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Cognitive Offloading Detection & Adaptive Pedagogical Policy Models
Model artifacts for the Cognitive Offloading Prevention System.
Contents
classifier_v1/:microsoft/deberta-basefine-tuned to classify student messages for cognitive offloading / answer extraction behavior.backbone/: Tokenizer files and transformer base weights (model.safetensors).head.pth: 2-layer MLP classification head (768 -> 256 -> 64 -> 2).config.json: Model configuration and hyperparameters.
policy_network_v1.pth: Deep Q-Network (DQN) policy trained via reinforcement learning to select dynamic pedagogical intervention actions (redirect,scaffold,socratic,delay) balancing learning gains against engagement friction.
Usage
To download these models locally:
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="addy0606/cognitive-offloading",
local_dir="models",
)
GitHub Repository
Source code, evaluation pipeline, and Flask interface: https://github.com/Addy0606/cognitiveoffloading
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("addy0606/cognitive-offloading", device_map="auto")