The Promises and Pitfalls of Using Language Models to Measure Instruction Quality in Education
Project Overview
The document examines the application of Natural Language Processing (NLP), specifically pre-trained Language Models (PLMs), in evaluating instructional quality in education, particularly within K-12 and special education environments. It contrasts traditional, often costly and inconsistent manual assessments with automated evaluations that can analyze classroom transcripts efficiently. The study highlights the effectiveness of these models in assessing high-inference teaching practices while addressing challenges such as noisy input data and imbalanced label distributions. It emphasizes the potential for NLP to provide timely feedback to educators, thus enhancing their teaching strategies. However, the document also acknowledges the limitations of these models, particularly in their ability to evaluate complex teaching practices and the lack of high-quality training samples, which can impact their overall performance.
Key Applications
Natural Language Processing (NLP) for instructional quality assessment
Context: In-person K-12 classrooms and simulated performance tasks for pre-service teachers
Implementation: Analyzing transcripts from classroom recordings using pre-trained Language Models to evaluate teaching practices
Outcomes: Achieved agreement levels comparable to human raters for discrete variables, improved ability to assess student-centered teaching practices using only teacher utterances
Challenges: Diminished efficacy on complex teaching practices; challenges include noisy and long input data, and imbalanced label distributions
Implementation Barriers
Technical Challenge
Noisy and long input data makes it difficult for models to focus on relevant information.
Proposed Solutions: Adoption of a two-stage prediction strategy to filter and focus on relevant sentences.
Data Limitation
Highly skewed distribution of teaching quality ratings, with very few high-rating samples available.
Proposed Solutions: Use class-weighted loss to improve predictions on underrepresented classes.
Project Team
Paiheng Xu
Researcher
Jing Liu
Researcher
Nathan Jones
Researcher
Julie Cohen
Researcher
Wei Ai
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Paiheng Xu, Jing Liu, Nathan Jones, Julie Cohen, Wei Ai
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai