virtual environments to foster gender equity.
Volume: 14
Issue: 4
Page: 2832-2845
Publish at: 2025-08-01

Researching creativity in education from ASEAN countries: bibliometric analysis

10.11591/ijere.v14i4.33669
Tuan-Vinh Nguyen , Viet-Nhi Tran
Creativity has emerged as a cornerstone of 21st-century education, particularly within Association of Southeast Asian Nations (ASEAN) countries striving to foster innovation and knowledge-based economies. This bibliometric study examines 453 publications on creativity in education across ASEAN from 2000 to 2024, utilizing Scopus data analyzed through Biblioshiny and VOSviewer. The findings reveal Indonesia as the dominant contributor (65% of publications), with scholars like Zubaidah and Corebima making significant contributions through highly cited works. Key journals, including “Thinking Skills and Creativity” and the “International Journal of Instruction”, have played crucial roles in disseminating ASEAN research globally. The thematic analysis identifies five major clusters centered on science, technology, engineering, and mathematics (STEM) integration, project-based learning, digital technologies, research methodologies, and professional development. Temporal network analysis demonstrates an evolution from discipline-specific approaches toward broader conceptualizations of creativity and an increased emphasis on technological integration. Despite these advances, the study identifies persistent challenges in cross-national collaboration, methodological consistency, and longitudinal research. This analysis provides strategic insights for educators, researchers, and policymakers in aligning educational practices with regional innovation goals while highlighting the need for enhanced research collaboration and standardized assessment tools.    
Volume: 14
Issue: 4
Page: 2593-2604
Publish at: 2025-08-01

Mobile gaming in education: a bibliometric analysis of trends and performance

10.11591/ijere.v14i4.32991
Lim Seong Pek , Fatin Syamilah Che Yob , Rita Wong Mee Mee , Walton Wider , M. Zaini Miftah , Jun S. Camara
This bibliometric analysis, based on data from the Web of Science (WoS) database, examines the increasing role of mobile gaming in education. Mobile gaming has demonstrated strong potential to improve student engagement, motivation, and cognitive skills, yet research on its integration within formal educational settings is still emerging. By analyzing data from 247 scholarly articles published between 2014 and 2023, this study identifies key trends, influential contributors, and areas of growing interest within this field. The findings reveal a significant rise in publications and citations, underscoring the expanding recognition of mobile gaming’s educational value. Prominent themes in the literature include gamification strategies that encourage collaboration, problem-solving, and critical thinking, focusing on multiplayer online battle arenas (MOBAs) and other interactive gaming environments that facilitate active learning. The analysis also highlights leading countries, with the United States at the forefront of research output, alongside significant institutional contributions globally. These insights guide educators and policymakers on leveraging mobile gaming in educational practices while addressing potential challenges. By emphasizing the alignment of mobile gaming with sustainable development goal 4 (quality education), this study establishes a foundation for future research into innovative, technology-enhanced learning solutions that are accessible, engaging, and effective.
Volume: 14
Issue: 4
Page: 2676-2685
Publish at: 2025-08-01

Influence of information and communication technologies on academic resilience in vulnerable contexts

10.11591/ijere.v14i4.31486
Mario Macea-Anaya , Jeny Vargas-Moreno , Rubén Baena-Navarro
This study investigates how the use of information and communication technologies (ICT) influences the academic resilience of high school students in Soacha, Colombia, an area with significant socioeconomic challenges. The objective of the research is to analyze whether greater access to and proper use of ICT can improve students’ ability to face academic and emotional adversities. A mixed methodology was applied, combining quantitative surveys and qualitative interviews with 300 students from three educational institutions, measuring ICT use and academic resilience through validated scales. The findings revealed a significant positive correlation (r=0.95) between ICT use and academic resilience, demonstrating that students who used ICT more frequently showed higher levels of self-efficacy, better stress management, and more efficient academic organization. These results highlight the importance of integrating ICT into educational policies in vulnerable contexts, given their impact on both academic performance and students’ emotional well-being. It is recommended to prioritize digital competence training in educational institutions to strengthen students' abilities to face academic and emotional challenges.
Volume: 14
Issue: 4
Page: 2617-2627
Publish at: 2025-08-01

Examining stressors’ influence on job satisfaction among engineering college faculty: a cross-sectional study

