Acta Scientific Computer Sciences

Research Article Volume 4 Issue 5

An Intuitive Implementation of Course Recommendation System Based on Learner's Personality

Asad Jatoi, Memoona Sami*, Muzamil Nawaz and Junaid Baloch

Department of Software Engineering, Mehran University of Engineering and Technology, Jamshoro, Pakistan

*Corresponding Author: Memoona Sami, Department of Software Engineering, Mehran University of Engineering and Technology, Jamshoro, Pakistan.

Received: March 16, 2022; Published: April 20, 2022

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Abstract

The work covered in this study concerns learner classification; a learner can be divided into numerous groups based on how comfortable he or she is with the course. In a broader sense, a learner can be theoretical, practical, or hybrid: a mix of the two. The research will use the Nave Bayes method to determine the degree of excellence of learners in their recognized learning style in a particular competency once the learner's type has been determined. The level of quality is divided into four categories: exceptional, good, medium, and fair. Recommendation will be given to the student for further improvements within the skillset or for newer skills once the classification of the learner in a category has been determined, as well as his or her level of proficiency in the area. The study places a greater emphasis on determining a learner's learning style because it is so important to the study. The research's main building block is the identification of learning styles, as the second portion of the research's recommendation is entirely based on superior categorization results.


Keywords: Classification; Algorithm; Recommendation System; Naïve Bayes; Learning Style

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Citation

Citation: Memoona Sami., et al. “An Intuitive Implementation of Course Recommendation System Based on Learner's Personality". Acta Scientific Computer Sciences 4.5 (2022): 37-42.

Copyright

Copyright: © 2022 Memoona Sami., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




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Acceptance rate35%
Acceptance to publication20-30 days

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