RSRFACG - A Framework for Recommendation System to Suggest the Course Grade using Filtering Technique and Association Rule Algorithm in Education Sector.
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| Title: | RSRFACG - A Framework for Recommendation System to Suggest the Course Grade using Filtering Technique and Association Rule Algorithm in Education Sector. |
|---|---|
| Authors: | Dol, Sunita M.1 sunita_aher@yahoo.com, Jawandhiya, P. M.2 pmjawandhiya@gmail.com, Laddha, Shilpa S.3 kabrageca@gmail.com |
| Source: | Journal of Engineering Education Transformations. Jan2025 Special issue, Vol. 38 Issue 3, p114-126. 13p. |
| Subject Terms: | *Computer programming, Information filtering systems, Data structures, Recommender systems, Apriori algorithm |
| Abstract: | Recommendation system acts as a information filtering system that provide the suggestions to users based on many different factors. In the current study, a framework for recommendation system called CGRSFA is developed for recommending the grade of course in Education Sector. For this framework, semester-wise, year-wise and overall grade information of students' courses is stored in ten datasets. This framework uses real data gathered from university site related to Four Year Bachelor of Technology - Computer Science and Engineering Programme, counting on 8400 entries of 200 students and 42 courses. Relevant rules which indicates courses dependencies and courses prerequites for other courses are found using this framework for each dataset e.g. the meaning of the rule "Advanced_C_Concepts=A+ → Data_Structure=A+" is that if student receives 'A+' grade in Advanced C Concepts course then that student will received 'A+' grade in Data Structure course also as System Programming course is the prerequisite to the course Compiler Construction. Rules which are not relevant are irrelevant rules and such rules are discarded. Filtered Associator algorithm is used to find the correlation among the courses. In Filtered Associator algorithm, first the grade dataset is filtered using the filtering method - Reservoir sampling Algorithm to remove the data items from dataset that do not meet certain and then Apriori association rule algorithm is applied on the filtered dataset. This recommendation system is useful for instructor as well as students for improving academic performance. This system can also be used in MOOCs for recommending the course grade. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Engineering Education Transformations is the property of Rajarambapu Institute of Technology and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
| FullText | Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 185166936 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: RSRFACG - A Framework for Recommendation System to Suggest the Course Grade using Filtering Technique and Association Rule Algorithm in Education Sector. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Dol%2C+Sunita+M%2E%22">Dol, Sunita M.</searchLink><relatesTo>1</relatesTo><i> sunita_aher@yahoo.com</i><br /><searchLink fieldCode="AR" term="%22Jawandhiya%2C+P%2E+M%2E%22">Jawandhiya, P. M.</searchLink><relatesTo>2</relatesTo><i> pmjawandhiya@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Laddha%2C+Shilpa+S%2E%22">Laddha, Shilpa S.</searchLink><relatesTo>3</relatesTo><i> kabrageca@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Engineering+Education+Transformations%22">Journal of Engineering Education Transformations</searchLink>. Jan2025 Special issue, Vol. 38 Issue 3, p114-126. 13p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Computer+programming%22">Computer programming</searchLink><br /><searchLink fieldCode="DE" term="%22Information+filtering+systems%22">Information filtering systems</searchLink><br /><searchLink fieldCode="DE" term="%22Data+structures%22">Data structures</searchLink><br /><searchLink fieldCode="DE" term="%22Recommender+systems%22">Recommender systems</searchLink><br /><searchLink fieldCode="DE" term="%22Apriori+algorithm%22">Apriori algorithm</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Recommendation system acts as a information filtering system that provide the suggestions to users based on many different factors. In the current study, a framework for recommendation system called CGRSFA is developed for recommending the grade of course in Education Sector. For this framework, semester-wise, year-wise and overall grade information of students' courses is stored in ten datasets. This framework uses real data gathered from university site related to Four Year Bachelor of Technology - Computer Science and Engineering Programme, counting on 8400 entries of 200 students and 42 courses. Relevant rules which indicates courses dependencies and courses prerequites for other courses are found using this framework for each dataset e.g. the meaning of the rule "Advanced_C_Concepts=A+ → Data_Structure=A+" is that if student receives 'A+' grade in Advanced C Concepts course then that student will received 'A+' grade in Data Structure course also as System Programming course is the prerequisite to the course Compiler Construction. Rules which are not relevant are irrelevant rules and such rules are discarded. Filtered Associator algorithm is used to find the correlation among the courses. In Filtered Associator algorithm, first the grade dataset is filtered using the filtering method - Reservoir sampling Algorithm to remove the data items from dataset that do not meet certain and then Apriori association rule algorithm is applied on the filtered dataset. This recommendation system is useful for instructor as well as students for improving academic performance. This system can also be used in MOOCs for recommending the course grade. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Engineering Education Transformations is the property of Rajarambapu Institute of Technology and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 114 Subjects: – SubjectFull: Computer programming Type: general – SubjectFull: Information filtering systems Type: general – SubjectFull: Data structures Type: general – SubjectFull: Recommender systems Type: general – SubjectFull: Apriori algorithm Type: general Titles: – TitleFull: RSRFACG - A Framework for Recommendation System to Suggest the Course Grade using Filtering Technique and Association Rule Algorithm in Education Sector. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Dol, Sunita M. – PersonEntity: Name: NameFull: Jawandhiya, P. M. – PersonEntity: Name: NameFull: Laddha, Shilpa S. IsPartOfRelationships: – BibEntity: Dates: – D: 02 M: 01 Text: Jan2025 Special issue Type: published Y: 2025 Identifiers: – Type: issn-print Value: 23492473 Numbering: – Type: volume Value: 38 – Type: issue Value: 3 Titles: – TitleFull: Journal of Engineering Education Transformations Type: main |
| ResultId | 1 |
