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30,907 Article Results

Improving students’ scientific argumentation through AI-supported feedback

10.11591/ijere.v15i4.38648
Joelash R. Honra , John Lorence A. Villamin
Scientific argumentation is a core practice in science education, yet many students struggle to construct arguments that effectively integrate claims, evidence, and reasoning. With the growing use of artificial intelligence (AI) in education, AI-supported feedback has emerged as a potential tool to scaffold students’ argumentation processes. This study examined the effects of AI-supported feedback on students’ scientific argumentation using a quasi-experimental, explanatory sequential mixed-methods design. Two intact groups participated: an experimental group receiving AI-supported formative feedback on written arguments and a control group receiving conventional teacher feedback. Quantitative data were collected using a validated rubric based on the claim–evidence–reasoning (CER) framework and Toulmin’s argument pattern (TAP), while qualitative data from student interviews and written responses provided contextual insights. Results showed that the experimental group achieved greater improvements in overall argumentation quality, particularly in evidence use and reasoning. Qualitative findings further indicated that AI feedback supported iterative revision and strengthened students’ understanding of evidence–claim relationships.
Volume: 15
Issue: 4
Page: 2741-2749
Publish at: 2026-08-01

Digital ecopedagogy-based counseling for cyberbullying prevention among vocational high school students

10.11591/ijere.v15i4.39314
Nina Permata Sari , Hendro Yulius Suryo Putro , Muhammad Andri Setiawan
Cyberbullying has become a growing concern among adolescents in vocational high schools, particularly in Indonesian contexts where conventional counseling often struggles to address online aggression effectively. This study evaluated the effectiveness of digital ecopedagogy-based counseling (DEBC), a structured digital counseling intervention that combines interactive online modules, ecological reflection tasks, peer mentoring, and counselor-guided discussions, in preventing cyberbullying among vocational high school students in South Kalimantan, Indonesia. A quasi-experimental design involved 180 students and six school counselors from three vocational schools, with an eight-week intervention for the experimental group and conventional face-to-face counseling for the control group. Data were collected through pre-test and post-test cyberbullying behavior scales, supported by interviews, focus group discussions, and observations. The experimental group showed significantly greater reductions in cyberbullying behavior (N-Gain=0.45–0.67, p
Volume: 15
Issue: 4
Page: 3497-3507
Publish at: 2026-08-01

Effects of digital learning intervention on pre-service teachers’ innovation competence: evidence from Kazakhstan

10.11591/ijere.v15i4.39363
Zhazira Stambekova , Aziya Zhumabayeva , Zhanna Zhussupova , Saule Zhorayeva
Developing innovation competence (IC) is essential for pre-service teachers, as it enables them to effectively design, implement, and adapt instruction in increasingly complex and digitally enriched learning environments where technology integration, student-centered approaches, and pedagogical flexibility are critical. However, empirical evidence on how structured digital learning interventions (DLIs) foster multidimensional IC remains limited. This study examined the effect of a twelve-week structured DLI on IC in 156 third-year pre-service teachers using a quasi-experimental pretest–posttest design with a non-equivalent control group (CG). Participants completed the IC scale before and after the intervention. Results showed that the intervention significantly improved overall IC and all four dimensions: creative problem-solving (CPS), pedagogical adaptability, proactive initiative, and technology-enhanced instructional design, with the largest gains in CPS and technology-enhanced instructional design. Engagement with digital learning activities further predicted competence development, particularly in the intervention group. These findings suggest that integrating structured digital learning with intentional pedagogical design can effectively enhance innovation-oriented competencies. Teacher education programs can apply these insights to design interventions that prepare future educators for innovative, technology-rich classrooms.
Volume: 15
Issue: 4
Page: 3241-3252
Publish at: 2026-08-01

A study on exploring the effects of the school climate and teacher’s accountability on academic performance of students in early childhood care and education

