Research Article
Skill Progress Prediction Using Machine Learning Algorithms
@INPROCEEDINGS{10.4108/eai.28-4-2025.2357928, author={Vikas B and Thiyagarajan D and P Geetha and T Grace Shalini}, title={Skill Progress Prediction Using Machine Learning Algorithms}, proceedings={Proceedings of the 4th International Conference on Information Technology, Civil Innovation, Science, and Management, ICITSM 2025, 28-29 April 2025, Tiruchengode, Tamil Nadu, India, Part I}, publisher={EAI}, proceedings_a={ICITSM PART I}, year={2025}, month={10}, keywords={student performance prediction machine learning at-risk students educational intervention academic analytics}, doi={10.4108/eai.28-4-2025.2357928} }- Vikas B
Thiyagarajan D
P Geetha
T Grace Shalini
Year: 2025
Skill Progress Prediction Using Machine Learning Algorithms
ICITSM PART I
EAI
DOI: 10.4108/eai.28-4-2025.2357928
Abstract
It is crucial to a school that they predict the performance of their students and be able to shift their teaching strategies accordingly. With the ability to foresee academic outcomes, you will know which students will possibly require more help. Incorporating these techniques helps improve the learning process and outcomes. Provided that there is sufficient information regarding the student’s past academic records and appropriate education measures, performance prediction will ensure the necessary intervention is provided to facilitate course completion in a timely manner. Student performance prediction and AT-RISK student identification can and should be done through the application of machine learning techniques. Timely intervention can thus improve the educational outcomes for those students. Identifying the right features to include in machine learning models is crucial. Possible features which are academic performance measures, personal characteristics, psychological attributes, and prior education.
