An Enhanced Research Productivity Monitoring System for Higher Education Institutions (HEI's) with Natural Language Processing (NLP)

Abstract

The HEI in the Philippines is entrusted with three mandates by the Commission on Higher Education (CHED): teaching, research, and extension service. Among these mandates, research productivity plays a crucial role in fostering knowledge generation, production, and transfer within the community. However, monitoring research productivity has been a persistent challenge due to the diverse evaluation criteria set by local and international accrediting organizations, which directly impact an HEI's educational quality. This study has selected an academic institution and aims to 1) Design a system model as research data collection; 2) employ Natural Language Processing (NLP)-based techniques to facilitate the monitoring and retrieval of information on research productivity and 3) generate visualizations and analytics based on research data. The extensive review of relevant literature, visualization and analysis of the institution's research data were carried out in this study. NLP techniques was employed to enable mapping of the institution's research output to the government and organizations research agenda, facilitating the identification of emerging topics through clustering techniques and modeling. Systems evaluation through Technology Acceptance Model (TAM) was gained 4.56 mean results from the respondent corresponding to “Strongly Agree” which indicates that system is able to assist in monitoring the institution's research productivity.