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30,938 Article Results

VisionEyeNet: a customized deep learning framework for early diagnosis of keratitis and uveitis

10.11591/ijai.v15.i3.pp2709-2722
Somashekhar Bannur Mayigowda , Raghavendra Kodandarama , Sudhamani Mallaiah , Manjunath Naganna , Jamuna Jamuna , Kiran Kumar B. S.
Keratitis and uveitis are increasingly prevalent ocular disorders, often linked to delayed detection and limited specialist access, particularly in rural healthcare settings. These diseases can lead to severe visual impairment or irreversible blindness if not identified at an early stage. Traditional diagnostic approaches are manual, time-consuming, and prone to human error, making them challenging for large-scale screening. To address these limitations, this study presents VisionEyeNet, a framework for automatic classification of keratitis and uveitis. VisionEyeNet integrates MobileNetV2 and DenseNet121 within a fusion architecture, along with image enhancement methods such as adaptive gamma correction and specular reflection suppression. The model was trained and evaluated on a curated dataset of 1,860 slit-lamp images (960 uveitis and 900 keratitis) using a patient-wise split (71.5% training, 8.4% validation, and 20% testing). On the independent test set, it achieved 98.0% accuracy (95% CI: 97.1–98.8%) with balanced performance across classes. Inference analysis showed an average processing time of 51±2 ms per image, supporting real-time use. These results indicate that VisionEyeNet has strong potential as a clinically useful decision-support tool, particularly in resource-limited settings.
Volume: 15
Issue: 3
Page: 2709-2722
Publish at: 2026-06-01

Predictive modeling for crop suitability and productivity using machine learning techniques

10.11591/ijai.v15.i3.pp2533-2542
Gulaganjihalli Ningegowda Shwetha , Bhat Geetalaxmi Jairam
With the increasing global population and rising food demand, improving agricultural productivity through data-driven decision support systems has become essential. This study proposes a cross-validated meta-stacking ensemble framework for multi-class crop suitability prediction using soil nutrient and environmental parameters. The dataset consists of 2,200 samples covering 22 crop types and seven predictor variables, including nitrogen (N), phosphorus (P), potassium (K), temperature, humidity, pH, and rainfall. Six machine learning (ML) models—random forest (RF), decision tree (DT), light gradient boosting machine (LightGBM), extreme gradient boosting (XGBoost), support vector classifier (SVC), and k-nearest neighbors (KNN)—were trained and optimized using RandomizedSearchCV with k-fold cross-validation. A stacked ensemble model was then developed to combine heterogeneous learners and improve predictive robustness. Experimental results demonstrate that the RF model achieved an accuracy of 99.36%, while the proposed cross-validated meta-stacking ensemble achieved comparable performance with improved generalization stability. Precision, recall, and F1-score values of 0.99 indicate consistent classification across all crop classes. Feature importance analysis revealed N, K, and rainfall as the most influential predictors. Model robustness was evaluated using cross-validation and an independent test split to minimize overfitting risk. The findings highlight the effectiveness of ensemble learning for sustainable recommendation systems.
Volume: 15
Issue: 3
Page: 2533-2542
Publish at: 2026-06-01

Machine learning approaches for anomaly detection of Jakarta air quality index

10.11591/ijai.v15.i3.pp2543-2553
Muhammad Rizky Nurhambali , Yenni Angraini , Anwar Fitrianto
Anomalies in time series data are observations that deviate markedly from surrounding values or overall patterns. Air quality index (AQI) data, which vary over time, provide a suitable context for anomaly detection. Time series anomaly detection can be done with machine learning approaches like long short-term memory (LSTM) and extreme gradient boosting (XGBoost). These methods have advantages over conventional methods in handling nonlinearity and large data dimensions. This study compares LSTM and XGBoost methods for detecting anomalies in Jakarta's hourly AQI data. The dataset was obtained from the AirNow website and covers the period from January 1, 2018, to December 31, 2023. Anomalies in the observed data were labeled using moving range (MR) (2) and (3) approaches with three and four-sigma thresholds, and feature engineering (FE) was applied to improve model performance. The results indicate that LSTM is more suitable than XGBoost for forecasting and classification tasks in AQI data. LSTM achieved an average mean absolute percentage error (MAPE) of 10.3840%, a root mean square error (RMSE) of 10.5913, and a balanced accuracy (BACC) of 0.9424 under MR (2) labeling with the four-sigma rule. The anomalies detected mostly occurred between 21:00 and 09:00 and during the rainy season.
Volume: 15
Issue: 3
Page: 2543-2553
Publish at: 2026-06-01

