Modern Education: Advanced Prediction Techniques for Student Achievement Data

Enhancing educational outcomes across varied institutions like universities, schools, and training centers necessitates accurately predicting student performance. These systems aggregates the data from multiple sources—exam centers, virtual courses, registration departments, and e-learning platforms. Analyzing this complex and diverse educational data is a challenge, thus necessitating the application of machine learning techniques. Utilizing machine learning algorithms for dimensionality reduction simplifies intricate datasets, enabling more comprehensive analysis. Through machine learning, educational data is refined, uncovering valuable patterns and forecasts by simplifying complexities via feature selection and dimensionality reduction methods. This refinement significantly amplifies the efficacy of student performance prediction systems, empowering educators and institutions with data-driven insights and thereby enriching the overall educational landscape. In this particular research, the Decision Tree Classification (DTC) model is used for forecasting student performance. DTC stands out as a potent machine-learning method for classification purposes. Two optimization algorithms, namely the Fox Optimization (FO) and the Black Widow Optimization (BWO), are integrated to heighten the model's accuracy and efficiency further. The amalgamation of DTC with these pioneering optimization techniques underscores the study's dedication to harnessing the forefront of machine learning and bio-inspired algorithms, ensuring more precise and resilient predictions of student performance, ultimately culminating in improved educational outcomes. From the results garnered for G1 and G3, it is evident that the DTBW model demonstrated the most exceptional performance in both predicting and categorizing G1, achieving an Accuracy and Precision value of 93.7 percent. Conversely, the DTFO model emerged as the most precise predictor for G3, achieving an Accuracy and Precision of 93.4 and 93.5 percent, respectively, in the prediction task.

@article{LU2024,
  title     = {Modern Education: Advanced Prediction Techniques for Student Achievement Data},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {1},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Xi LU},
  doi       = {10.14569/IJACSA.2024.01501126},
  url       = {https://doi.org/10.14569/IJACSA.2024.01501126}
}