DOI: 10.5281/zenodo.17369216
title: CDLM-AA

description: CDLM-AA: Cognitive Diagnostic Language Model for Adaptive English Writing Assessment

Overview

CDLM-AA is an intelligent cognitive diagnosis and adaptive assessment system for English writing ability, integrating pre-trained language models (e.g., BERT or GPT) with cognitive diagnostic theory. The framework overcomes the limitations of traditional rule-based and human-evaluated writing assessments by providing scalable, context-sensitive, and personalized feedback.

At its core, CDLM-AA introduces:



CDLM (Cognitive Diagnostic Language Model) — a multimodal, transformer-based language architecture aligned with cognitive attributes.

Adaptive Assessment Mechanism — dynamically adjusts task difficulty based on individual learner profiles.

Innovative Methodologies — graphical propagation layers and spatial-temporal attention for fine-grained diagnostic evaluation.


✨ Features

1. Cognitive Diagnostic Language Model (CDLM)



Built on pre-trained language models (e.g., BERT, GPT), fine-tuned on annotated writing samples.

Maps writing samples to latent cognitive attributes (grammar, coherence, vocabulary, organization).

Multimodal Encoder + Encoder–Decoder architecture integrates textual and auxiliary inputs.

Sparse Mixture-of-Experts dynamically routes representations for specialized cognitive tasks.📎 See Figure 1 on page 7 for the overall architecture diagram of CDLM.


2. Multimodal Encoder Architecture



Employs static and dynamic channel mixing for robust feature refinement.

Balances adaptability with stable structure to support different writing genres.📎 Figure 2 on page 8 shows the dual-path encoding structure with static/dynamic attention blocks.


3. Graphical Propagation & Attention Mechanisms



Graphical Propagation Layer dynamically adjusts weighting of attributes according to learner performance.

Spatial-Temporal Attention focuses on critical textual segments, highlighting grammar, coherence, and structure.

Real-time iterative recalibration aligns feedback with learner progress.📎 Figures 3–4 (pages 10–11) illustrate the feature mapping and graph-based attention process.


4. Adaptive Assessment



Selects tasks using a utility function targeting weak attributes.

Task difficulty and focus evolve with learner proficiency, ensuring continuous engagement and growth.


📊 Datasets




Dataset
Description
Purpose




English Writing Skill Evaluation Dataset
Writing samples annotated for grammar, vocabulary, coherence
Model training and benchmarking


Cognitive Writing Assessment Dataset
Think-aloud protocols, keystroke logs, cognitive writing behaviors
Cognitive process modeling


Language Model Guided Writing Dataset
Human-model interactive writing tasks
LLM-assisted feedback testing


Adaptive English Writing Proficiency Dataset
Adaptive tasks adjusted by performance
Real-time feedback evaluation






 


🚀 Usage



Attribute mastery scores (grammar, coherence, vocabulary, organization)

Attention visualization on critical text segments

Personalized writing feedback

Adaptive prompt selection for next tasks


🧪 Applications



Automated, adaptive English writing evaluation

Cognitive profiling for targeted learning interventions

Real-time formative feedback in digital learning environments

Scalable assessment for large educational settings


🧩 Model Components




Component
Description




CDLM
Transformer-based cognitive diagnostic language model


Multimodal Encoder
Static + dynamic channel mixing for feature integration


Graphical Propagation Layer
Adjusts task focus dynamically


Spatial-Temporal Attention
Highlights key textual elements for feedback


Adaptive Feedback Loop
Iteratively updates learner profile and prompt difficulty




📈 Performance




Dataset
Accuracy
Precision
Recall
F1 Score




English Writing Skill Evaluation
89.78
89.23
88.56
88.89


Cognitive Writing Assessment
90.67
90.12
89.56
89.89


Language Model Guided Writing
89.12
88.56
87.90
88.23


Adaptive Writing Proficiency
90.03
89.60
88.94
89.27




📎 Tables 1–2 on pages 15–16 compare CDLM-AA to ResNet, ViT, I3D, BLIP, DenseNet, and MobileNet, showing consistent gains across all metrics.

🧭 Future Work



Expand cross-linguistic support for multilingual writing assessment.

Enhance efficiency via lightweight model variants for classroom deployment.

Integrate explainable AI for transparent feedback interpretation.

Enable real-time adaptive learning loops for live educational applications.


📜 License

This project is licensed under the MIT License.

🙏 Acknowledgments

This work was supported by the Higher Education Research Program of Hainan Province (Grant No. Hnjg2023-58).Authors: Minghui Chen, Yang Tian, Shenggu Chen, Zhengzheng Huang.This research bridges cognitive diagnostic theory with pre-trained language modeling, advancing adaptive and scalable English writing assessment.