Architectural design of an internet of things-based framework for road bike speed optimization

10.11591/ijai.v15.i3.pp2125-2140
Tigor Hamonangan Nasution , Opim Salim Sitompul , Fahmi Fahmi , Muhammad Anggia Muchtar
This research aims to develop an internet of things (IoT) system framework to predict cyclists’ optimal speed in road cycling using multisensor data and machine learning. The primary issue raised is the lack of an intelligent system capable of integrating physiological, performance, and environmental data in real-time speeds for cyclists. The designed framework consists of four functional layers: data acquisition layer; data processing and feature layer; predictive modeling layer; and recommendations and output layer. Modeling is carried out using gradient boosting regression (GBR), performed end-to-end with validation on real cyclist activity data. The test results demonstrate that the system can provide precise optimal speed estimates and offer pacing zone recommendations that positively impact athlete performance strategies. This research contributes novelty in the form of an adaptive multivariate prediction approach and a modular IoT architecture design that can be implemented on cloud and edge platforms.
Volume: 15
Issue: 3
Page: 2125-2140
Publish at: 2026-06-01

Teachers’ perceptions on continuous professional development programs in international schools in Klang Valley

10.11591/ijere.v15i3.31799
Edward Devadason , Babu Vengadasalam , Ng Yu Jin
This study explores teachers’ perceptions of continuous professional development (CPD) initiatives in international schools within the vibrant educational landscape of Klang Valley. Through qualitative research methods, including interviews and observations, this paper investigates the factors influencing teachers’ engagement and the impact of CPD on teaching quality. The study involves nine teachers and two principals, offering diverse perspectives on their CPD experiences. Findings reveal that CPD programs are generally well-received, contributing to teachers’ ongoing growth, enhanced teaching skills, and improved classroom management in diverse settings. Key factors affecting CPD participation include time constraints and workload, program relevance, leadership support, financial limitations, and mismatched group dynamics, with time constraints and workload being particularly significant. Observations during CPD sessions demonstrate active teacher involvement. This research enriches existing knowledge by providing a comprehensive understanding of teacher perceptions of CPD in Klang Valley’s international schools. This study highlighted that the provision of CPD opportunities should be proposed and provided with teachers’ professional needs taken into consideration, and leadership should ensure these needs are met for maximum effectiveness. These findings have great implications for policymakers, school administrators, and educators with an interest in developing better CPD practices in international settings.
Volume: 15
Issue: 3
Page: 2169-2182
Publish at: 2026-06-01

Artificial intelligence literacy and adoption among basic education teachers

10.11591/ijere.v15i3.36999
Trixie E. Cubillas , Neunna Vinzie D Dela Cruz , Gwyneth Queen F Galvadores , Jileen May B Olivares , Ariel U. Cubillas
Despite growing interest in artificial intelligence (AI) integration, a gap in AI literacy and adoption among teachers limits the benefits of AI-enhanced learning and widens the digital divide. This study explored AI literacy and adoption among basic education teachers in Butuan City, Philippines, using the technological pedagogical content knowledge (TPACK) framework and social cognitive theory (SCT). It examined factors influencing readiness to integrate AI tools into teaching. Using a quantitative descriptive-causal design, data from 243 randomly selected teachers were analyzed through structural equation modeling (SEM) with the adopted research instruments. Results show that AI literacy and positive affective-cognitive variables strongly predict AI adoption, with behavioral intention (BI) mediating the link between self-esteem (SE) and AI literacy. Findings underscore the need for targeted professional development and institutional support to bridge the AI literacy gap and ensure the responsible and effective integration of AI in primary education.
Volume: 15
Issue: 3
Page: 1986-2000
Publish at: 2026-06-01

