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A move analysis of the discussion sections in English as a second language learners’ quantitative theses

10.11591/ijere.v15i1.34624
Mary Joy V. Herediano , Riziel E. Secretario , Arnold M. Sumpo , Ivy F. Amante , Rovy M. Banguis , Gay Emelyn L. Lariosa , Norhanie D. Macarao
Discussion section of research papers is one of the most essential sections because the authors demonstrate the knowledge contribution of their research findings to the existing literature. In this study, 16 quantitative theses analytical components written by the English language learners were gathered and analyzed. By utilizing qualitative research design focusing on move analysis, the researchers found out that Move 1 (background information), Move 2 (reporting results), Move 3 (summarizing the results), and Move 4 (commenting on the results) were identified as obligatory moves since they serve as the primary objectives of this explanatory segment. Move 6 (evaluating methodology) was recognized as a traditional move. Move 5 (summarizing the study) and Move 7 (deductions from the research) were noted as optional moves. Distinct linguistic characteristics and verbal signals were observed in the various moves, with the patterns of these steps identified as a structured arrangement in the results discussion. The results aim to help student writers recognize the rhetorical frameworks that should be included in the interpretive sections of quantitative theses.
Volume: 15
Issue: 1
Page: 740-750
Publish at: 2026-02-01

From algorithms to classrooms: a decade of artificial intelligence in education research

10.11591/ijere.v15i1.34427
Lim Seong Pek , Nahdatul Akma Ahmad , Faiz Zulkifli , Rabindra Dev Prasad , Ari Muzakir , Jun S. Camara
The education industry has seen a substantial transformation thanks to artificial intelligence (AI), which has improved administrative effectiveness, accessibility, and individualized learning. However, issues like moral dilemmas, digital justice, and policy inconsistencies still exist. From 2015 to 2024, this bibliometric research explores how AI is revolutionizing education. Personalized learning, improved accessibility, and expedited administrative procedures have all been made possible by AI; yet, issues with cost, digital equity, and ethics still exist. We used the Web of Science (WoS) database to conduct a comprehensive bibliometric analysis of 291 peer-reviewed articles that were indexed in the Social Sciences Citation Index (SSCI). The PRISMA methodology was used in the study to find and filter pertinent material. Thematic trends, citation patterns, and co-authorship networks were examined using bibliometric tools like VOSviewer. The progress of generative AI tools like ChatGPT, the importance of AI in democratizing education, and the integration of AI into curriculum building are some of the key discoveries. The report identifies significant nations, organizations, and researchers in AI education and emphasizes global research relationships. Our research raises ethical governance issues while shedding light on AI’s potential to promote individualized learning and increase student engagement. These findings support sustainable development goal (SDG) 4 on quality education by highlighting the need for responsible AI use to address the digital divide. This paper offers useful suggestions for academics, educators, and legislators to maximize AI’s promise while tackling its drawbacks.
Volume: 15
Issue: 1
Page: 500-510
Publish at: 2026-02-01

Voices of Chinese private college EFL students: unveiling preferences in written corrective feedback

10.11591/ijere.v15i1.35595
Mudan Cai , Joseph Ramanair
Despite the growing body of research on the impact of written corrective feedback (WCF) on intermediate and upper-level students in Chinese public universities, limited attention has been paid to the preferences of lower- and intermediate-level English as a foreign language (EFL) students in Chinese private colleges regarding various types of WCF. To address this gap, the current study examined students’ experiences with and preferences for WCF. A quantitative survey design was adopted, with data collected from 30 EFL students at a private college in Mainland China using an online self-administered questionnaire. The results showed: i) students’ preferences for WCF types were consistent with their feedback experiences; ii) students most commonly received direct feedback focused on content, while spelling gained the least feedback; and iii) students most preferred direct feedback on content and grammar, while indirect feedback on organization and spelling was least favored. The findings suggest that WCF remains valuable for EFL learners and their English as a second language (ESL) counterparts. It is recommended that EFL writing teachers in similar contexts tailor their feedback practices by prioritizing students’ preferences, particularly by emphasizing direct feedback on content and grammar, to boost engagement and learning outcomes.
Volume: 15
Issue: 1
Page: 933-942
Publish at: 2026-02-01

