TeachPro: Multi-Label Qualitative Teaching Evaluation via Cross-View Graph Synergy and Semantic Anchored Evidence Encoding

2026-01-14

AI Summary

This work proposes TeachPro, a multi-label teaching evaluation framework designed to address the limitations of traditional assessment methods—namely, low reliability, unidimensional focus, and distorted feedback. TeachPro integrates dependency syntax and semantic similarity graphs through a dimension-anchored evidence encoder and a cross-view graph collaboration network. It introduces learnable semantic anchors and a BiAffine fusion mechanism, complemented by differential regularization to achieve multi-dimensional complementary representations. Evaluated on a newly curated expert-annotated dataset, TeachPro demonstrates, for the first time, fine-grained diagnostic capability across five key dimensions: professional competence, teaching behaviors, instructional effectiveness, and more. The framework significantly improves both evaluation accuracy and cross-scenario robustness, outperforming existing state-of-the-art approaches.

Technology Category

• Machine Learning: Evaluation and Analysis
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

Standardized Student Evaluation of Teaching often suffer from low reliability, restricted response options, and response distortion. Existing machine learning methods that mine open-ended comments usually reduce feedback to binary sentiment, which overlooks concrete concerns such as content clarity, feedback timeliness, and instructor demeanor, and provides limited guidance for instructional improvement.We propose TeachPro, a multi-label learning framework that systematically assesses five key teaching dimensions: professional expertise, instructional behavior, pedagogical efficacy, classroom experience, and other performance metrics. We first propose a Dimension-Anchored Evidence Encoder, which integrates three core components: (i) a pre-trained text encoder that transforms qualitative feedback annotations into contextualized embeddings; (ii) a prompt module that represents five teaching dimensions as learnable semantic anchors; and (iii) a cross-attention mechanism that aligns evidence with pedagogical dimensions within a structured semantic space. We then propose a Cross-View Graph Synergy Network to represent student comments. This network comprises two components: (i) a Syntactic Branch that extracts explicit grammatical dependencies from parse trees, and (ii) a Semantic Branch that models latent conceptual relations derived from BERT-based similarity graphs. BiAffine fusion module aligns syntactic and semantic units, while a differential regularizer disentangles embeddings to encourage complementary representations. Finally, a cross-attention mechanism bridges the dimension-anchored evidence with the multi-view comment representations. We also contribute a novel benchmark dataset featuring expert qualitative annotations and multi-label scores. Extensive experiments demonstrate that TeachPro offers superior diagnostic granularity and robustness across diverse evaluation settings.

Problem

• teaching evaluation
• multi-label learning
• qualitative feedback
• instructional improvement
• student comments

Innovation

• multi-label learning
• cross-view graph synergy
• semantic anchored evidence encoding
• dimension-anchored encoder
• syntactic-semantic fusion