Threat appraisal and prevention of risky sexual behavior among high school students in Indonesian: the mediating roles of response efficacy and self-efficacy

10.11591/ijere.v15i3.38765
Erni Gustina , Ira Nurmala , Nunik Puspitasari
Premarital sexual behavior among adolescents remains a public health concern. However, school programs often focus on risk perception without showing how perceived threat can lead to protection. This study examined the influence of threat appraisal (severity and vulnerability) on the prevention of premarital sexual behavior through response efficacy and self-efficacy as mediators. A cross-sectional survey was conducted among 333 high school students selected by multistage sampling. Likert-scale measures assessed severity, vulnerability, response efficacy, and self-efficacy. The model was analyzed using partial least squares structural equation modeling (PLS-SEM) with bootstrapping (α=0.05). Most participants reported no premarital sexual behavior (66.1%). Severity and vulnerability positively predicted response efficacy and self-efficacy, with severity exerting a stronger effect. Response efficacy and self-efficacy were negatively linked to premarital sexual behavior (p<0.05), meaning that higher coping appraisal (the ability to handle risky situations) was associated to lower risk behavior. Indirect effects from severity and vulnerability to behavior through both mediators were significant. The model explained 34% of the variance in premarital sexual behavior. Threat appraisal reduced premarital sexual behavior mostly by improving coping appraisal. Thus, school-based interventions should combine risk communication with the development of refusal, negotiation, and self-confidence skills to support protection.
Volume: 15
Issue: 3
Page: 2390-2398
Publish at: 2026-06-01

Psychometric validation of the humor styles questionnaire among Indonesian pre-service teachers

10.11591/ijere.v15i3.38732
Ali Rachman , Noorhapizah Noorhapizah , Yogi Prihandoko , Nahdia Fitri Rahmaniah
This study aimed to develop and validate the Indonesian version of the humor styles questionnaire (HSQ-ID) for use in pre-service teacher education. A cross-sectional psychometric design was applied to a sample of 729 Indonesian pre-service teachers, using systematic translation, content validation, exploratory factor analysis (EFA), and confirmatory factor analysis (CFA) with the robust maximum likelihood (ML) estimator. HSQ-ID showed a stable four-factor structure, strong model fit (comparative fit index (CFI)=0.97, root mean square error of approximation (RMSEA)=0.045, standardized root mean square residual (SRMR)=0.040), and acceptable internal consistency across all subscales (Cronbach α=0.72–0.89). One-way analysis of variance (ANOVA) indicated significant differences in humor styles across 10 teacher specialization fields, suggesting that humor use is shaped by disciplinary and professional training contexts. These findings confirm that the HSQ-ID is a valid and reliable instrument for evaluating humor styles in Indonesian teacher education and can support future assessment-based pedagogical interventions.
Volume: 15
Issue: 3
Page: 2111-2120
Publish at: 2026-06-01

Writing challenges and support for elementary students: facial emotions study

10.11591/ijere.v15i3.33688
Nguyen Thi Xuan Yen , Nguyen-Bich-Thy Bui , Thien-Vu Giang
Writing is one of the first basic skills that promote successful learning and mental health of elementary students. The 2018 Vietnamese curriculum reform has created challenges in the formation and practice of writing skills of lower elementary students. The primary research questions address: i) the key cognitive challenges in students’ writing performance; ii) the emotional experiences associated with writing tasks; and iii) the instructional strategies employed to enhance writing skills. Using a mixed-methods study design on 159 students and 12 teachers, through writing tests, facial action coding system (FACS) and semi-structured interviews, we recorded important insights. The findings showed that second-grade students demonstrated a higher significant advancement in writing. First-grade students mainly exhibit positive emotions with writing tasks. In contrast, second-grade students experience a higher prevalence of negative emotions. This shift suggests that as academic expectations increase, students have greater stress and emotional challenges, necessitating supportive interventions. This study’s findings can contribute to the national curriculum development, guide effective teaching practices, and contribute to wider discussions on educational reform within the Vietnamese context.
Volume: 15
Issue: 3
Page: 2659-2667
Publish at: 2026-06-01