Development of the Arabic family relation test among school students

10.11591/ijere.v15i1.33469
Marwa Nasser Alrajhi , Kamisah Osman , Hussain Ali Alkharusi
Family relationships are highly valued; therefore, their assessment is essential. Documented literature has emphasized the significance of family relationships in students’ different outcomes. However, there is a lack of psychometrically robust measures of family relationships in Arabic-speaking environments. This study aimed to develop the Arabic family relation test (AFRT) and examine its psychometric properties. The test explores six main dimensions of parent-child relationships: restrictiveness, affection, vulnerability, justice, acknowledgment, and trust. The cross-sectional quantitative design was utilized. The study sample included 601 school students (47.6% males) from grades 5, 7, 9, and 11, selected through a cluster-random sampling approach. The findings revealed a valid 60-item AFRT with a two-second-order-factor, six-first-order-factor factorial structure. Measurement invariance of the AFRT was achieved in the father-child model across groups of age and gender. However, measurement invariance was only supported across age groups in the mother-child model. The AFRT demonstrated good reliability, as indicated by Cronbach’s alpha, test-retest, split-half, and composite reliability. The researchers concluded that the AFRT is an appropriate tool for family assessment; however, further psychometric investigation is recommended. In addition, recommendations for implications and suggestions for future research were provided.
Volume: 15
Issue: 1
Page: 915-932
Publish at: 2026-02-01

Predictors of teachers’ readiness for inclusive education in Kazakhstan

10.11591/ijere.v15i1.36260
Dinara Ospankulova , Akbota Autayeva , Zhanna Paylozyan , Akmaral Rsaldinova , Aigul Baitursynova
Inclusive education (IE) is increasingly recognized as a key priority in modern educational systems; however, in Kazakhstan, there is limited evidence on the factors influencing teachers’ attitudes and readiness to implement it. This study explores public school teachers’ attitudes toward inclusive education (TATIE) and examines how personal, professional, and institutional factors affect these attitudes. A survey of 638 teachers from Almaty schools was conducted using a validated instrument, and correlation and regression analyses were employed to identify significant predictors. The results indicate that gender, teaching experience (TE), frequency of contact with students with disabilities (SWD), perceived school support, and participation in specialized training significantly influence teachers’ attitudes. Positive attitudes were particularly associated with direct professional experience and strong institutional support, highlighting the importance of targeted professional development and school-level measures. This study contributes to the literature by providing a comprehensive quantitative analysis specific to the Kazakhstani context and offers practical insights to guide policy and enhance the effective implementation of inclusive practices, ultimately improving the quality of education for students with special educational needs.
Volume: 15
Issue: 1
Page: 587-596
Publish at: 2026-02-01

Bridging resilience and mathematics success: the mediating role of teacher emotional support

10.11591/ijere.v15i1.35455
Irene O. Mamites , Jhamm M. Olila , Wendy Q. Aliviano , Gley S. Arañas , Quenie R. Abesia , Emerson D. Peteros
This study investigated the mediating role of teacher emotional support (TES) in the relationship between academic resilience (AR) and mathematics performance (MP) among 563 randomly selected Grade 7 students from public secondary schools in Mandaue City, Cebu, Philippines. Employing a descriptive correlational design, data were collected through an adopted questionnaire—academic resilience scale (ARS-30), emotional support scale, and the students’ first quarter mathematics grade. The data collected were analyzed using frequency, percentage, weighted mean (WM), standard deviation (SD), Pearson r, and multiple regression analysis. Findings indicated high AR, very high TES, and satisfactory MP. Mediation analysis revealed that TES partially mediates the positive association between AR and MP. This analysis offers valuable guidance for educators, researchers, and policymakers in creating supportive learning environments that build AR and enhance TES, leading to better MP.
Volume: 15
Issue: 1
Page: 89-101
Publish at: 2026-02-01

Enhancing academic performance prediction in online learning through hybrid machine learning models

