Work

DLSU at BEA 2025 Shared Task: Towards Establishing Baseline Models for Pedagogical Response Evaluation Tasks

Year: 2025

Type: conference-paper

Abstract: We present our submission for Tracks 3 (Providing Guidance), 4 (Actionability), and 5 (Tutor Identification) of the BEA 2025 Shared Task on Pedagogical Ability Assessment of AI-Powered Tutors.Our approach sought to investigate the performance of directly using sentence embeddings of tutor responses as input to downstream classifiers (that is, without employing any fine-tuning).To this end, we benchmarked two general-purpose sentence embedding models: gte-modernbert-base (GTE) and all-MiniLM-L12-v2, in combination with two downstream classifiers: XGBoost and multilayer perceptron.Feeding GTE embeddings to a multilayer perceptron achieved macro-F1 scores of 0.4776, 0.5294, and 0.6420 on the official test sets for Tracks 3, 4, and 5, respectively.While overall performance was modest, these results offer insights into the challenges of pedagogical response evaluation and establish a baseline for future improvements.

Source: Workshop on Innovative Use of NLP for Building Educational Applications

DOI: 10.18653/v1/2025.bea-1.101

Authors: Maria Monica Manlises, Mark Edward Gonzales, Lanz Lim

Institution: De La Salle University

Language: English

Topic: Intelligent Tutoring Systems and Adaptive Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: hybrid

OpenAlex ID: W4412889188