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WVisionBERT-VL: a multimodal model architecture for toxicity classification on social media platforms using large language models

10.11591/ijai.v15.i4.pp3365-3375
Witta Listiya Ningrum , Achmad Benny Mutiara , Diana Ikasari
The increasing prevalence of toxic content on social media, conveyed through text, images, and videos, poses significant challenges for automated content moderation systems. Although prior studies have reported promising results in unimodal and bimodal settings, they often fail to capture implicit and contextual toxicity emerging from interactions across multiple modalities, particularly in non-English environments. This paper proposes WVisionBERT-VL, an end-to-end multimodal framework for toxicity detection that integrates text, image, and video modalities within a unified architecture. The proposed model incorporates modality-specific encoders, bidirectional multi-head cross attention (BMHCA) for cross-modal synchronization, and an adaptive fusion gate to dynamically balance modality contributions. A balanced multimodal dataset is constructed from social media platforms, including X, Instagram, and TikTok, and refined using a model-based labeling strategy with limited human-in-the-loop validation. Experimental results on a custom Indonesian dataset demonstrate strong in-domain performance, achieving an accuracy of 94.12%, a macro-F1 of 0.9407, and a receiver operating characteristic - area under the curve (ROC-AUC) of 0.9721, with robustness further validated through five-fold cross-validation. Cross-dataset evaluation highlights challenges related to domain shift, underscoring the need for future research on robust and domain-adaptive multimodal toxicity detection.
Volume: 15
Issue: 4
Page: 3365-3375
Publish at: 2026-08-01

English medium instruction in Jordanian medical education: a systematic review

10.11591/ijere.v15i4.39643
Hassan Mohammad Bani-Issa , Norsofiah Abu Bakar , Muhammad Zaid Daud , Jong Hui Ying , Hytham M. Bany Issa
English-medium instruction (EMI) dominates Jordanian medical education, yet its equity implications and consequences for assessment validity remain poorly evidenced, particularly after the disruptions of the COVID-19 pandemic. Following preferred reporting items for systematic reviews and meta-analyses (PRISMA) 2020 guidelines, this review searched Scopus, Web of Science, ERIC, and PubMed for peer-reviewed work published between 2000 and 2024. A total of 34 studies met inclusion criteria after mixed methods appraisal tool (MMAT) quality appraisal and were analyzed through thematic synthesis. The evidence reveals a structural paradox: EMI supports international academic integration while disadvantaging students from Arabic-medium secondary schools by conflating English proficiency with medical competence. The lexical and morphological complexity of medical English increases cognitive demands and became more pronounced during pandemic-related online teaching. Students rely on code-switching and morphological analysis as coping strategies, but neither is recognized in policy or assessment. The review delivers the first PRISMA-aligned synthesis of EMI in Jordanian medical education, proposes the Jordanian Medical English Corpus (JoMEC) as a corpus-based diagnostic for measuring lexical burden, and reframes EMI equity as a measurable issue of assessment validity rather than a normative concern alone. Findings support bilingual scaffolding and validity-oriented assessment reform to advance equity in medical education across Jordan and comparable Middle East and North Africa (MENA) contexts.
Volume: 15
Issue: 4
Page: 3204-3214
Publish at: 2026-08-01

Immersive technology in English language learning: a bibliometric analysis

10.11591/ijere.v15i4.37947
Zhou Bo , Lim Seong Pek , Nahdia Kabir , Mohamed Bouteraa , Asna Asna
This bibliometric analysis, drawing on data from the Web of Science (WoS) core collection, explores the expanding role of immersive technologies in English language education. Virtual reality (VR) and augmented reality (AR) have shown strong potential to improve learner motivation, engagement, and communicative competence, yet their integration into formal English language settings remains uneven. By analyzing 248 peer-reviewed articles published between 2021 and 2025, this study finds significant trends, influential contributors, and emerging areas of interest within the field. The findings show a steady increase in publications and citations, reflecting growing recognition of the educational value of immersive environments. Prominent themes include emotional engagement, lowered language anxiety, and improved performance in vocabulary, speaking, listening, and cultural understanding. Much of the literature underlines authentic and situated learning, VR-based interactive environments, VR-supported problem-based learning, and AR-assisted vocabulary development. The analysis also identifies leading countries, with China and the United States producing the largest share of research, a pattern supported by strong institutional participation worldwide. These insights help guide educators and policymakers as they consider how to bring immersive technologies into English instruction. The study also establishes a foundation for future research on effective, engaging, and sustainable immersive language learning practices. Overall, these findings clarify how research on immersive technologies in English language education has evolved between 2021 and 2025 and identify influential studies and key contributors. They also point to persisting gaps, such as equity, teacher readiness, and cognitive-load–informed design, that warrant further investigation.
Volume: 15
Issue: 4
Page: 3623-3635
Publish at: 2026-08-01