10.11591/ijere.v14i4.30209
Krishnamoorthy V. , Parthasarathy Karthikeyan , Rajini J. , Anandavel V. , Hariharasudan Anandhan
This study explores the various dimensions of stress experienced by engineering college teachers and their impact on job satisfaction. The research specifically examines the relationship between stress factors and job satisfaction among faculty members in engineering colleges in the western region of Tamil Nadu. A cross-sectional research design was used in this study. Data were collected using a structured questionnaire from 210 faculty members between June and December 2023, employing a convenient sampling method. The questionnaire comprised three sections: demographic details, stress dimensions, and job satisfaction variables, which were adapted from previous studies. Reliability testing ensured data consistency and factor analysis identified core stress dimensions. Multiple regression analysis was applied to assess the influence of stress dimensions on job satisfaction, while correlation analysis examined relationships between the variables. The data were analyzed using SPSS version 20. Key findings revealed that organizational climate, role conflict, professional and personal growth, and role ambiguity significantly influence job satisfaction. However, role overload did not show a notable impact. A strong correlation between professional growth and job satisfaction was observed, highlighting a critical area for targeted interventions. These insights provide valuable guidance for policymakers in academic institutions to develop effective strategies to mitigate faculty stress and enhance job satisfaction within the academic environment.
Volume: 14
Issue: 4
Page: 3018-3026
Publish at: 2025-08-01

Determinants of artificial intelligence acceptance among undergraduates

10.11591/ijere.v14i4.32565
Tan Owee Kowang , Lim Kim Yew , Goh Chin Fei , Ong Choon Hee
Despite the potential benefits of artificial intelligence (AI) brings to education, its extensive use does not automatically guarantee effective integration or consistent improvements in learning. Hence, this research aims to identify the determinants of AI acceptance among undergraduates and examine the relationship between these determinants and AI acceptance. Five determinants of AI acceptance were identified based on the technology acceptance model (TAM) and empirical evidence: perceived effectiveness of AI, user satisfaction, user attitude toward AI technology, attitude toward using AI, and user self-efficacy. This quantitative study focused on 791 undergraduates from a management school in Malaysia. A questionnaire was distributed to 310 undergraduates using a stratified sampling method, and 259 responses were collected. Descriptive analysis results indicated that undergraduates perceive attitudes toward AI technology and using AI as very important determinants of AI acceptance. Pearson correlation analysis also revealed that four determinants (perceived effectiveness of AI, satisfaction in using AI, attitude towards AI technology, attitude towards using AI) significantly correlated with AI acceptance. This finding suggests that, within the context of AI acceptance among management school undergraduates, attitude-related determinants are the primary drivers. The findings from this research could be used by the management school as a reference to enhance undergraduates’ AI acceptance levels and identify areas for inclusive education system improvement.
Volume: 14
Issue: 4
Page: 2773-2780
Publish at: 2025-08-01

Playing catch-up: Vietnamese rural student voices on bridging academic marginalization

10.11591/ijere.v14i4.32663
Danh Cong Vu , Phuong Thanh Nguyen
The increasing adoption of English as the instructional language in Vietnamese higher education has created significant learning barriers for rural students. Their under-resourced schools and a communicative English-neglected curriculum provided limited foundations prior to engaging in lectures, presentations, and discussions now conducted exclusively in academic English. Rural students often face significant challenges when transitioning into this demanding academic environment due to their limited prior exposure to English and the disparities in educational resources between rural and urban areas, yet there is limited understanding of their experiences and coping mechanisms. This study examines the challenges faced by rural Vietnamese undergraduate English majors and investigates their coping strategies in achieving academic competency. Using semi-structured interviews, this case study investigated three Vietnamese undergraduate English majors from low-income, rural backgrounds at an urban university. The findings revealed two key dimensions: i) participants initially experienced significant challenges including skill deficits across all language domains and identity struggles manifesting as feelings of inadequacy and isolation and ii) students developed a comprehensive set of coping strategies encompassing language learning, social support, and psychological adaptation. The study highlights the necessity of implementing multi-dimensional support systems that address not only language learning but also social and psychological aspects of academic adaptation. Such comprehensive support, through precision language education, can promote more equitable access and inclusion in language education.
Volume: 14
Issue: 4
Page: 3304-3314
Publish at: 2025-08-01
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