STUDENT PERFORMANCE  PREDICTION  MODEL  DEVELOPMENT USING  DATA MININING  TECHNIC: IN  CASE  OF  SELECTED  SUBJECT SCORES

ASCHENEK, KASSA

dc.contributor.author	ASCHENEK, KASSA
dc.date.accessioned	2022-03-07T07:35:12Z
dc.date.available	2022-03-07T07:35:12Z
dc.date.issued	2021-09
dc.identifier.uri	http://ir.bdu.edu.et/handle/123456789/13152
dc.description.abstract	Education is a key factor for achieving continuing economic progress. Many students are getting less in result many reasons. More academic institutes now store massive student educational and related data. A large number of students enter every year. So, the demanding growth of data in education sectors continues. Handling and analyzing such a high amount of raw data manually for performance evaluation creates dissatisfaction, boring and unsuccessful. Manually handling and evaluating such a big amount of data for performance evaluation leads to discontent, boredom, and failure. To put it another way, traditional methodologies are overly complex and difficult to analyze and evaluate. To solve these issues, we use data mining techniques, which are a set of machine learning algorithms, to examine the data.In other words automated discovery of previously unknown, valid, novel, useful, and understandable patterns in school databases.The study's major goal is to use data mining to create a predictive model for student academic performance in Amhara National Regional State's Awi zone Injibara and AgewMidir general secondary schools. This can greatly support policymakers, planners, and education providers working on the control of student performance. The methodology used for this research was a hybrid six-step CRISP Knowledge Discovery Process.The essential data were gathered from a school data warehouse created specifically for student result purposes, collected from 2009 to 2012 E.C. The researcher used two popular data mining algorithms (J48 Decision Trees and Naïve Bayes Classifier) to develop the predictive model using a larger dataset (5408 cases). The researcher used 10-fold cross-validation and percentage split test mode for data mining algorithms of the two predictive models for performance comparison purposes. The results indicated that the decision tree (J48 algorithm) is the better predictor with a prune d parameter of the tree of 10-fold cross-validation mode due to the nature of data which is categorical for which J48 is better. It has 95.37% accuracy on the given school dataset with j48 algorithm, Naïve Bayes Classifier came out to be the second with an accuracy of 91.76%. Keywords: Education, Student performance, Data mining, Classification, Weka, Decision Tree, Naive Bayesian	en_US
dc.language.iso	en_US	en_US
dc.subject	INFORMATION TECHNOLOGY	en_US
dc.title	STUDENT PERFORMANCE PREDICTION MODEL DEVELOPMENT USING DATA MININING TECHNIC: IN CASE OF SELECTED SUBJECT SCORES	en_US
dc.type	Thesis	en_US