| --- |
| language: |
| - en |
| license: apache-2.0 |
| tags: |
| - text-classification |
| - student-stress |
| - mental-health |
| - education |
| - tensorflow |
| - keras |
| pipeline_tag: text-classification |
| metrics: |
| - accuracy |
| - f1 |
| --- |
| |
| # Student Stress Prediction Model |
|
|
| ## Model Summary |
|
|
| This model predicts stress levels in students based on behavioral, academic, and psychological indicators. It was developed as part of research into the implications of AI on student stress — supporting a forthcoming peer-reviewed publication on the topic. |
|
|
| The model is built with TensorFlow/Keras and is designed for binary or multi-class classification of student stress levels from structured survey/feature data. |
|
|
| --- |
|
|
| ## Model Details |
|
|
| - **Developed by:** Chandrasekar Adhithya Pasumarthi ([@Adhithpasu](https://github.com/Adhithpasu)) |
| - **Affiliation:** Frisco ISD, TX | AI Club Leader | Class of 2027 |
| - **Model type:** Deep Neural Network (TensorFlow/Keras) |
| - **Language:** English |
| - **License:** Apache 2.0 |
| - **Related research:** *"AI's Implications on Student Stress Levels"* — forthcoming publication (expected April 2026) |
| - **Related prior work:** *"Comparing Vision Transformers and Convolutional Neural Networks: A Systematic Analysis"*, JCSTS Vol. 8(2), January 2026 |
|
|
| --- |
|
|
| ## Intended Uses |
|
|
| **Direct use:** |
| - Predicting stress risk levels in students based on academic and behavioral features |
| - Supporting school counselors or ed-tech platforms in early identification of at-risk students |
| - Research into AI-driven mental health monitoring in educational settings |
|
|
| **Out-of-scope use:** |
| - Clinical diagnosis of mental health conditions |
| - Use without informed consent from students |
| - Deployment in high-stakes decision-making without human oversight |
|
|
| --- |
|
|
| ## Training Data |
|
|
| The model was trained on a student stress dataset containing features such as: |
| - Academic performance indicators (GPA, study hours, deadlines) |
| - Behavioral signals (sleep patterns, extracurricular load) |
| - Self-reported psychological indicators (anxiety levels, social pressure) |
|
|
| > **Note:** Dataset details and preprocessing steps are documented in the associated research paper. |
|
|
| --- |
|
|
| ## Evaluation |
|
|
| | Metric | Value | |
| |-----------|--------| |
| | Accuracy | TBD | |
| | F1 Score | TBD | |
| | Precision | TBD | |
| | Recall | TBD | |
|
|
| *(Fill in with your actual results before publishing)* |
|
|
| --- |
|
|
| ## How to Use |
|
|
| ```python |
| import tensorflow as tf |
| import numpy as np |
| |
| # Load the model |
| model = tf.keras.models.load_model("student_stress_model") |
| |
| # Example input — replace with your actual feature vector |
| # Features: [study_hours, sleep_hours, gpa, extracurriculars, anxiety_score, ...] |
| sample_input = np.array([[6.0, 5.5, 3.8, 3, 7]]) |
| |
| # Predict |
| prediction = model.predict(sample_input) |
| stress_label = np.argmax(prediction, axis=1) |
| |
| labels = {0: "Low Stress", 1: "Moderate Stress", 2: "High Stress"} |
| print(f"Predicted stress level: {labels[stress_label[0]]}") |
| ``` |
|
|
| --- |
|
|
| ## Model Architecture |
|
|
| ``` |
| Input Layer → Dense(128, relu) → Dropout(0.3) |
| → Dense(64, relu) → Dropout(0.2) |
| → Dense(num_classes, softmax) |
| ``` |
|
|
| *(Update this section to match your actual architecture)* |
|
|
| --- |
|
|
| ## Limitations & Bias |
|
|
| - The model is trained on a specific student population and may not generalize across different demographics, school systems, or cultures. |
| - Self-reported features are subject to response bias. |
| - Stress is a complex, multifactorial construct — model predictions should be treated as probabilistic indicators, not ground truth. |
|
|
| --- |
|
|
| ## Ethical Considerations |
|
|
| Student mental health data is sensitive. This model is intended for research and educational tool development only. Any real-world deployment should: |
| - Obtain explicit informed consent from students (and guardians for minors) |
| - Be reviewed by a qualified mental health professional |
| - Comply with FERPA and applicable data privacy regulations |
|
|
| --- |
|
|
| ## Citation |
|
|
| If you use this model in your research, please cite: |
|
|
| ```bibtex |
| @misc{pasumarthi2026studentstress, |
| author = {Chandrasekar Adhithya Pasumarthi}, |
| title = {Student Stress Prediction Model}, |
| year = {2026}, |
| publisher = {Hugging Face}, |
| url = {https://huggingface.co/Adhithpasu/student-stress-prediction} |
| } |
| ``` |
|
|
| --- |
|
|
| ## Contact |
|
|
| - GitHub: [@Adhithpasu](https://github.com/Adhithpasu) |
| - Research: See linked publication for full methodology and dataset details. |