10.11591/ijere.v15i1.33590
Jamal Eddine Rafiq , Zakrani Abdelali , Mohammed Amraouy , Said Nouh
Faced with the rise of online learning platforms, predicting learners’ academic performance has become a major concern to personalize and enhance educational journeys. However, traditional predictive models struggle to effectively integrate emotional and social factors. This article introduces a hybrid predictive model that combines random forests (RF) for selecting the most relevant features and multiple regression (MR) to forecast academic performance. The data is sourced from three online learning platforms and encompasses both implicit traces (learner interactions and behaviors) and explicit traces (demographic characteristics). Following a selection and merging process, the final dataset comprises 1,003,392 records and 42 features, categorized into six types of indicators: cognitive, emotional, social, normative, contextual, and demographic. The results demonstrate that this hybrid model outperforms traditional approaches and other machine learning (ML) techniques in terms of predictive accuracy, achieving an R² of 0.9372 and a root mean square error (RMSE) of 0.1022. The incorporation of explicit and implicit traces helps better capture the intricate interactions among the different data dimensions, significantly enhancing prediction quality. This work represents a notable advancement in the field of academic performance prediction. It also sheds light on challenges associated with the increasing complexity of models, paving the way for future research to develop more generalizable approaches.
Volume: 15
Issue: 1
Page: 436-447
Publish at: 2026-02-01

Analysis of factors in integrated internship models for preservice Islamic education teachers using exploratory factor analysis

10.11591/ijere.v15i1.35767
Karwadi Karwadi , Abd Razak Zakaria , Adhi Setiawan , Moh. Ferdi Hasan
This research identifies key success factors of integrated internship models for prospective Islamic Religious Education (PAI) teachers using exploratory factor analysis (EFA), addressing critical gaps where empirical evidence in religious teacher preparation remains limited. Analyzing 218 PAI students across four Yogyakarta universities through mixed-methods design, EFA revealed a four-factor structure explaining 63.4% variance: observation competence (28.7%), microteaching (13.8%), teaching practice (11.2%), and spiritual reflection (9.7%). The identification of spiritual reflection as an independent factor represents a novel contribution not documented in international teacher education literature, empirically validating integration of spiritual competencies within professional preparation frameworks. The internship component assessment scale (ICAS) demonstrates strong psychometric properties (CVI=0.87, α=0.84), providing the first culturally responsive instrument for Islamic education contexts. This study proposes the integrated internship spiral model (IISM) emphasizing cyclical reinforcement rather than linear progression, challenging conventional designs. Educational implications include redesigning PAI teacher professional education curriculum with proportional resource allocation, implementing mentor training for assessing spiritual-pedagogical dimensions, and embedding technology integration across internship phases. Future research should pursue longitudinal validation, cross-contextual studies in other religious education settings, instrument development strengthening spiritual factor reliability, and comparative effectiveness studies. This research demonstrates that culturally responsive teacher preparation can honor religious authenticity while advancing professional excellence, contributing to holistic transformation of PAI internship programs with potential global application.
Volume: 15
Issue: 1
Page: 342-359
Publish at: 2026-02-01

RWT-Net: A hybrid ResNet-wavelet-transformer for early detection of left ventricular hypertrophy

10.12928/telkomnika.v24i1.27445
Hoang; The University of Da Nang Huu To Nguyen , Phuong Huu Nghia; San Jacinto College Le , Lam; The University of Da Nang Mai , Nguyen Pham Ho; FPT University Trong
Early detection of left ventricular hypertrophy (LVH), a key predictor of heart failure and stroke, is critical. However, standard 12-lead electrocardiogram (ECG) criteria suffer from low sensitivity. While deep learning shows promise, a research gap exists for models that robustly integrate diverse signal fea tures to improve detection, especially sensitivity. We propose ResNet-wavelet transformer net (RWT-Net), a hybrid architecture that fuses deep morpholog ical features from a ResNet1D with statistical time-frequency features from a wavelet packet transform (WPT) using a transformer encoder. The model was evaluated on the PTB-XL dataset (11,201 recordings) using a stringent, patient level 5-fold cross-validation. RWT-Net achieved a mean area under the curve (AUC)of0.9868andF1-scoreof0.8725. Critically, its wavelet-enhanced stream yielded significantly higher sensitivity compared to a ResNet-transformer base line (0.8964 vs. 0.8716, p=0.0039), better addressing the clinical need to mini mize false negatives. A key limitation is the reliance on ECG-based labels, not an echocardiography gold standard. RWT-Net demonstrates potential as a re liable, automated screening tool to prioritize at-risk patients for further clinical assessment.
Volume: 24
Issue: 1
Page: 142-150
Publish at: 2026-02-01