Pedagogical environments for the formation of research competence in students within practice-oriented chemistry education

10.11591/ijere.v15i4.39216
Kanat Sadykov , Nesipkhan Bektenov , Nursulu Zhussupbekova , Ainash Baidullayeva
Despite the importance of research competence in modern chemical education, traditional instructional methods often fail to sufficiently prepare students for independent scientific inquiry and professional growth, creating a gap between theoretical knowledge and practical application. The research aimed to experimentally validate a multifaceted system of pedagogical environments (research-focused, metacognitive-digital, and motivational) designed to foster research competence in chemistry students through practice-oriented learning. The research design of the research was a 30-week longitudinal pedagogical experiment utilizing a quasi-experimental design with pre- and post-test assessments. The sample comprised of 325 third- and fourth-year future chemistry teachers (experimental group (EG)=165; control group (CG)=160) from a national university in Kazakhstan. Statistical analysis (Pearson χ² test) confirmed significant growth in the EG across all competence criteria, with the proportion of students reaching “high-level” research proficiency increasing from 10.9% to 38.8%. The key implication is integrating structured pedagogical environments into teacher-training curricula effectively bridges the gap between theoretical knowledge and practical inquiry, providing a scalable framework for modernizing science, technology, engineering and mathematics (STEM) education.
Volume: 15
Issue: 4
Page: 3463-3478
Publish at: 2026-08-01

Artificial intelligence technologies in teaching Russian as a foreign language

10.11591/ijere.v15i4.38077
Larissa Krymova , Navruz Khasanov , Zhamiila Arstanbekova , Ariya Azamatova , Nuraisha Bekeyeva
The present study aims to investigate the impact of integrating artificial intelligence (AI) technologies into teaching Russian as a foreign language (RFL) from the perspective of educators. Employing a mixed-methods research design, the study utilized several methodologies, including a teacher survey, an analytical-descriptive approach to data interpretation, and the development and evaluation of AI-based interventions. The study sample comprised 120 RFL instructors from three public universities in Kazakhstan. Preliminary findings revealed a considerable awareness among teachers regarding various AI technologies, such as chatbots, voice assistants, the ChatGPT neural network, educational platforms, gaming applications, and task design tools. Nonetheless, the practical utilization of these technologies varied significantly, with only a subset of teachers incorporating them into their regular teaching practices. The study culminated in the development of a conceptual framework for AI-driven educational interventions, incorporating platforms such as Coursera, Moodle, Open EdX, and eFront; game-based applications including Duolingo, Talk2Russia, and Russian Verbs Pro; and task creation tools such as Kahoot! and Quizlet. Following the integration of these interventions into the curriculum, post-implementation evaluations indicated that teachers generally perceived the tools as effective, with the average effectiveness rating surpassing 4.0 out of 5.0 across all assessed categories. The findings of this study have practical applicability; they can be used to enhance professional development programs for teachers of the Russian language and to formulate strategies for the integration of AI technologies into language education within the Central Asian region.
Volume: 15
Issue: 4
Page: 3422-3438
Publish at: 2026-08-01

Rethinking computer-based examinations in higher education: psychological, technical, and pedagogical challenges from students’ learning experience and future directions

10.11591/ijere.v15i4.39228
Ahmad Adnan AlZyoud , Eman Mohammad Qudah
The swift adoption of computer-based examinations (CBEs) in higher education has revolutionized assessment methods; nonetheless, there is a lack of thorough research investigating the psychological, technical, and pedagogical experiences of students using these systems. This research explores the various challenges associated with CBEs at Yarmouk University and assesses their influence on students’ learning experiences. A cross-sectional quantitative survey was conducted among 545 undergraduate students from various academic fields during the first semester of the 2025–2026 academic year. Both descriptive and inferential analyses were performed to evaluate students’ perceptions. Research shows a moderate level of acceptance for CBEs, but notable worries remain. The main source of stress was found to be technical reliability, often overshadowing worries about educational content. The one-way navigation aspect was recognized as a significant obstacle, which restricted students’ ability to review answers and added to cognitive strain and hasty decision-making. Furthermore, a significant gap in feedback was noted, as students mostly viewed the system as a tool for grading rather than a resource for ongoing learning. The research finds that successful digital assessment necessitates not just operational effectiveness but also adaptable design, alignment with pedagogical goals, and valuable feedback systems. Practical implications involve rethinking navigation elements, improving technical support, and offering focused training for faculty.
Volume: 15
Issue: 4
Page: 2861-2873
Publish at: 2026-08-01

