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Reframing teacher evaluation in higher education: a three-pillar framework from Assam

10.11591/ijere.v15i4.40024
Arabinda Rajkhowa , Munmi Dutta
Teacher evaluation shapes the quality of classroom instruction and, through it, student learning outcomes; yet in Indian higher education the dominant single-source model, student feedback channeled through the internal quality assurance cell (IQAC), is widely critiqued as ritualistic and developmentally inert. To the authors’ knowledge, no prior study has integrated student feedback, structured self-evaluation, and peer review into a coherent operational framework for regional Global South contexts. Drawing on primary data collected between 2009 and 2025 from approximately 200 undergraduate arts and science students, principally at North Lakhimpur College (now North Lakhimpur University) and other institutions across Assam, this study employs a qualitative-descriptive design with thematic analysis and triangulation of open-ended questionnaires and semi-structured interviews. Two contributions emerge. First, it yields a culturally grounded fivefold taxonomy of good teaching from Assamese student articulations, including culturally distinctive expectations: the teacher’s public moral role in the community and the obligation of intellectual life beyond the syllabus, that standardized student evaluation of teaching (SET) instruments routinely miss. Second, it proposes a developmental three-pillar framework integrating reformed student feedback, disciplined teacher self-evaluation, and structured peer review to restore the formative function of evaluation and improve student learning outcomes. Both the taxonomy and the framework are scalable across comparable institutions in the Global South.
Volume: 15
Issue: 4
Page: 2840-2851
Publish at: 2026-08-01

Policy–governance–culture dynamics in Myanmar education reform: implications for transformational leadership practice

10.11591/ijere.v15i4.39057
Chi Che , Win Pa Pa Tun
Myanmar’s education reform faces a persistent enactment gap because policy intent is filtered through governance feasibility and culturally grounded legitimacy norms. This study examined whether transformational leadership functions as a mediation practice that buffers policy–governance misalignment and under what conditions that buffering is stronger. The novelty of this study lies in proposing and testing an integrated policy–governance–culture (PGC) leadership mediation model that explains reform enactment through the joint effects of structural misalignment, leadership buffering, and culturally conditioned legitimacy. An explanatory sequential mixed-methods design combined a two-wave time-lag survey of teachers and middle leaders from 48 schools (N=720) with semi-structured interviews to clarify mechanisms. Measurement models showed acceptable-to-strong fit (CFA: CFI=0.956, TLI=0.948, RMSEA=0.044, SRMR=0.041). Multilevel SEM (ICC_RE=0.11) indicated that policy–governance misalignment directly reduced reform enactment (β=−0.15, p=.003) while increasing leadership mediation practices (β=0.25, p
Volume: 15
Issue: 4
Page: 2959-2972
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

Aquaponic greenhouse agriculture integrated with multi-modal sensors and LED-grow-light IoT-based

10.11591/ijece.v16i4.pp2254-2264
Pujianti Wahyuningsih , Muhammad Risal , Nining Haerani , Abdul Jalil
This study aims to develop a smart greenhouse aquaponic farming system that integrates aquaculture and hydroponic cultivation based on the Internet of Things (IoT). The proposed integration method employs multi-modal sensors and LED-grow-lights as supporting technologies to enable remote monitoring and control of aquaponic farming conditions through the Blynk IoT platform. The multi-modal sensors utilized in this research include DHT11 for monitoring air temperature and humidity, light dependent resistor (LDR) and infrared (IR) sensors for measuring sunlight intensity and LED-grow-lights levels, a soil moisture sensor for measuring hydroponic water volume, DS18B20 for monitoring aquaponic water temperature, a total dissolved solids (TDS) sensor for nutrient concentration, and pH-4502C for measuring water acidity. The LED-grow-lights functions as an artificial light source to replace sunlight under unfavorable weather conditions. In this study, a Raspberry Pi was implemented as the central data processing unit, while the Blynk IoT platform was employed to transmit aquaponic greenhouse data to the farmer’s smartphone. The experimental results demonstrate that the integration of multi-modal sensors enables effective monitoring of IoT-based aquaponic farming conditions with an accuracy level of up to 94% compared with other product of sensors, a monitoring and control delay ranging transmits the data from the embedded devices to smartphone farmer between 5 and 9 seconds, and reliable replacement of sunlight by the LED-grow-lights during adverse weather conditions.
Volume: 16
Issue: 4
Page: 2254-2264
Publish at: 2026-08-01

A hybrid retrieval augmented generation framework for automated educational document understanding and intelligent response generation

