Page 171 - ICDEBI2018

International Journal of Trend in International Journal of Trend in Scientific Research and Development (IJTSRD)Research and Development (IJTSRD)
International Open Access JournalInternational Open Access Journal | www.ijtsrd.comwww.ijtsrd.com

ISSN No: 2456 ISSN No: 2456 - 6470 | Conference Issue – ICDEBIICDEBI-2018

INTERNATIONAL CONINTERNATIONAL CONFERENCE ON DIGITAL ECONOMY AND FERENCE ON DIGITAL ECONOMY AND

ITS IMPACT ON BUSINESS AND INDUSTRYTS IMPACT ON BUSINESS AND INDUSTRYTS IMPACT ON BUSINESS AND INDUSTRY

Organised By: V. P. Institute of Management Studies & Research, SangliOrganised By: V. P. Institute of Management Studies & Research, SangliOrganised By: V. P. Institute of Management Studies & Research, Sangli

Classification Technique Classification Technique for Predicting Learning Behavior g Learning Behavior of

Student Student in Higher Education

Mrs. Varsha. P. Desai
Assistant Professor, V. P. Institute of Management Studies & Research, Sangli, Maharashtra, IndiaV. P. Institute of Management Studies & Research, Sangli, Maharashtra, IndiaV. P. Institute of Management Studies & Research, Sangli, Maharashtra, India
Affiliated to Shivaji University, KolhapurAffiliated to Shivaji University, Kolhapur, Maharashtra, India

ABSTRACT
In education system it is very important to decide In education system it is very important to decide
learning behavior of students. Today there is huge behavior of students. Today there is huge suitable algorithm for getting suitable algorithm for getting optimum solution to the
competition in higher educational institutes. Quality competition in higher educational institutes. Quality problem is a challenging task in data mining.problem is a challenging task in data mining.
education is essential for facing new educational education is essential for facing new educational
challenges. Educational Data Mining is useful to challenges. Educational Data Mining is useful to Data mining plays vital role in education system. Data mining plays vital role in education system.
classify students according to their knowledge and classify students according to their knowledge and Predicting learning behavior of student is very critical Predicting learning behavior of student is very critical
learning behavior. It helps teachers to implement ehavior. It helps teachers to implement process. Learning behavior of student depend of process. Learning behavior of student depend of
different teaching methodology as per learning different teaching methodology as per learning different factors like gender,different factors like gender, family background,
behavior of student. Researcher used Naïve Bayes behavior of student. Researcher used Naïve Bayes location, age, interest, strength, weakness, culture, location, age, interest, strength, weakness, culture,
classification technique on training data set of classification technique on training data set of curriculum etc. Today education system creates curriculum etc. Today education system creates
students. Classification is a supervised learning students. Classification is a supervised learning tremendous carrier opportunities in the front of tremendous carrier opportunities in the front of
approach which categorized data into predefined rized data into predefined students. It is challenging work for teacher to provide students. It is challenging work for teacher to provide
classes. The implementation is carried out using C#. classes. The implementation is carried out using C#. education as per student need education as per student need and interest. Learning
Algorithm is implemented on set of multivalued Algorithm is implemented on set of multivalued student behavior is very essential for getting better student behavior is very essential for getting better
attributes to predict slow learner, average learner and attributes to predict slow learner, average learner and teaching outcome as well as student’s satisfaction. A teaching outcome as well as student’s satisfaction. A
fast learner students. The objective of researcher is to fast learner students. The objective of researcher is to Classification technique in data mining helps teachers Classification technique in data mining helps teachers
extract hidden knowledge from dataset for prediction nowledge from dataset for prediction to predict student behavior and selecting appropriate to predict student behavior and selecting appropriate
of learning behavior of student. teaching methodology to enhance teaching and logy to enhance teaching and
learning process.
KEYWORD: Training Training Dataset, Dataset, Supervised, Supervised,
Unsupervised, Machine learning, Data Mining.Unsupervised, Machine learning, Data Mining. II. Literature Review:
Researcher has gone through previous research related esearcher has gone through previous research related
R
I. INTRODUCTION to classification techniques in data mining. It is o classification techniques in data mining. It is
t
Data Mining is a process of discovering knowledge Data Mining is a process of discovering knowledge observed that, Naïve Bayes classification algorithm is observed that, Naïve Bayes classification algorithm is
from database. It is a technique to identify patterns identify patterns used for student’s performancused for student’s performance classification. Web
and determine relationship between objects in dataset. and determine relationship between objects in dataset. mining and multifactor analysis technique is mining and multifactor analysis technique is
[3]
Data mining motivates various applications in Data mining motivates various applications in implemented for prediction . Decision tree, Random
machine learning to learn from data. It consists of machine learning to learn from data. It consists of forest and Naïve Bayes theorem is used for forest and Naïve Bayes theorem is used for
many algorithms which are based on supervised and many algorithms which are based on supervised and classification of student behavior. Researcher evaluate classification of student behavior. Researcher evaluate
unsupervised learning. There are different techniques fferent techniques results of all three algorithms and results of all three algorithms and it is found that
of data mining like classification, clustering, of data mining like classification, clustering, Naïve Bayes method gives better results than other Naïve Bayes method gives better results than other
[4]
predictive analysis, association rule mining, sequence predictive analysis, association rule mining, sequence classification techniques. Naïve Bays algorithm is Naïve Bays algorithm is
mining, graph mining, regression and time series mining, graph mining, regression and time series implemented for slow Lerner prediction using python implemented for slow Lerner prediction using python
analysis etc. Selection and implementation of best tion and implementation of best

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