Data-Driven Teaching Effectiveness Assessment Through Logistic Regression for Enhanced Evaluation Systems and Probabilistic Decision-Making

DOI

10.2991/978-94-6239-638-8_27How to use a DOI?

Keywords

teaching effectiveness; logistic regression; probabilistic assessment; text analysis; student evaluations; uncertainty quantification; bilingual processing

Abstract

Teaching evaluations provide essential feedback for improving educational quality, yet institutions struggle to efficiently utilize unstructured student comments. This study implements ordinal logistic regression to analyze 4,410 bilingual (English-Filipino) student comments, creating a probabilistic framework for predicting teaching effectiveness across standardized evaluation dimensions. The models achieved predictive performance with AUC values ranging from 0.83 to 0.91, with Knowledge of Subject demonstrating 86.9% accuracy. The system demonstrated 75% reduction in manual analysis time while providing quantified uncertainty measures.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Nolan M. Yumen
AU  - Angie C. Canillo
PY  - 2026
DA  - 2026/04/30
TI  - Data-Driven Teaching Effectiveness Assessment Through Logistic Regression for Enhanced Evaluation Systems and Probabilistic Decision-Making
BT  - Proceedings of the  Workshop on Computation: Theory and Practice (WCTP 2025)
PB  - Atlantis Press
SP  - 535
EP  - 549
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6239-638-8_27
DO  - 10.2991/978-94-6239-638-8_27
ID  - Yumen2026
ER  -