10.11591/ijece.v16i4.pp1964-1975
Basavesh D. , Jayashree Nagaraj
New students often struggle when short articles clash with thick textbooks. Still, even though large language models offer some teaching support, standard online setups lack focused accuracy - sometimes making things up - and risk user data control. Here comes an idea: build a tightly tested, self- contained system that aligns learning materials automatically without needing the internet, keeping information private by design. One look at two setups shows how they handle local reasoning differently. Instead of using both encoder and decoder parts, one system skips the encoder entirely. That simpler design grabs full context through ChromaDB without shrinking the data first. Meanwhile, the older type crunches input down, losing meaning along the way. Even though it runs fast - just under a second - errors pop up often, four out of five responses drifting off course. On the flip side, the new method builds correct code nearly every time, adds clear explanations tied to lesson goals, yet takes more than fourteen seconds to reply. Slower? Yes. More accurate? Clearly. What stands out is how compressed models running locally can still catch up in understanding classroom content. Another key point emerges: building tutors powered by artificial intelligence (AI) becomes safer when data never leaves the device and outside services are not needed at all.
Volume: 16
Issue: 4
Page: 1964-1975
Publish at: 2026-08-01

Enhancing students’ environmental literacy through STEM and project-based learning

10.11591/ijere.v15i4.39227
Akylbekova Turar , Bebolat Ibashev , Salih Çepni , Zhazira Mukataeva , Klara Sarsekova , Ibraimov Aibat
Amid escalating global environmental challenges and the increasing emphasis on sustainable development, enhancing students’ environmental literacy has become a critical priority in higher education. However, conventional instructional approaches remain predominantly theory-oriented and insufficient in fostering interdisciplinary thinking and real-world problem-solving competencies. Addressing this gap, this study proposes the integration of the science, technology, engineering, and mathematics–project-based learning (STEM–PjBL) model, which combines STEM education with PjBL to promote active engagement in authentic environmental problem-solving contexts. This study offers a novel contribution by implementing the STEM–PjBL approach within a Chemical Ecology course, thereby linking interdisciplinary STEM integration with the development of environmental literacy and systems thinking. A quantitative research design was employed involving 70 master’s students from Abai Kazakh National Pedagogical University with prior exposure to environmental disciplines. A 15-item diagnostic instrument was developed to assess environmental literacy, with content validity confirmed using Aiken’s V (0.70–0.91). Data were analyzed using descriptive statistics, correlation analysis, and Student’s t-test. The findings indicate a high level of environmental literacy (mean=85.1) and provide empirical evidence supporting the effectiveness of the STEM–PjBL approach. The results demonstrate improvements in systems thinking and environmentally responsible behavior, highlighting its potential to enhance environmental education practices in higher education.
Volume: 15
Issue: 4
Page: 3557-3566
Publish at: 2026-08-01

Miniaturized patch antenna for the S-band communication subsystem of the 3U University CubeSat

10.11591/ijece.v16i4.pp1913-1926
Nabil El Hassainate , Loubna Berrich , Nabil Benjelloun , Ahmed Oulad Said , Zouhair Guennoun
This paper introduces a miniaturized patch antenna for the reception module of the 3U University CubeSat in the S-band communications subsystem. In order to reduce the physical characteristics of the antenna (dimensions, mass) and achieve circular polarization (CP), as well as increasing its performances, two techniques are used: the first consists of introducing semicircle truncation on both sides of the square patch, and the second consists of modifying the ground plane with networks of symmetrical slots along the main axes (x,y). The fabricated antenna prototype has overall dimensions of 55×55×3.27 mm and a total mass of 20.59 g. The developed antenna spans the uplink band (2.025 to 2.110 GHz) for payload and telemetry operations. The designed antenna achieves a reflection coefficient below minus 10 dB across the target frequency band, along with a minus 3 dB axial ratio bandwidth that is well appropriate to space communication links. The comparisons of the prototype results to the simulation results using CST and HFSS provide close agreement of around 90%.
Volume: 16
Issue: 4
Page: 1913-1926
Publish at: 2026-08-01

Real-time facial and body pose emotion recognition for children with autism based on YOLOv8 and LSTM

10.11591/ijece.v16i4.pp1899-1912
Siti Nurohmahwati , Ananda Putra Kanieza , Ade Rifky Setiawan , Ahmad Fadlan
Children with autism spectrum disorder (ASD) often face challenges in recognizing and expressing emotions, which can affect their behavior and participation in inclusive classroom environments. This study proposes a real-time multimodal emotion recognition system integrating deep learning and Internet of Things (IoT) technologies to support early emotional monitoring in children with ASD. The framework combines YOLOv8 for facial expression detection and YOLOv8-based pose estimation for body movement analysis, along with a long short-term memory (LSTM) network for temporal emotion classification. At the facial level, the system recognizes five emotional states: sad, happy, neutral, boredom, and tantrum. At the temporal level, the LSTM model classifies behavioral sequences into three categories: neutral/bored, happy, and tantrum, enabling hierarchical emotion interpretation from instantaneous expressions to temporal patterns. Experimental results show that the facial expression model achieves 92% precision, while the LSTM-based classifier reaches 95% peak validation accuracy and 93.33% final test accuracy. The system is deployed on a web- based monitoring platform with real-time notifications for educators and parents. The proposed approach demonstrates effectiveness in providing timely emotional insights to support early intervention and improve inclusive education for children with ASD.
Volume: 16
Issue: 4
Page: 1899-1912
Publish at: 2026-08-01

