| # 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**. | |
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| ## 📚 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 | |
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| ## 🏋️♂️ 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%) | |
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| ## 🚀 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 | |
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| ## 📌 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. | |