Optimizing Resource Allocation for University Biotechnology Education Using Big Data Analytics

Abstract

In the rapidly evolving landscape of commercial biotechnology education, big data emerges as a pivotal tool for enhancing educational resource allocation. This study investigates the application of big data analytics within higher education institutions specialized in biotechnology, highlighting the shift towards a market-oriented management style that adapts entrepreneurial and flexible educational models. Using a robust big data platform, the research analyzes extensive datasets generated from the interactions of 230 university faculty members from various Indian universities, gathered during an international conference on educational excellence. The participants, including 180 male and 50 female professors, contributed to both quantitative and qualitative data collection. Quantitative analysis was conducted using the 34-item Big Data Assessment for Learning Scale (B_DALS), while qualitative insights were drawn from semi-structured interviews with ten administrative leaders, such as Deans and Department Heads. The study focuses on understanding faculty perceptions of big data’s utility in learning assessments, exploring its potential to enhance teaching and learning strategies within biotechnology education. The findings reveal significant opportunities provided by big data to optimize educational resource allocation, improve learning outcomes, and address the challenges of managing large-scale educational data. These insights underscore the necessity for robust IT infrastructure, effective learning management systems, and policies that foster engaging learning analytics and faculty development. The study contributes valuable empirical data, offering a foundational perspective for future research on leveraging big data in biotechnological education settings, aiming to enhance institutional accreditation and overall educational quality.