BERT-based Multi-label Cognitive Load Classifier

This model is a fine-tuned bert-base-uncased transformer trained to classify students' cognitive and psychological states (e.g., cognitive load, confidence, anxiety) from naturalistic human-AI educational dialogues in K-12 settings.

🧠 What does the model do?

The model performs multi-label classification on student-AI conversations, identifying whether a given interaction reflects one or more of the following cognitive and affective states:

Math Confidence / Math Anxiety
AI Confidence / AI Concerns
Intrinsic Cognitive Load
Extraneous Cognitive Load
Germane Cognitive Load

Each input text (a single conversation) may correspond to multiple labels simultaneously.

📚 Training Data

The model was trained on a custom dataset collected from a large-scale empirical study involving 160 K-12 students interacting with an AI-powered teachable agent in a math learning platform (ALTER-Math, name anonymized for review).

Dialogues: 1,440 student-agent interactions over 10 days
Labels: Derived from pre- and post-questionnaires grounded in Cognitive Load Theory and affective constructs
Label types: Binary indicators (0/1) per psychological factor
Preprocessing: Tokenized using Hugging Face's AutoTokenizer, padded to max length of 128

🏋️‍♂️ Training Setup

Model: bert-base-uncased
Task: Multi-label text classification
Loss: BCEWithLogitsLoss
Optimizer: AdamW
Batch Size: 16
Epochs: 5
Learning Rate: 1e-5
Evaluation Strategy: Hold-out test set (20%)

🚀 Intended Use

This model is designed to support AI-based unobtrusive assessment of cognitive load in education, enabling:

Researchers to monitor how students respond cognitively and emotionally to AI tutors
Developers to build more adaptive, trustworthy AI learning agents
Teachers to gain insight into student engagement and overload without invasive devices

📌 Limitations

The dataset size is modest (N=160), and model generalization to other domains or age groups is not guaranteed.
Labels are inferred from questionnaire-aligned criteria, which may include subjectivity.
The model does not currently handle out-of-distribution input or code-switching effectively.