Immuno-bioinformatics analysis of progressive alignment and logic learning machine-derived viral conserved sequences

10.11591/ijai.v15.i4.pp3189-3201
Felza Ridho , Mohammad Isa Irawan , Nurul Hidayat , Awik Puji Dyah Nurhayati
This study proposes a method to identify conserved sequence positions in viral data, with the primary goal of facilitating their translation into proteins. The approach aims to support the early detection of sub-sequences that hold promise as vaccine candidates. The method involves five key steps. First, mutation analysis using the Kimura model: analyzed mutations in the viral data using the Kimura model to provide insights into sequence variations. Second, alignment method selection to align sequences effectively, the progressive alignment approach with the neighbor-joining algorithm was chosen. Third, hybrid algorithm for identifying unchanged sequences: a hybrid algorithm that combines progressive alignment and a logic learning machine (LLM) was employed to identify unchanged sequences. Fourth, determining conserved sequences: based on the longest unchanged sequence, conserved regions within the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) data were identified. Fifth, immuno-bioinformatics analysis for vaccine candidate peptides: using immuno-bioinformatics, protein sequences were analyzed to identify potential vaccine candidates based on B cell epitopes. Experimental validation confirmed the algorithm’s ability to identify candidate proteins and generate vaccine-worthy peptides from conserved protein sequences. The streamlined approach focuses on conserved protein sequences, ensuring efficient and targeted identification of vaccine candidates. By leveraging these conserved regions, contributions can be made to the expedited development of vaccines.
Volume: 15
Issue: 4
Page: 3189-3201
Publish at: 2026-08-01

Data-driven analysis of growth factors in oyster mushroom cultivation: a case study from Indonesia’s market

10.11591/ijai.v15.i4.pp3568-3580
Yosef Budiman , Gilang Adi Prasetyo , Asma’ Khoirunnisa’ , Hanifah Mar’atush Shalihah , Muhamad Riyan Maulana , Yanuar Agung Fadlullah , Sugiri Sugiri
The oyster mushroom is one of the potential agricultural products that can be developed as an alternative to other agricultural products, to maintain Indonesia's economic condition. However, the production of oyster mushrooms remains low and falls short of the minimum amount of market demand. This study employs a machine learning (ML)–based approach to identify the key parameters influencing oyster mushroom production rates. Recursive feature elimination (RFE) was applied to reduce the initial 19 features to nine, enabling faster processing while maintaining high predictive accuracy. The results showed that agricultural features showed a high contribution rather than environmental, economic, and demographic features. Furthermore, these parameters were related to the train-test analysis to visualize the statistical analysis shown by the best method, adaptive boosting (AdaBoost), with coefficient of determination (R2), mean squared error (MSE), and mean absolute error (MAE) values of 0.997575, 0.009841, and 0.085884, respectively. Related research relevant to the research findings was analyzed to validate that agricultural product features affect the decline of oyster mushroom production. Other supported research conducted by integrating real-time analysis and twin digital models, which can enhance substrate quality.
Volume: 15
Issue: 4
Page: 3568-3580
Publish at: 2026-08-01

YOLO-based deep learning for tooth detection, segmentation, and numbering in panoramic radiographs

10.11591/ijai.v15.i4.pp3591-3602
Sri Oktamuliani , Luqyana Mahdiyah , Haritsul Haq , Nesa Perdana Putri , Wulandani Liza Putri
Panoramic dental radiographs are essential for diagnosis and treatment planning, but are difficult to interpret due to overlapping anatomical structures and limited image quality. Automating tooth detection, segmentation, and numbering can enhance clinical efficiency and reduce observer variability. This study developed a you only look once version 8 (YOLOv8)-based deep learning model for automatic detection and segmentation of teeth in panoramic radiographs. A dataset of 302 images containing 9,009 annotated teeth was divided into 70% training, 20% validation, and 10% testing sets. Teeth were labeled using the Federation Dentaire Internationale (FDI) two-digit numbering system with bounding boxes and segmentation masks. Model performance was assessed using precision, recall, F1-score, and mean average precision (mAP) at intersection over union (IoU) thresholds of 0.5 and 0.5–0.95. The model achieved bounding box precision, recall, and F1-score of 0.9075, 0.9352, and 0.9212, respectively, and segmentation scores of 0.9078, 0.9344, and 0.9209. Bounding box mAP@0.5 reached 0.9510 and mask mAP@0.5 was 0.9504, while stricter thresholds lowered performance to 0.7763 and 0.6896. Class-wise analysis showed high accuracy overall but reduced performance on posterior teeth due to anatomical overlap and peripheral image quality. YOLOv8 enables fast, accurate, and robust tooth detection and segmentation, supporting real-time computer-aided diagnosis in orthodontics, prosthodontics, and forensic dentistry.
Volume: 15
Issue: 4
Page: 3591-3602
Publish at: 2026-08-01

