Past Issues

Investigating the Role of Teachers in Supporting Students Through Transitions Brought by the National Education Policy 2020

Abstract

The National Education Policy 2020 (NEP 2020) introduces major structural and pedagogical reforms within India’s school and higher‐education systems—such as the 5+3+3+4 school structure, foundational literacy and numeracy (FLN) mission, multidisciplinary and flexible curricula, assessment reform, teacher professional development and digital integration. This paper examines the critical role of teachers in helping students navigate the transitions engendered by these reforms. Specifically, it explores how teachers’ roles are changing, what support they require to effectively guide students through change (curriculum, assessment, pedagogy, technology), and what constraints hamper that role. Drawing on literature on teacher professional development and transformation under NEP 2020, the paper offers a framework of teacher responsibilities and enablers/barriers in the context of student transition, and provides recommendations to strengthen teacher support systems. The analysis finds that while NEP 2020 rightly places teachers at the centre of change, success will depend on robust in service training, domain specific mentoring, time and resource allocation, as well as systemic support for teacher well being and adaptive capacity.

सोशल मीडिया, मानसिक स्वास्थ्य और युवा पहचान-व्यथा: भारतीय संदर्भ में एक समाजशास्त्रीय अध्ययन

Abstract

यह शोध-पत्र भारतीय युवाओं के मानसिक स्वास्थ्य और पहचान-निर्माण पर सोशल मीडिया के प्रभाव का समाजशास्त्रीय विश्लेषण प्रस्तुत करता है। अध्ययन द्वितीयक स्रोतों पर आधारित व्याख्यात्मक एवं आलोचनात्मक साहित्य-संश्लेषण है और किसी काल्पनिक प्राथमिक सर्वेक्षण का दावा नहीं करता। विश्लेषण का केंद्रीय तर्क है कि प्रभाव केवल उपयोग की अवधि से नहीं, बल्कि उपयोग की शैली, सामग्री, सामाजिक तुलना, दर्शक-प्रतिक्रिया, साइबरबुलिंग, एल्गोरिदमिक दृश्यता, ऑफलाइन संबंधों और पूर्ववर्ती संवेदनशीलता से बनता है। निष्क्रिय स्क्रॉलिंग, आदर्शीकृत छवियों से तुलना, लोकप्रियता-सूचकों पर निर्भरता और ऑनलाइन बहिष्करण चिंता, असंतोष, नींद में बाधा तथा वास्तविक और प्रस्तुत स्व के बीच दूरी बढ़ा सकते हैं। इसके विपरीत सक्रिय, प्रामाणिक और उद्देश्यपूर्ण सहभागिता सामाजिक समर्थन, आत्म-अभिव्यक्ति और समुदाय से जुड़ाव को प्रोत्साहित कर सकती है। भारतीय संदर्भ में परिवार, लिंग, भाषा, वर्ग और ग्रामीण-शहरी असमानता निर्णायक हैं। निष्कर्षतः सोशल मीडिया को मानसिक परेशानी का एकमात्र कारण मानने के बजाय व्यक्ति, समाज और मंचीय संरचना के पारस्परिक संबंधों का विश्लेषण आवश्यक है।

Green Synthesis and Antibacterial Activity of Fittonia albivenis-Mediated CuO–Ag Nanoparticles

Abstract

Multidrug-resistant Escherichia coli remains a major therapeutic challenge because conventional antibiotics are increasingly compromised by resistance mechanisms and restricted penetration into bacterial cells. This study evaluated Fittonia albivenis methanolic leaf extract as a reducing, chelating, and capping agent for the green synthesis of CuO–Ag bimetallic nanoparticles. Sequential Soxhlet extraction was performed using petroleum ether, ethyl acetate, and methanol, and the methanolic fraction showed the highest extraction yield and the greatest phenolic and flavonoid contents. CuO–Ag nanoparticles were synthesized at Cu:Ag molar ratios of 1:1, 1:2, and 2:1 and were characterized by UV–Visible spectroscopy, FTIR, dynamic light scattering, zeta-potential analysis, X-ray diffraction, scanning electron microscopy, and transmission electron microscopy. The Cu:Ag 1:2 formulation exhibited the most favorable properties, with a hydrodynamic diameter of 31.6 ± 3.7 nm, a PDI of 0.21 ± 0.02, and a zeta potential of −36.8 ± 2.1 mV. XRD confirmed monoclinic CuO and face-centred cubic silver phases, while TEM showed particles predominantly within 24–36 nm. Antibacterial activity was assessed against E. coli MTCC 723 and MTCC 40. The optimized formulation produced inhibition zones of 25.8 ± 0.6 and 22.6 ± 0.5 mm, respectively. MIC/MBC values were 32/64 µg/mL for MTCC 723 and 64/128 µg/mL for MTCC 40. The results demonstrated that Fittonia-mediated CuO–Ag nanoparticles possessed stable physicochemical properties and strong bactericidal activity against both strains. Their enhanced performance over monometallic controls was attributed to complementary ROS generation, metal-ion release, membrane disruption, and inhibition of essential bacterial enzymes.

Machine Learning-Based Early Detection of Learning Gaps in K–12 Students

Abstract

Early identification of learning gaps is essential for providing timely educational support and preventing persistent academic difficulties among K–12 students. Traditional assessment approaches often identify learning problems after considerable academic decline, limiting opportunities for early intervention. This study developed a machine learning-based predictive framework for detecting learning gaps by integrating academic, behavioural, and engagement-related indicators. A student learning dataset was analysed through data preprocessing, feature engineering, and predictive modelling approaches. Multiple machine learning algorithms, including Logistic Regression, Support Vector Machine, Random Forest, Extreme Gradient Boosting (XGBoost), and Artificial Neural Network, were developed and evaluated using accuracy, precision, recall, F1-score, ROC-AUC, and confusion matrix analysis. The comparative results demonstrated that ensemble learning approaches provided superior predictive performance by effectively capturing complex relationships among different learning indicators. XGBoost achieved the highest classification performance, followed by Random Forest, indicating strong capability for identifying students at risk of learning difficulties. Feature importance analysis revealed that previous academic achievement, assessment performance trends, assignment completion behaviour, and engagement patterns were major contributors to learning-gap prediction. The findings highlight the potential of artificial intelligence-driven educational analytics for transforming traditional reactive assessment systems into proactive early-warning mechanisms. The proposed framework provides educators with data-driven insights for identifying vulnerable learners and designing targeted interventions. This research contributes to the growing field of artificial intelligence in education by presenting an effective predictive approach for supporting personalised learning and evidence-based educational decision-making.