Instructional and learning needs in evolution education: a multi-stakeholder needs assessment

10.11591/ijere.v15i3.38972
Vanjoreeh A. Madale , Monera A. Salic-Hairulla
Evolution is a foundational concept in biology, yet it remains challenging for learners and teachers alike. This study conducted a descriptive convergent mixed-methods needs assessment to examine instructional and learning needs in evolution education from the perspectives of students, teachers, and administrators. A total of 50 participants (35 students, 10 teachers, and 5 administrators) from a public senior high school completed researcher-developed questionnaires containing Likert-scale and open-ended items. Quantitative data were analyzed using descriptive statistics, while qualitative responses underwent thematic analysis, with integration occurring at interpretation. Findings indicate that students possess moderate conceptual understanding but struggle with abstract processes such as natural selection, long-term species change, and evidence interpretation, alongside gaps in science process skills. Teachers report difficulties addressing misconceptions, integrating inquiry-based practices, and accessing contextualized instructional materials. Administrators emphasize the need for laboratory resources, curriculum-aligned materials, and sustained professional development. Overall, results highlight that strengthening evolution education requires coordinated strategies that integrate structured science process skills, targeted teacher training, and institutional support systems. The study provides a multi-stakeholder framework to guide evidence-based instructional and systemic improvement in evolution education.
Volume: 15
Issue: 3
Page: 2062-2072
Publish at: 2026-06-01

Bug safari: promoting ecological awareness in early childhood through nature-based learning

10.11591/ijere.v15i3.38526
Kazım Biber , Caner Börekci
This study examines the effectiveness of a nature-based educational program called bug safari, designed to enhance preschool children’s attitudes toward small creatures, particularly bugs, and to foster their ecological awareness. Developed within the framework of the Reggio Emilia approach, the program integrates multi-sensory and interdisciplinary learning methods, including observation, drama, storytelling, art activities, and parental involvement. The study was conducted in two preschool classrooms in Balıkesir, Türkiye. In the experimental group, bug safari activities were implemented once a week for six weeks, while the control group continued with the existing preschool curriculum. Data were collected using the 22-item bug awareness and ecological awareness questionnaire, developed by the researcher, and administered as both a pre-test and post-test. A mixed-design analysis of variance (ANOVA) revealed that the experimental group showed statistically significant improvements in bug awareness, understanding of the role of bugs in the ecosystem, and ecological consciousness, whereas no significant changes were observed in the control group. The findings indicated that nature-based programs involving direct experiences and active participation effectively promoted positive environmental attitudes and ecological awareness in early childhood. This study underscores the importance of integrating child centered, experiential, and nature-oriented approaches into preschool education to support cognitive, emotional, and behavioral development.
Volume: 15
Issue: 3
Page: 2217-2227
Publish at: 2026-06-01

Narrative comprehension in 5-year-old Vietnamese-speaking children using the multilingual assessment instrument for narratives

10.11591/ijere.v15i3.38932
Nguyen Thi Hoang Yen , Ben Phạm , Hang Pham , Van Pham , Phuong Nguyen , Phuong Bui
This study addresses how elicitation modes and socio-demographic factors influence narrative macrostructure understanding in 5-year-old Vietnamese-speaking children. A convenience sampling of 311 typically developing children were assessed using the multilingual assessment instrument for narratives (MAIN). Narrative comprehension was evaluated through 10 standardized questions for both retelling (cat story) and storytelling (baby goats story) modes. Findings revealed a significant advantage for retelling (M=7.21, SD=2.31) over storytelling (M=5.73, SD=2.50; p<.001), highlighting the role of linguistic scaffolding. While children mastered identifying character goals, challenges remained in explaining internal states and making causal inferences. Narrative comprehension was independent of gender, location, and general communication skills, but significantly influenced by maternal education level (p<.05). Vietnamese narrative development follows universal patterns, yet deep comprehension is shaped by specific environmental inputs. This study establishes a normative baseline for narrative skills within the Vietnamese preschool curriculum and provides a validated tool for speech and language therapists to facilitate early identification and targeted interventions.
Volume: 15
Issue: 3
Page: 1908-1918
Publish at: 2026-06-01