Automated prediction of the mode of birth delivery using geometric features of uterine contraction segments

10.11591/ijece.v16i4.pp1885-1898
Rubana Hoque Chowdhury , Roma Sultana , Quazi Delwar Hossain , Mohiuddin Ahmad
Pregnancy is a unique, complex process and it's hard to predict the mode of birth delivery due to the lack resources. This study aims to develop a clinical decision support system to predict birth delivery mode according to three categories: spontaneous vaginal delivery, induced vaginal delivery, and cesarean section. This methodology entails the automated extraction of uterine contraction segments from the electrohysterogram (EHG) signal based on the zero-crossing rate. Each segment is processed through a discrete Fourier transform to obtain the Fourier coefficients. Geometric features, including area, perimeter, circularity, variance, and bending energy were extracted from the boundary shape of these complex coefficients using the convex hull method. We found the women who experience spontaneous deliveries exhibit higher feature values and a lower circularity value compared to those who undergo cesarean sections or induced births. Based on these extracted parameters, the random forest (RF) model yielded promising results: reaching an accuracy above 90% in the classification between caesarean and spontaneous deliveries and spontaneous and induced vaginal deliveries and somewhat lower, around 70% between induced and cesarean. To conclude, the utilization of all proposed EHG parameters through machine learning can enhance obstetricians' ability to predict the mode of birth delivery.
Volume: 16
Issue: 4
Page: 1885-1898
Publish at: 2026-08-01

Open data for clinical AI: a comprehensive review of disease prediction datasets

10.11591/ijece.v16i4.pp1955-1963
Sindhu Rajendran , Chandrashekar B. S.
With the advancements in the medical sector world-wide, the use of machine learning has been in use. In order to use these machine learning models for prediction and diagnosis of certain diseases one of the main components is datasets. The need for high-quality datasets in healthcare prediction models is critical due to the data-driven nature of machine learning. These models rely on comprehensive, accurate, and representative datasets to make reliable predictions that can impact real-world patient outcomes. This paper provides an insight about the different components in the datasets present for diseases such as osteoporosis, heart disease, diabetes, respiratory, syncytial virus, interactive thyroid, Parkinson’s and sepsis. Also, a comparative study on the parameters of the datasets in the Indian perspective and globally are also discussed.
Volume: 16
Issue: 4
Page: 1955-1963
Publish at: 2026-08-01

A review of stability analysis in islanded microgrids with photovoltaic integration

10.11591/ijece.v16i4.pp1832-1840
Ganeshan Viswanathan , Govindanayakanapalya Venkatagiriyappa Jayaramaiah
Microgrids, emerging as a solution to meet rising energy demands and combat environmental issues, present unique challenges in stability analysis, especially when integrated with photovoltaic (PV) systems. This review explores the stability analysis of islanded microgrids with PV integration, addressing significant gaps in current understanding and methodologies. Firstly, the paper classifies microgrid stability into small signal, transient, and voltage stability, highlighting the distinct characteristics of each aspect. Subsequently, it provides an overview of stability analysis techniques, encompassing conventional, intelligent, and hybrid methodologies. The operational challenges faced by islanded microgrids are examined, along with effective control strategies to mitigate them. Moreover, the integration of photovoltaic systems into microgrids is scrutinized, including system configurations, stability impacts, and control methods. Finally, the paper discusses existing challenges and outlines future directions for advancing microgrid stability analysis. By explaining these critical aspects, this review underscores the necessity of enhancing stability analysis frameworks to ensure the robustness and reliability of islanded microgrids with PV integration in the evolving energy landscape.
Volume: 16
Issue: 4
Page: 1832-1840
Publish at: 2026-08-01

Behavioral fingerprints: driver profiling using transformer models on next generation simulation trajectory data

