DropGuardian
- 0 Collaborators
We have developed an early warning system for student dropouts using Intel® AI Analytics Toolkits, specifically Intel® DAL and MKL. This powerful solution enables educational institutions to identify potential dropout risks early on by analyzing student data efficiently and accurately. ...learn more
Project status: Published/In Market
Intel Technologies
oneAPI,
MKL
Overview / Usage
The idea is to develop an early warning system for student dropouts using Intel® AI Analytics Toolkits, its libraries.. The system will use machine learning algorithms to identify students who are at risk of dropping out of school. The system will be scalable, cost friendly and innovative, and it will help improve student retention rates and promote equitable access to quality education. The COVID-19 pandemic has highlighted the need for such a system, as student dropouts have become a major challenge faced by the education sector which in turn results in various other problems like drug addiction , poverty and many more. By leveraging the power of Intel® AI Analytics Toolkits and its libraries, we can develop a solution that can help address this challenge and hopefully solve it for greater good.
Methodology / Approach
Our methodology for solving the problem of early student dropouts involves data preprocessing, feature selection, model selection, model training and evaluation, fine-tuning and hyperparameter optimization, prediction and intervention, and continuous improvement. We preprocess the collected data by handling missing values, removing irrelevant features, and transforming categorical data into numerical representations. Feature selection techniques are employed to identify the most relevant features, reducing dimensionality and enhancing model efficiency. We select an appropriate machine learning model, train it using the preprocessed data, and evaluate its performance using various metrics. Hyperparameter optimization is conducted to fine-tune the model's performance. The trained model is then used for predicting student dropout and facilitating timely interventions. Continuous improvement is achieved by retraining and refining the model as new data becomes available and interventions are implemented, ensuring its long-term effectiveness.
Technologies Used
Intel AI Analytics Toolkit
Intel oneapi Data Analytics Library (OneDAL)
Intel Math Kernel Library (Intel MKL)