REVIEW ON EVALUATION TECHNIQUES FOR BETTER STUDENT LEARNING OUTCOMES USING MACHINE LEARNING

Authors

  • Dr.K.Nagi Reddy Professor, Department of IT, Lords Institute of Engineering and Technology, Hyderabad Author
  • Mohd Zia Ul Rahman, Abhishek Saroj, Mohd B.E. Student, Department of IT, Lords Institute of Engineering and Technology, Hyderabad Author

Abstract

This paper reviews student learning outcomes based on various evaluation parameters critical to the
education system. It considers student learning outcomes alongside factors such as learner engagement, use of
learning strategies, teacher experience, motivational beliefs, and technology in learning. Examination and
evaluation are essential for measuring student learning outcomes. Classification algorithms like Decision Trees,
Naïve Bayes, and Support Vector Machines aid in categorizing student performance, facilitating ongoing
monitoring of their progress. Machine learning techniques are employed to determine whether learning outcomes
have been achieved. Regular assessment of student learning is crucial to accurately measure true learning
outcomes. Once assessed regularly, aggregation of learning outcomes should be conducted to summarize the overall
course learning outcomes.

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Published

2024-09-30

Issue

Vol. 14 No. 7 (2024): September

Section

Articles

How to Cite

REVIEW ON EVALUATION TECHNIQUES FOR BETTER STUDENT LEARNING OUTCOMES USING MACHINE LEARNING. (2024). INTERNATIONAL JOURNAL OF MANAGEMENT RESEARCH AND REVIEW, 14(7), 64-75. https://ijmrr.com/index.php/ijmrr/article/view/273

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