10.12928/telkomnika.v24i4.27790
Mohamed; Abdelmalek Essaadi University Laamimach , Mghari; Abdelmalek Essaadi University Mohammed , Aziz; Abdelmalek Essaadi University Mabrouk
Characterizing individual driver behavior is essential for advancing intelligent transportation systems (ITS) and autonomous vehicle safety. While deep learn ing models excel at macroscopic traffic prediction, individual driving styles are often aggregated away. This paper addresses this gap by proposing a novel, weakly supervised transformer framework for driver behavior profiling using high-resolution next generation simulation (NGSIM) US-101 trajectory data. We extract microscopic behavioral features including acceleration, lane change dynamics, and headway management from 30-second observation segments. A transformer encoder learns complex temporal dependencies to classify drivers into ’aggressive’ and ’normal’ profiles, achieving a 97% F1-score on proxy labeled segments. Crucially, these “proxy labels” are derived from heuristic statistics, meaning the model is trained to learn the mapping from sequences to these behavioral indicators rather than identifying objective aggression. Our methodology enables the creation of precise “behavioral fingerprints” that cap ture individual driving nuances. These insights are vital for developing adaptive ITS that anticipate traffic stability issues and enhance autonomous vehicle safety by predicting human intent.
Volume: 24
Issue: 4
Page: 1168-1176
Publish at: 2026-08-01

TikTok as a constructivist platform for ethically and algorithmically literate TESL in the industrial era 5.0

10.11591/ijere.v15i4.37991
Ninik Suryatiningsih , Dwi Priyo Utomo , Joko Widodo , Masduki Masduki
This research places the integration of TikTok in teaching English as a second language (TESL) as a directed pedagogic design that combines constructivist engagement, digital ethics, and algorithmic literacy in the context of the industrial era 5.0. This study aims to: i) examine the influence of constructivist engagement on speaking fluency; ii) assess the effectiveness of digital ethics rubrics; and iii) test the mediating role of algorithmic literacy. Using a sequential mixed method, the study involved 130 participants (120 students, 10 lecturers) in a 12-week intervention that included ethics orientation and algorithmic literacy, task modeling, micro-speaking video production, rubric-based peer-lecturer feedback, and reflection and revision. Quantitative data were analyzed by regression, paired t-test, analysis of variance (ANOVA), and mediation analysis, while qualitative data were analyzed thematically and integrated through joint displays. Results showed that constructivist involvement had a significant effect on speaking fluency (β=0.68; R²=0.46; p
Volume: 15
Issue: 4
Page: 3334-3345
Publish at: 2026-08-01

Mathematical mindset shift: reducing math anxiety and enhancing attitudes in secondary school students through growth mindset intervention

10.11591/ijere.v15i4.32397
Noor Atiqah Mohd Noh , Aini Marina Ma'rof , Yusni Mohamad Yusop
This study examines the effectiveness of a growth mindset intervention (GMI) in improving mathematical mindset, reducing math anxiety, and enhancing attitudes toward mathematics among secondary school students. A true-experimental design was employed, involving 69 Form Two students from secondary schools in Kedah, Malaysia. Participants were randomly assigned to a treatment group (n=35) or a control group (n=34). Data were analyzed using multivariate analysis of covariance (MANCOVA). The results revealed significant differences in math anxiety (p=0.000) and attitudes toward math (p=0.000) between the treatment and control groups after controlling for covariates (math scores and gender). However, no significant difference was observed in mathematical mindset between groups (p=0.292). Within the treatment group, significant improvements were noted in mathematical mindset (p=0.001), math anxiety (p=0.000), and attitudes toward math (p=0.000), while the control group showed no significant changes across these variables. Students in the treatment group experienced reduced math anxiety and improved attitudes toward math following the intervention. These findings suggest that GMI is an effective strategy for mitigating math anxiety and fostering positive attitudes toward mathematics, providing valuable insights for educators seeking to improve students' learning experiences and outcomes.
Volume: 15
Issue: 4
Page: 3182-3192
Publish at: 2026-08-01

Online collective efficacy and its relationship with organizational sustainable development in higher education

10.11591/ijere.v15i4.38721
Ashraf Ragab Ibrahim , Ibrahim Mohammed Ibrahim , Billal Mohamed Aboelhasayb , Mohammed Maher Mohammed , Ahmed Metwally Eissa , Mohamed Ali Nemt-allah
Digital transformation in higher education has intensified reliance on online collaboration, yet the role of shared digital capability beliefs in driving institutional sustainability remains underexplored. This study examined the relationship between online collective efficacy (OCE) and organizational sustainable development (OSD) among faculty members in Egyptian higher education. Using a quantitative correlational design, a purposive sample of 647 faculty members and teaching assistants from Al-Azhar University completed two validated instruments: the OCE scale and the OSD questionnaire. Pearson correlation and multiple regression analyses revealed exceptionally strong positive associations between all OCE dimensions and OSD outcomes (r=.887, p
Volume: 15
Issue: 4
Page: 2775-2785
Publish at: 2026-08-01
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