Journal of Engineering Education Transformations

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Machine Learning based Tutor Ward System (MLTWS) for Cognitive Learning style Management


Affiliations
1 System Engineer, Tata Consultancy Services, Chennai, Tamil nadu., India
2 Department of Mechatronics Engineering, Thiagarajar College of Engineering, Madurai., India
3 Software Development Engineer, Alfa TKG, Chennai, Tamil nadu., India
4 Department of Electronics and Communication Engineering, KLN College of engineering, Madurai., India
     

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Many educations sector find it difficult to examine a student's performance on each assessment activities and provide feedback based on cognitive, reflective and psychomotor abilities. It is a difficult task for student tutors to individually assess each student's performance in each category (Remember, Understand, Apply, and Analyze) and provide feedback. As a result, a machine learning model is required to help tutors to evaluate the student performance and provide feedback to students and their parents. It serves as an extension of the student portal, allowing access to all information about students, including their assessment scores. A proposed model is to forecast individual student's and the entire class's strengths and weaknesses in single portal. This enables both teachers and students to adjust their teaching and learning methods as needed. This approach paved a way for tutors spent much more time with their slower learners, treated them with more compassion.

Keywords

Machine Learning, Cognitive Skills, Tutoring, Student Portal.
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  • Machine Learning based Tutor Ward System (MLTWS) for Cognitive Learning style Management

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Authors

Gautham K
System Engineer, Tata Consultancy Services, Chennai, Tamil nadu., India
Julius Fusic S
Department of Mechatronics Engineering, Thiagarajar College of Engineering, Madurai., India
Gurunandhan ADP
Software Development Engineer, Alfa TKG, Chennai, Tamil nadu., India
Sugumari T
Department of Electronics and Communication Engineering, KLN College of engineering, Madurai., India

Abstract


Many educations sector find it difficult to examine a student's performance on each assessment activities and provide feedback based on cognitive, reflective and psychomotor abilities. It is a difficult task for student tutors to individually assess each student's performance in each category (Remember, Understand, Apply, and Analyze) and provide feedback. As a result, a machine learning model is required to help tutors to evaluate the student performance and provide feedback to students and their parents. It serves as an extension of the student portal, allowing access to all information about students, including their assessment scores. A proposed model is to forecast individual student's and the entire class's strengths and weaknesses in single portal. This enables both teachers and students to adjust their teaching and learning methods as needed. This approach paved a way for tutors spent much more time with their slower learners, treated them with more compassion.

Keywords


Machine Learning, Cognitive Skills, Tutoring, Student Portal.

References