10.11591/ijere.v15i4.36631
Kalpana Nagar , G. S. Prakasha
This study investigates parental perceptions of school climate, teachers’ accountability, and students’ academic performance, as well as the relationship among these three factors. The descriptive research uses the demographic background of parents as a stratified sampling method to collect primary data. The statistical procedure shows that Pearson’s product-moment correlations between school climate and teachers’ accountability affect the academic achievement, with effect sizes of r=0.632, r=.646, which significantly correlate with each other. Regression analysis reveals the variability of the effect sizes of school climate and teachers’ accountability on academic achievement, with the values of β=0.070 and β=0.115. The independent variable, school climate and teachers’ accountability, explains 45.4% of the variability of academic performance of the students in early childhood care and education. The present study recommends that training teachers would link their accountability and measures to track the supportive climate of school to improve the student’s academic performance. Future research should conduct longitudinal studies to examine how relationships between school climates and teachers’ accountability affect different student populations and their academic performance.
Volume: 15
Issue: 4
Page: 2883-2890
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

Procrastination trap: how personality traits fuel generative artificial intelligence over-reliance and erode academic performance

10.11591/ijere.v15i4.39382
Mohamad Rizal Abdul Hamid , Chen Jung Ku , Anath Rau Krishnan , Imran Mehboob Shaikh , Yoke Lian Lau , Mohd Zulkifli Muhammad , Saiful Bahri
This study investigates how long-term personality traits contribute to generative artificial intelligence (GenAI) use and whether these traits have an impact on academic procrastination and performance. The Big five personality model was used for this investigation, and a quantitative methodology was employed to analyze data from 200 undergraduate students in East Malaysia via partial least squares structural equation modeling (PLS-SEM). Findings indicated that while neuroticism and openness were associated with increased levels of GenAI use, conscientiousness was found to be a protective factor against GenAI dependency. In addition, results showed that when students excessively utilize GenAI, they experience increased levels of procrastination which leads to longer procrastination periods and decreased levels of deep learning engagement. Finally, procrastination was identified as a partial mediator between GenAI use and poor academic performance. Thus, GenAI dependency produces a ‘competence illusion’, causing a decline in students’ academic abilities over time. Ultimately, this study supports the need for interventions designed to help students learn self-regulation and develop critical AI literacy skills to enable technology to serve as a cognitive scaffold as opposed to a replacement for students’ own cognitive efforts.
Volume: 15
Issue: 4
Page: 3362-3374
Publish at: 2026-08-01

Effect of PhET simulations and YouTube videos on polytechnic students’ conceptual understanding in fluid mechanics

10.11591/ijere.v15i4.37002
Jean D'Amour Iradukunda , Lakhan Lal Yadav
Fluid mechanics is an important branch of engineering and science with various technological and scientific applications. However, students often struggle to develop a solid conceptual understanding of related concepts due to their abstract nature, which involves invisible forces and complex scientific phenomena. This study investigated the effect of physics education technology (PhET) interactive simulations combined with educational YouTube videos on students’ conceptual understanding in ten areas of fluid mechanics. Using a quasi-experimental pre-test and post-test design, 168 construction technology students from two Rwanda Polytechnic (RP) colleges were assigned to the experimental group (n=81) and the control group (n=87). The experimental group got instruction using PhET interactive simulations and YouTube videos, while the control group was taught using traditional methods. A validated test assessed students’ conceptual understanding before and after the intervention. Descriptive and inferential statistics analyses of the study show that the students in the experimental group demonstrated far enhanced conceptual understanding in different areas of fluid mechanics than those in the control group. Results using different measures (normalized learning gains and effect sizes, using different approaches) show that the experimental group gained significantly greater improvement in their level of conceptual understanding compared to that of the control group. For example, Cohen’s d for the post-test scores for the two groups was found to be 1.85; ratios of Hake’s normalized learning gains and Cohen’s d for post- and pre-test for the experimental group to the control group were respectively 2.2669 and 2.1623. Based on these findings, the study provided actionable recommendations for educational policy and practice for polytechnic colleges.
Volume: 15
Issue: 4
Page: 3025-3037
Publish at: 2026-08-01

Developing a 2-DOF robotic arm kit for embodied AI learning in middle school technology education

