RS_RFACG – A Framework for Recommendation System to Suggest the Course Grade using Filtering Technique and Association Rule Algorithm in Education Sector

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.