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Artificial Neural Network Model for Prediction of Students’ Success in Learning Programming

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Title Artificial Neural Network Model for Prediction of Students’ Success in Learning Programming
 
Creator Stanković, Nebojša Ljubomir
Blagojević, Marija Dragovan
Papić, Miloš Željko
Karuović, Dijana Ivan
 
Subject ANN
Knowledge acquisition
Knowledge test
Programming skills
Web-based application
 
Description 249-254
The model for predicting students’ success in acquiring programming knowledge and skills is presented in this paper. In order to collect the data needed for development of the model, 159 undergraduate IT students from Faculty of Technical Sciences in Čačak were analyzed. Besides the score on programming knowledge test, the following data were also gathered for each student: high school, the subject he/she took at the entrance exam, size of student’s birthplace, average high school grade, points from high school, gender, previous education, existence of IT educational profile in high school, study year, percentage of attendance on classes, reason for enrolment, subjective assessment of preparedness for programming, solving sequential tasks, type of programming student prefers, subjective assessment of preparedness for working in industry, solving tasks with branching and cycle, solving complex tasks, knowledge level, formal education, informal education, Kolb's learning style. In order to predict students’ success in learning programming multilayer perceptron was used with backpropagation learning algorithm. The cross-validation methodology was used for the training and testing of the classifiers. Transformation process is performed on the points students achieved on the test in order to get three categories related to success. Based on the results about the relevance of the parameters, the model reached an accuracy of 92.3%. In order to facilitate the use of the model, a Web-based application for displaying the results was created. It is primarily intended for teachers with no experience in working with neural networks, who can use it for planning the teaching.
 
Date 2021-03-11T06:15:18Z
2021-03-11T06:15:18Z
2021-03
 
Type Article
 
Identifier 0975-1084 (Online); 0022-4456 (Print)
http://nopr.niscair.res.in/handle/123456789/56472
 
Language en_US
 
Rights CC Attribution-Noncommercial-No Derivative Works 2.5 India
 
Publisher NISCAIR-CSIR, India
 
Source JSIR Vol.80(03) [March 2021]