10.11591/ijere.v15i4.39254
Dasol Kim , Sooin Kim
Artificial intelligence (AI) literacy is increasingly emphasized in technology education, yet many middle-school classroom activities remain screen-based and offer limited opportunities to experience the full AI pipeline in an authentic design context. To address this gap, this study developed a low-cost, classroom-ready 2-degree-of-freedom (2-DOF) robotic arm kit and an eight-lesson AI-integrated instructional unit in which students assembled the robot arm, collected and labeled image data, trained and improved a classification model, and applied model outputs to robot-arm control. A quasi-experimental pretest–posttest control-group design was employed with 98 ninth-grade students in four intact classes (experimental group, n=46; comparison group, n=52). Quantitative data were analyzed using ANCOVA with pretest scores as covariates, and qualitative data from student reflection reports were analyzed thematically. The experimental group significantly outperformed the comparison group on value of AI, efficacy of AI, AI literacy, and total scores. Qualitative findings further showed that students recognized both benefits and risks of AI and proposed multi-layered safety strategies involving physical safeguards, operational rules, and data/model management. These findings suggest that embedding the full AI pipeline in a tangible engineering-design task can support embodied, responsibility-oriented AI learning in middle school technology education.
Volume: 15
Issue: 4
Page: 3060-3074
Publish at: 2026-08-01

The effect of systematically created success situations on learning motivation among primary school students

10.11591/ijere.v15i4.39540
Sabila Abzhanova , Gulnar Uaisova , Madina Togatay , Aliya Jakhayeva , Nazymkul Altayeva , Saule Zhorayeva
Learning motivation is a critical factor influencing students’ academic engagement, persistence, and long-term educational outcomes. In primary education, motivation is particularly important, as early learning experiences shape children’s attitudes toward school and their willingness to participate in academic activities. The aim of this study was to examine the impact of systematically created success situations on learning motivation among primary school students in Kazakhstan. The study involved 72 participants. A quasi-experimental design was employed, with students participating in a six-week intervention structured to provide frequent success experiences, while a comparison group continued regular classroom instruction. Motivation was assessed before and after the intervention using selected subscales of the intrinsic motivation inventory, including interest/enjoyment, perceived competence, and effort/importance. The results indicated significant improvements in all measured dimensions of motivation for students who experienced the intervention compared with the comparison group. Increases were observed in students’ perceived competence, engagement with learning activities, and willingness to invest effort. These findings suggest that systematically creating opportunities for success is an effective approach to enhancing learning motivation in primary education and provide practical guidance for classroom implementation.
Volume: 15
Issue: 4
Page: 3405-3414
Publish at: 2026-08-01

Teachers’ perspectives on pedagogical challenges of AI integration in English language teaching

10.11591/ijere.v15i4.39226
Parthiban Ganesan , Karthikeyan Padmanathan , Thiyagu Kaliappan , Raja Kumar Subburaj , Durgaprasad Sahoo
Artificial intelligence (AI) tools are rapidly being integrated into English language teaching (ELT). However, limited empirical evidence exists regarding the pedagogical challenges teachers encounter during classroom implementation. This study investigates whether demographic variables influence ELT teachers’ perceptions of AI effectiveness and related pedagogical challenges. A quantitative survey design was employed, involving 200 ELT teachers with prior AI exposure. Data were collected using a validated Likert-scale instrument (α=0.81) and analyzed using independent samples t-tests, one-way ANOVA with Tukey post hoc analysis, and Chi-square (χ²) tests. Results revealed significant gender differences in perceived effectiveness (t=2.504, p=0.044) and pedagogical challenges (t=2.627, p=0.018), with female teachers reporting higher mean scores. Locality differences were significant only for perceived effectiveness (F=4.610, p0.05) or teaching experience (χ²=4.266, p>0.05). Educational level taught was significantly associated with perceived effectiveness (χ²=13.816, p
Volume: 15
Issue: 4
Page: 3439-3450
Publish at: 2026-08-01

The quality of advocacy services in primary school social work from the perspective of Vietnamese teachers

10.11591/ijere.v15i4.38789
Ha Van Hoang , Pham Thi Kieu Duyen
This study aimed to assess the level of satisfaction among primary school teachers with advocacy services in school social work and to identify influencing factors. A quantitative method was applied through a questionnaire survey of 398 primary school teachers, focusing on evaluating aspects of advocacy services such as reliability, responsiveness, professional competence, empathy, and implementation conditions. The results showed that the overall satisfaction level of teachers was quite high (M=4.01, SD=0.27), and all components of the service were viewed positively. Simultaneously, factors such as gender, age, location, and region influenced how teachers evaluated the quality of the service, while years of service and educational level had only a limited impact. On the other hand, all service components showed a positive correlation with the level of satisfaction with advocacy services in school social work. In this study, responsiveness, reliability, empathy, and implementation conditions showed statistically significant results. Therefore, the study suggests policy directions and further research, particularly in applying the service quality (SERVQUAL) model to measure and improve aspects of social work services.
Volume: 15
Issue: 4
Page: 3508-3517
Publish at: 2026-08-01

