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Constructing and Visualizing Models using Mime-based Machine-learning Framework

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Prospects and Challenges of Using Machine Learning for Academic Forecasting.

Edeh Michael Onyema¹, Khalid K Almuzaini², Fergus Uchenna Onu³

¹Department of Mathematics and Computer Science, Coal City University, Enugu, Nigeria.

Computational Intelligence and Neuroscience

|August 1, 2022

Summary

This summary is machine-generated.

Machine learning (ML) enhances academic forecasting for student performance and learning styles. Despite challenges like errors and data acquisition, ML offers promising solutions for educational decision-making.

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Area of Science:

Education
Computer Science
Data Science

Background:

Traditional forecasting methods in education have limitations.
Machine learning (ML) offers advanced capabilities for predicting academic events.
ML algorithms can analyze student performance and learning styles.

Purpose of the Study:

To examine the potential and challenges of ML in academic forecasting.
To highlight ML's role in improving educational decision-making.
To assess ML's application in predicting student academic achievements.

Main Methods:

Review of ML algorithms like K-nearest neighbor (KNN), random forest, artificial neural network (ANN), and Bayesian neural network (BNN).
Analysis of ML's application in predicting student learning behaviors and academic success.
Identification of limitations in ML deployment for academic forecasting.

Main Results:

ML algorithms bridge gaps in traditional forecasting, enabling timely decisions.
ML aids in early detection of at-risk students for targeted interventions.
ML applications show promise in enhancing academic forecasting accuracy.

Conclusions:

ML is a powerful technology for improving academic forecasting.
ML supports educational institutions in planning and decision-making.
ML has the potential to enrich the overall quality of education.