A model for enhancing teacher training by integrating internet of things, cloud computing, and robotics in a blended learning

10.11591/ijere.v15i4.39473
Meiramgul Mukhambetova , Salamat Idrissov , Nurgul Baitemirova
Despite educational digitalization, a divide persists in integrating robotics and the internet of things (IoT) into classroom practice. Both pre-service and in-service teachers struggle to translate theory into application due to the lack of unified methodological frameworks. This gap demonstrably undermines student academic performance. The present study introduces the teachers-oriented, learning content, methodological approaches, digital support, expected outcomes (T-LMDE) model to bridge this divide through the synergistic integration of key dimensions. The model integrates robotics and IoT with cloud computing, utilizing the Arduino IoT cloud for data analytics and remote control. Delivered via a blended-learning portal, the curriculum provides didactic materials and feedback mechanisms, enabling educators to master complex engineering solutions. The model’s efficacy was empirically validated with 31 participants across two modalities: a 15-week course for pre-service teachers and an 80-hour intensive program for in-service educators. Using a quasi-experimental, mixed-methods design, data were analyzed via paired t-tests and qualitative thematic scrutiny. Findings confirm the model’s positive impact on professional transformation and competency advancement, shifting educators from content consumers to “teachers-as-designers”.
Volume: 15
Issue: 4
Page: 3322-3333
Publish at: 2026-08-01

Blended-language instructional-approach as a determinant of science learning in rural classrooms

10.11591/ijere.v15i4.39263
Atomatofa Rachel Ovuezirie , Sekegor Crescentia Ojenikoh , Avbenagha Andrew , Ewesor Stella
Scientific concepts such as gravity continue to pose challenges for students in rural and under-resourced classrooms, where reliance on a single language of instruction often restricts access to meaning and limits conceptual understanding. This study investigated the impact of a blended English and Urhobo (BEAU) instructional approach on junior secondary one students’ learning and retention of gravity concepts in rural Nigeria. A quasi-experimental pre-test–post-test non-equivalent control group design was employed with 243 students assigned to English-only, Urhobo-only, or BEAU instructional conditions. A validated 30-item multiple-choice gravity test was administered, and analysis of covariance (ANCOVA) was used to examine differences while controlling for pre-test scores. The findings show that students taught using the BEAU language approach achieved better significantly in both post-test and retention tests compared to those taught using English-only or Urhobo-only instructional approaches. The findings provide empirical evidence that the blended language instructional approach enhances science learning outcomes in rural Nigerian contexts. Linguistically responsive instructional approach improves conceptual understanding and supports long-term retention of abstract scientific ideas, underscoring the importance of leveraging students’ linguistic resources to strengthen science education in rural and under-resourced classrooms.
Volume: 15
Issue: 4
Page: 3636-3645
Publish at: 2026-08-01

Bridging ethics and performance in engineering education through predictive learning analytics

10.11591/ijere.v15i4.38767
Hamza Abu Owida , Areen Arabiat
This literature review examines the opportunities, implementation challenges, ethical implications, and emerging directions of predictive learning analytics (PLA) in engineering education. Using a structured review of the literature, the study synthesizes evidence from several publications with emphasis on studies examining risk prediction, personalized support, curricular improvement, interpretability, fairness, and intervention design. The review shows that PLA can improve early identification of at-risk students, support adaptive learning pathways, and inform data-driven refinements in engineering curricula; however, its impact depends on data quality, model transparency, institutional capacity, and the availability of timely human support. The analysis further indicates that the most consequential barriers are fragmented data ecosystems, the difficulty of translating predictions into effective interventions, and unresolved ethical concerns related to privacy, bias, consent, and student agency. The article contributes to educational research by offering an integrated synthesis that connects technical development with pedagogical evaluation and ethical governance in engineering education. It concludes by proposing that future PLA adoption should align predictive modeling with explainable artificial intelligence, learning-theory-informed intervention design, and institution-level implementation strategies. Publications were selected for relevance to PLA in engineering education and then synthesized narratively across opportunities, challenges, ethics, and future directions.
Volume: 15
Issue: 4
Page: 2973-2982
Publish at: 2026-08-01