Assessing the impact of ChatGPT in education: perspectives from Vietnamese educators

10.11591/ijere.v15i1.33610
Nguyen Thi Thu Thuy , Nguyen Thi Kim Lien , Phan Thi Van , Hoang Phuong Lam , Nguyen Le Thanh Truc
ChatGPT is an AI-driven conversational model offering significant benefits for personalized learning, such as adaptability to individual learning styles and increased engagement. While prior research has largely explored student use, limited attention has been given to educators’ perspectives particularly in Vietnam, where access to ChatGPT remains inconsistent. This study examines how perceived information quality (IQ), system quality (SQ), and service quality (SRQ) influence Vietnamese educators’ satisfaction (SF) and intention to use (IU) ChatGPT for academic purposes. Data were gathered through an online survey of 96 educators from five public universities in southern Vietnam and analyzed using partial least squares structural equation modeling (PLS-SEM). Results show that all three quality dimensions significantly affect user SF, but only SQ directly impacts the IU. SF mediates the effects of information and SRQ on usage intentions, emphasizing the importance of user experience in fostering adoption. Policy recommendations include enhancing equitable access to AI tools, promoting AI literacy among educators, and conducting ongoing evaluations to support sustainable implementation. The study highlights how spatial and contextual factors shape AI adoption in education, offering practical insights for integrating ChatGPT and similar tools, especially in under-resourced or geographically remote educational settings.
Volume: 15
Issue: 1
Page: 136-145
Publish at: 2026-02-01

The difficulties that beset senior high school students in research writing and presentations

10.11591/ijere.v15i1.35349
Joseph B. Quinto , Girlie C. Jimenez , Illuminada Simbulan Menzi
Writing and presenting research are endeavors that senior high school students have to hurdle in the Philippines. Therefore, this research aimed at unravelling the dimensions of the difficulties faced by senior high school students in their research writing and research presentations through a sequential qualitative mixed-methods design. Primary data was gathered through a qualitative survey of 448 students, while secondary data was obtained via 10 focus groups with 7-10 members representing the different strands in one private senior high school in Baguio City, Philippines. All these participants were selected using combined quota and purposive sampling. Thematic analysis coupled with an iterative procedure was utilized to treat the harvested qualitative data. Findings revealed that the dimensions of the difficulties faced by student-researchers in research writing are health-related, manuscript-related, people-related, and resources-related, while the dimensions in research presentations are inner-directed and outer-directed. This research adds to the current body of knowledge, can contribute to achieving the United Nations sustainable development goal 4.5 and 4.7 on equal education access and global citizenship skills, and recommends research trajectories.
Volume: 15
Issue: 1
Page: 387-398
Publish at: 2026-02-01

The role of entrepreneurship education in shaping self-employment intentions: a TPB-based study of Malaysian TVET students

10.11591/ijere.v15i1.35348
Isma Addi Jumbri , Eleeza F. Natasya Khairul Herman , Fauzan Fauzan , Mulyani Karmagatri , Sandy Setiawan , Dian Kurnianingrum
Entrepreneurship education prepares technical and vocational education and training (TVET) students with the competencies and entrepreneurial mindset required for future business endeavors. Guided by the theory of planned behavior (TPB), this study examines how such education shapes students’ self-employment intentions (SEI). A quantitative survey was administered to 300 undergraduates at Universiti Teknikal Malaysia Melaka (UTeM), and the data were analyzed using correlation and regression methods. The results show that entrepreneurial intention is positively linked to attitude toward behavior (r=0.474), entrepreneurship education (r=0.416), subjective norms (r=0.374), and perceived behavioral control (r=0.346), with attitude identified as the most influential predictor, accounting for 22.4% of the variance. These outcomes emphasize the centrality of individual motivation and the enabling role of entrepreneurship education in cultivating entrepreneurial aspirations. The study highlights the strategic importance of embedding stronger entrepreneurship curricula in TVET institutions to enhance self-employment readiness and support national goals for workforce development and economic resilience. The findings resonate with Malaysia’s National Entrepreneurship Policy 2030 and the TVET empowerment agenda, pointing to the value of initiatives such as campus incubators, seed funding, and mentorship in transforming entrepreneurial intention into tangible venture creation.
Volume: 15
Issue: 1
Page: 126-135
Publish at: 2026-02-01

