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Predictions Algorithms in Educational Systems based on Student Performance


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1 MCA Department, P.E.S's Modern College of Engineering, Pune, India
     

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Now a student’s performance towards education is influenced through various factor. Proper motivation and guidance towards Students should increase. Factor and proper assessment abilities support for better performance. Thus the different techniques of data mining is used for increase the performances of the candidates. Identifying the performance of candidates most important research area .studies of educational data mining based on distinguish mining algorithms connected with different predictions techniques. Thus learners performance is suffered by distinguish parameters for example considers a learning environment, financial issues etc. the paper study based on environmental factors and institute factors for evaluating student performance.

Keywords

Data Mining, Educational Data Mining, Classification.
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  • Predictions Algorithms in Educational Systems based on Student Performance

Abstract Views: 521  |  PDF Views: 5

Authors

Padma Mishra
MCA Department, P.E.S's Modern College of Engineering, Pune, India
Vaishali B. Sangvikar
MCA Department, P.E.S's Modern College of Engineering, Pune, India

Abstract


Now a student’s performance towards education is influenced through various factor. Proper motivation and guidance towards Students should increase. Factor and proper assessment abilities support for better performance. Thus the different techniques of data mining is used for increase the performances of the candidates. Identifying the performance of candidates most important research area .studies of educational data mining based on distinguish mining algorithms connected with different predictions techniques. Thus learners performance is suffered by distinguish parameters for example considers a learning environment, financial issues etc. the paper study based on environmental factors and institute factors for evaluating student performance.

Keywords


Data Mining, Educational Data Mining, Classification.

References





DOI: https://doi.org/10.25089/%2FMERI%2F2018%2Fv12%2Fi2%2F182837