Student satisfaction in student affairs management: the role of cross-functional cooperation in Hainan, China

10.11591/ijere.v15i3.39033
Erlin Tian , Supot Rattanapun
Student affairs management (SAM) is increasingly expected to deliver timely, coherent, and student-centered services, yet satisfaction remains uneven in Chinese universities because students experience SAM as an integrated system rather than isolated units. This study asks whether cross-functional cooperation (CFC) explains how service quality, service gaps, and students’ psychological and engagement factors translate into satisfaction with SAM in Hainan Province, China. Using a cross-sectional survey of 250 undergraduate and postgraduate students from ten public and private universities, the study applies partial least squares–structural equation modeling (PLS-SEM) with 5,000-sample bootstrapping to test direct and mediating effects. Results show that CFC is the strongest predictor of satisfaction (β=0.419, p<0.001). Service gaps reduce satisfaction (β=−0.217, p<0.001), while psychological and engagement factors increase satisfaction (β=0.206, p<0.001). Service quality has no direct effect but operates through CFC, indicating that coordination is required to convert service inputs into positive experiences. The findings highlight governance reforms that institutionalize cross-department coordination, shared case management, and gap monitoring to improve SAM effectiveness under Hainan’s reform context.
Volume: 15
Issue: 3
Page: 1963-1970
Publish at: 2026-06-01

Practices and strategies of informal assessments on grammar rules among second language learners

10.11591/ijere.v15i3.37717
Jason V. Chavez , Rolly G. Salveleon , Ma. Theresa L. Eustaquio , Haydee G. Adalia , Ma. Pilar T. Rosaldo , Joseph B. Quinto , Salita D. Dimzon , Sar-Ana M. Abdurasul , Rasmil T. Abdurasul , Ivy M. Nazareth
While informal assessment offers authentic insights into second language (L2) grammar acquisition, the specific strategies and implementation challenges remain underexplored. This study investigated the practices employed by L2 educators in conducting informal grammar assessments and the obstacles they encounter. Using a qualitative exploratory design, 20 purposively selected language educators from diverse linguistic regions in the Philippines participated in semi-structured interviews. Data were analyzed using reflexive thematic analysis. The findings revealed a pedagogical shift from static testing to stealth monitoring, characterized by contextualized micro-checks, gamified strategies to lower affective filters, and peer-scaffolded evaluation. However, significant challenges emerged, specifically the tension between assessment validity and reliability, as well as cognitive overload due to the dual burden of instruction and real-time data recording. The study concluded that while educators prioritize the authenticity of low-stakes assessment, effective implementation requires enhanced assessment literacy and structural support to mitigate subjectivity and operational fatigue.
Volume: 15
Issue: 3
Page: 2379-2389
Publish at: 2026-06-01

Instructional scaffolding in dialogue-based programming tutoring

10.11591/ijere.v15i3.38919
Julieto Perez , January Naga , Salma Naga-Marohombsar
This study examines how instructional scaffolding is enacted in dialogue-based artificial intelligence (AI) tutoring systems for programming education and evaluates the levels of cognitive demand they support. While AI tutors can guide novice learners through programming tasks, it remains unclear whether they promote meaningful higher-order thinking or primarily support procedural task completion. Using a mixed-methods approach, 1,255 tutor utterances from 36 tutoring sessions were analyzed using a dual-layer coding framework grounded in instructional scaffolding theory and Bloom’s revised taxonomy. Results show that instructional support is concentrated at the understanding and applying levels, with prompting and explaining as dominant strategies. Higher-order cognitive scaffolding (analyzing, evaluating, creating) was rare or absent. Sequential patterns revealed repetitive prompting–explaining cycles with limited scaffold progression. These findings indicate that AI tutoring effectively supports foundational learning but lacks mechanisms for deeper cognitive engagement. This study highlights the need for pedagogically informed AI tutor design and provides actionable insights for educators and system developers to integrate AI tools in ways that promote higher-order thinking and independent problem-solving.
Volume: 15
Issue: 3
Page: 2478-2486
Publish at: 2026-06-01
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