Archive | 2021
Using Word Embeddings to Analyze Teacher Evaluations: An Application to a Filipino Education Non-Profit Organization
Abstract
Analysis of teacher evaluations is crucial to the development of robust educational programs, particularly through the validation of desirable qualities being reflected on in the text. This research applies Natural Language Processing techniques on a real-world dataset from a Filipino education non-profit to explore insights from analyzing evaluations written by Teacher Fellows who assess their own progress. Prior to this research, only qualitative assessment had been conducted on the text. Inspired by the use of word embedding similarities to capture semantic alignment, we utilize GloVe embeddings to determine to what extent these evaluations reflect concepts critical to measuring the competency of Teacher Fellows and upholding the organization’s Vision and Mission. As Fellows’ quantitative ratings improved, so too did their demonstration of competency in the text. Further, Teacher Fellow language was consistent with the organization’s Vision and Mission. This research therefore showcases the possibilities of NLP in education, improving our understanding of Teacher Fellow evaluations, which can lead to advances in program operations and education efforts.