Development of ergonomic skills of future teachers of preschool organizations based on klax pedagogy

10.11591/ijere.v15i4.39441
Feruza Abdrimova , Sholpan Kolumbayeva , Aliya Kosshygulova , Gulnur Amirzhanova , Saltanat Khassanova
Training future preschool teachers requires the systematic development of ergonomic skills due to the high physical and emotional demands of their professional activity. However, existing teacher education programs often emphasize theoretical knowledge while providing limited opportunities for the development of practical ergonomic competencies. This study addresses this gap by investigating the effectiveness of klax pedagogy, a movement-oriented and experiential approach, in dev eloping ergonomic competence among future preschool teachers. A quasi-experimental pre-test/post-test control group design was employed with 84 students (experimental group: n=42; control group: n=42). The experimental group participated in klax-based activities focused on posture control, movement coordination, spatial organization, and ergonomic awareness, while the control group received traditional instruction. The results revealed a statistically significant improvement in ergonomic skills in the experimental group (t=9.84, p
Volume: 15
Issue: 4
Page: 3489-3496
Publish at: 2026-08-01

Classification of P300 event-related potentials using SNN, CNN and LSTM deep learning models

10.12928/telkomnika.v24i4.27659
Ahlaam; Bright Star University M. Saed , Ibtihal; College of Electrical and Electronics Technology Fawzi Elshami , Ali; University of Benghazi I. Elgayar
Accurate classification of P300 event-related potentials remains challenging due to the complex, non-stationary, and low signal-to-noise characteristics of electroencephalography (EEG) signals in brain-computer interface (BCI) systems. P300-based devices, such as the P300 speller, enable communication for patients with severe motor impairments, including those with locked-in syndrome; however, reliable brain signal classification is still a critical limitation. This study presents a comparative evaluation of deep learning models, including convolutional neural networks (CNN), long short-term memory (LSTM) networks, and spiking neural networks (SNN), for P300 signal classification. SNNs represent a biologically inspired paradigm that models the discrete, time-dependent behavior of neural spiking activity and offers advantages in terms of energy efficiency and hardware implementability. Experimental results demonstrate that CNN achieved the highest average classification accuracy (81.04%), followed closely by SNN (80.94%) and LSTM (80.60%). Although CNN slightly outperformed the other models, SNNs showed comparable accuracy while requiring fewer training samples and offering potential benefits for low power and real-time BCI systems. These findings highlight the trade-offs between classification performance and computational efficiency and underline the promise of SNNs as an efficient alternative for P300-based BCI applications.
Volume: 24
Issue: 4
Page: 1294-1306
Publish at: 2026-08-01

Autonomous trenching robot with intelligent obstacle detection and path optimization for precision cable installation

10.11591/ijeecs.v43.i2.pp425-438
Muhammad Omar , Hamza Ali Nisar , Muhammad Usman , Husnain Siddique , Suffian Zaman , Saad Saleem Khan , Justyna Robinson
Trenching for underground cable and pipeline installation is typically labor intensive, time-consuming, and potentially hazardous, particularly in environments with buried obstacles. This paper presents a low-cost autonomous trenching robot with intelligent obstacle detection and path optimization to improve excavation efficiency, safety, and accuracy. The proposed system integrates ultrasonic and infrared sensors with an embedded controller for real-time obstacle detection and autonomous navigation. A path optimization algorithm automatically adjusts the trenching route whenever an obstacle is detected, allowing continuous operation while reducing unnecessary movement and energy consumption. The robot employs a tracked mobile platform and an automated trenching mechanism capable of maintaining consistent trench depth and width under different terrain conditions. Experimental results demonstrate that the proposed system accurately detects obstacles, successfully replans its path in real time, and performs reliable autonomous trenching with minimal human intervention. Compared with conventional manual trenching methods, the developed robot improves operational efficiency, enhances excavation accuracy, and reduces safety risks for workers. The proposed system provides a practical and scalable solution for underground cable and pipeline installation and has strong potential for future applications in intelligent construction, infrastructure development, and autonomous civil engineering.
Volume: 43
Issue: 2
Page: 425-438
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
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