metadata

pipeline_tag: table-question-answering
base_model:
  - ACakshay/all-mpnet-base-v2_joblib

Student Learning Style Identification Model

This repository contains a model designed to identify the learning styles of students based on various input features. The model is built to assist in personalizing educational experiences by classifying learning styles according to the Felder-Silverman Learning Style Model (FSLSM).

Model Description

The model is trained to identify the learning styles of students, which can help in designing personalized educational content and improving the learning experience. It uses features such as student responses, activities, and behaviors to predict their learning preferences, including sensory, visual, auditory, and kinesthetic learning styles.

Model Details

Model Type: Classification model (e.g., Random Forest, SVM, etc.)
Training Data: Data based on student learning activities, quizzes, self-assessments, etc.
Learning Style Categories:
- Active vs. Reflective
- Sensing vs. Intuitive
- Visual vs. Verbal
- Sequential vs. Global
Performance Metrics: (mention accuracy, precision, recall, or other relevant metrics here)

Installation

To use this model, make sure you have Python 3.x installed along with the necessary dependencies.

pip install transformers torch scikit-learn