Title
Faktori učenja i predviđanje uspešnosti u programiranju primenom veštačkih neuronskih mreža
Creator
Stanković, Nebojša, 1966-
CONOR:
26252135
Copyright date
2021
Object Links
Select license
Autorstvo-Nekomercijalno-Bez prerade 3.0 Srbija (CC BY-NC-ND 3.0)
License description
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Language
Serbian
Cobiss-ID
Inventory ID
050003493
Theses Type
Doktorska disertacija
description
Datum odbrane: 27.12.2021.
Other responsibilities
Academic Expertise
Tehničko-tehnološke nauke
University
Univerzitet u Kragujevcu
Faculty
Fakultet tehničkih nauka
Alternative title
Factors of learning and predicting success in programming using artificial neural networks
Format
133 листa
Abstract ()
Academic education is one of the key areas in the process of modernization of a country. The
ability to predict success helps teachers identify students who have the potential to attend
advanced courses, as well as students who need additional education. In modern society
programming skills are becoming increasingly important. Many studies show that programming
is one of the critical skills of students' technological literacy. Therefore, there is a need to analyze a large amount of data on the basis of which factors that affect student performance in the field of programming can be predicted. In recent years, the application of artificial intelligence in education has increased significantly worldwide. Artificial neural networks
(ANN), as one of its tools, are experiencing numerous successful implementations.
In the doctoral dissertation Factors of learning and predicting success in programming using
artificial neural networks, the ANN model developed for the purpose of predicting the success of students in acquiring programming knowledge and skills is presented. 180 students of the study program Information Technology from the Faculty of Technical Sciences in Čačak were analyzed. Data on previous education were collected for each student.
Students' success in learning programming is measured through achievements on the
knowledge test and is classified into three categories: unsuccessful, moderately successful and
very successful. A three-layer ANN model based on a backpropagation learning algorithm was
used to predict student success.
19 models were created. The model with the best predictive accuracy (90,7%) was used as the
final model for implementation. A web application was created for that model, with the help of
which the teacher has the possibility of adapting the teaching, and more efficient organization
of the same, which leads to successfully mastered material.
Authors Key words
Success prediction, Programming, Knowledge test, Artificial intelligence,
Artificial neural networks, Web application
Classification
004.43+004.032.6]:37(043.3)
Type
Tekst
Abstract ()
Academic education is one of the key areas in the process of modernization of a country. The
ability to predict success helps teachers identify students who have the potential to attend
advanced courses, as well as students who need additional education. In modern society
programming skills are becoming increasingly important. Many studies show that programming
is one of the critical skills of students' technological literacy. Therefore, there is a need to analyze a large amount of data on the basis of which factors that affect student performance in the field of programming can be predicted. In recent years, the application of artificial intelligence in education has increased significantly worldwide. Artificial neural networks
(ANN), as one of its tools, are experiencing numerous successful implementations.
In the doctoral dissertation Factors of learning and predicting success in programming using
artificial neural networks, the ANN model developed for the purpose of predicting the success of students in acquiring programming knowledge and skills is presented. 180 students of the study program Information Technology from the Faculty of Technical Sciences in Čačak were analyzed. Data on previous education were collected for each student.
Students' success in learning programming is measured through achievements on the
knowledge test and is classified into three categories: unsuccessful, moderately successful and
very successful. A three-layer ANN model based on a backpropagation learning algorithm was
used to predict student success.
19 models were created. The model with the best predictive accuracy (90,7%) was used as the
final model for implementation. A web application was created for that model, with the help of
which the teacher has the possibility of adapting the teaching, and more efficient organization
of the same, which leads to successfully mastered material.
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