Neural Architectures for Pedagogical Evaluation: A Convolutional Framework for the Automated Assessment of Instructional Confidence - Ranjan Banerjee,; Amartya Ghosh,; Pranab Gharai,; Ria pyne,; Piyali De,; Avijit Kumar Chaudhuri,; Payel Sengupta,; Debmalya Mukherjee,; Anushree Jana, (The Bioscan, 2026)
The optimization of instructional delivery in higher education is increasingly contingent upon the integration of sophisticated technological frameworks capable of providing objective, real-time feedback to faculty members. Among the various psychological constructs that influence teaching effectiveness, lecturer confidence emerges as a primary determinant of student engagement, information retention, and the overall classroom climate.1 Confidence is not merely a subjective internal state; it is a systematically projectable trait manifested through facial micro-expressions, body orientation, and linguistic fluency.1 The ability of a lecturer to exhibit self-assurance enables more structured communication, a higher capacity for handling spontaneous audience challenges, and a more profound conviction in the dissemination of complex ideas.1 With the rapid advancement of deep learning, specifically within the domain of computer vision, the automated recognition of these affective states has moved from theoretical inquiry to practical implementation.5 This report analyzes a novel approach utilizing a scratch-built Convolutional Neural Network (CNN) architecture designed to categorize lecturer confidence into three distinct levels: high, medium, and low.1 By processing a unique dataset of 4,219 images extracted from diverse lecture environments, the proposed model demonstrates a significant improvement in classification accuracy over established architectures like VGG16 and AlexNet.1 The following analysis explores the technical architecture, mathematical optimization, pedagogical implications, and the rigorous ethical standards required for the deployment of such systems in modern academic settings.