Determinants of entrepreneurial intentions of private university students in Thailand

10.11591/ijere.v15i4.39455
Nithima Yuenyong , Tanpat Kraiwanit , Ekaphot Congkrarian
The growing interest in student entrepreneurship, research has yet to comprehensively examine both internal and external determinants of startup intentions among private university undergraduates in Thailand, a context shaped by rapid digital transformation and national innovation priorities. This study investigates the factors predicting entrepreneurial intentions among 400 undergraduate students at private universities in Pathum Thani, Thailand. Using a quantitative research design, data were collected via a validated structured questionnaire and analyzed through descriptive statistics and stepwise multiple regression. Five factors significantly predicted startup entrepreneurial intention: financial resources, entrepreneurial skills, prior experience, economic conditions, and technological factors, collectively explaining 60.3% of the variance. Financial resources and entrepreneurial skills emerged as the strongest predictors, while political-legal, socio-cultural, and environmental factors were not significant predictors. The study concludes that entrepreneurial intention is shaped by the interplay between individual competencies and contextual enablers. These findings call for universities to strengthen entrepreneurship education across all faculties and for policymakers to improve students' access to financial resources and startup support mechanisms.
Volume: 15
Issue: 4
Page: 3111-3123
Publish at: 2026-08-01

Artificial intelligence usage in the teaching-learning process: perception and challenges

10.11591/ijere.v15i4.38964
Ishani Basak , Benny Thomas , Shinto Thomas
Artificial intelligence (AI) has advanced in the post-pandemic era and is unavoidable in teaching and learning. Teachers’ perceptions, as the primary gatekeepers, are essential for ensuring quality education and inclusive classrooms, with AI as a collaborator. While some teachers resist these technological shifts, others are actively adapting an AI-assisted teaching approach. We conducted this study to understand the reasons for teachers’ resistance (challenges and difficulties) and how they perceive the use of AI in the teaching, learning, and assessment process, because the first step in effective incorporation is having a favorable attitude towards it. Hence, this study explored the perceptions of 15 secondary private school teachers, selected through purposive sampling, regarding the incorporation of AI into teaching, learning, and assessment processes, as well as the challenges they faced. The researchers developed an in-depth interview schedule and conducted interviews to understand participants’ perceptions and challenges. The data is analyzed following the thematic analysis steps by Braun and Clarke. Thematic analysis revealed that teachers demonstrated a positive understanding towards the pedagogical relevance of AI, rather than merely having a favorable perception. Furthermore, teachers predominantly viewed AI as an additional tool to enhance the effectiveness of knowledge transactions and instructional design. The challenges include infrastructure accessibility and professional training; time management for preparation, skill updating, and fulfilling varied teaching and other responsibilities; the inability to verify the accuracy of information; and parental mindset. This study offers insights for developing AI-aided teacher training and relevant curricula for schools.
Volume: 15
Issue: 4
Page: 3124-3140
Publish at: 2026-08-01

Culturally grounded student engagement in global citizenship education in Philippine higher education

10.11591/ijere.v15i4.39524
Airyn M. Arbuliente , Erwin B. Berry , Alvin O. Cayogyog , El Dixon G. Plazo
Global citizenship education (GCE) is increasingly integrated into higher education; however, student engagement remains limited, often characterized by superficial participation rather than meaningful involvement. This study addresses this gap by examining how students experience engagement in GCE within a developing-country context. Using an interpretive phenomenological approach, data were collected from 25 undergraduate students in a Philippine state college through in-depth interviews and analyzed using interpretive phenomenological analysis (IPA). The findings reveal that engagement becomes meaningful when students participate in authentic community-based activities, develop emotional connections to real-world issues, engage in critical reflection on complex systems, and exercise voice and initiative in learning. Importantly, the study identifies culturally grounded engagement as a key dimension, showing that Filipino values such as bayanihan (communal unity), pakikipagkapwa-tao (shared humanity), and malasakit (profound care) function as resources that shape how students interpret and engage with global issues. These findings suggest that engagement in GCE is not only multidimensional but also culturally embedded. The study contributes to the literature by extending existing student engagement frameworks to incorporate cultural context as a core dimension. Practically, it highlights the need for culturally responsive and experiential pedagogies to enhance meaningful engagement in higher education.
Volume: 15
Issue: 4
Page: 2919-2930
Publish at: 2026-08-01
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