Unlocking the power of student feedback a datadriven approach to teacher assessment for eulogio “amang” Rodriguez institute of science and technology

This study aimed to develop a data-driven framework for utilizing student feedback to enhance teacher assessment at Eulogio “Amang” Rodriguez Institute of Science and Technology (EARIST). By analyzing both indirect (student feedback) and direct (exam-based) assessment data in Networking 1 and 2, the study aimed to: (1) identify key parameters of effective teaching perceived by students; (2) design a systematic approach for collecting, analyzing, and interpreting student feedback using LMS and Faculty Portal; (3) establish a correlation between indirect and direct assessment metrics; (4) develop actionable recommendations for improving teaching strategies and fostering professional development; and (5) upgrade continuous quality improvement through evidence-based teacher evaluation practices.The analysis showed differences in how well students achieved course outcomes over different semesters, as well as a gap between how students viewed their performance and their actual exam results. Key recommendations include tackling challenges related to specific course outcomes, closing the divide between direct and indirect assessments, improving assessment methods, and using data for ongoing enhancement. The study highlights the need for a holistic approach to assessment, which includes both direct and indirect measures, to improve teaching and learning at EARIST.