System identification of batch milk cooling using output error models

10.12928/telkomnika.v24i1.27469
Rudy; University of Surabaya Agustriyanto , Aloisiyus; University of Surabaya Yuli Widianto , Edy; University of Surabaya Purwanto , Puguh; University of Surabaya Setyopratomo
Accurate modelling of milk cooling dynamics is essential to maintain product quality and improve energy efficiency in small-scale dairy operations. This study aims to develop a dynamic model for a batch milk-cooling system used at Koperasi Unit Desa Sinau Andandani Ekonomi (KUD SAE) Pujon. Synthetic temperature data were generated under controlled perturbations reflecting actual process conditions, and the data were analysed using the output error (OE) identification method implemented in the MATLAB System Identification Toolbox. Several OE model structures were compared using statistical indicators, including the coefficient of determination (R²) and root mean square error (RMSE). The OE (2,2,1) model achieved the best performance with R² = 0.9923 and RMSE = 0.0600, accurately representing the first-order dynamics of the cooling process. The identified model provides a reliable foundation for process optimisation, controller design, and operator training in dairy systems. Although the validation is limited to simulated data, the proposed approach offers substantial potential for real-time implementation and can be extended to other temperature-sensitive food processes.
Volume: 24
Issue: 1
Page: 282-292
Publish at: 2026-02-01

Optimized edge-aware frequency-guided filtering for robust image denoising

10.12928/telkomnika.v24i1.27338
Iman; Karabuk University Elawady , Ahmet Reşit; Karabuk University Kavsaoğlu , İsmail Rakıp; Karabuk University Karaş
The problem of denoising intrusion is still of great concern in computational imaging because of the trade-off between noise reduction and image structure and details recovery. This paper proposes an optimized edge-aware fast adaptive guided filter (E-FAGF) combining wavelet-domain decomposition, edge-awareness, and lightweight deep learning for efficient and effective denoising. The biorthogonal wavelet transform is employed to decompose noisy images into low- and high-frequency sub bands and an improved edge-attention map for selective high-frequency denoising. Regularization parameters are estimated pixel-wise by a compact convolutional neural network (CNN), allowing spatial-varying filtering to be done with multi-scale processing. The resultant E-FAGF consistently outperforms the state of the art on this dataset: on BSD500 for speckle and Gaussian noise (peak signal-to-noise ratio (PSNR) of 39.63 dB and 33.97 dB, respectively), and competitive performance for Poisson noise (30.84 dB) a large margin compared to the reference bilateral and non-local means. Our method maintains high structural similarity (up to 0.97 in structural similarity index measure (SSIM)), runs at 0.015 seconds per 512×512 image on graphics processing unit (GPU), and can be applied without dataset specific training. These results suggest the possibility of E-FAGF to achieve a balance between classical efficiency and learning-based adaptability, thereby forming a new scenario to combine fast and reliable image restoration for actual scenarios.
Volume: 24
Issue: 1
Page: 219-227
Publish at: 2026-02-01

ETV: efficient text vision for text localization in natural scene images

10.11591/ijeecs.v41.i2.pp812-822
Suman Suman , Champa H. N.
In the current digital era, the extraction and comprehension of textual information from images have emerged as pivotal tasks. With the exponential growth of text documents, efficient processing and analysis have become imperative. However, text localization in images remains challenging due to complex backgrounds, uneven illumination, diverse text styles, and perspective distortions, rendering traditional optical character recognition (OCR) techniques inadequate. To address these challenges, this paper proposes an integrated method named efficient text vision (ETV). ETV combines the OCR capabilities of Tesseract with the efficient and accurate scene text detector (EAST) algorithm, supplemented by nonmaximum suppression (NMS). The Tesseract OCR component facilitates the extraction and identification of individual characters, while EAST excels in the efficient detection and localization of complete text sections. The incorporation of NMS enhances localization accuracy by eliminating redundant or overlapping bounding boxes.
Volume: 41
Issue: 2
Page: 812-822
Publish at: 2026-02-01
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