Using Artificial Intelligence Methods to Predict Student Academic Achievement

dc.authorscopusid 57351974700
dc.authorscopusid 57213371849
dc.contributor.author Al-Khafaji, Mustafa
dc.contributor.author Eryilmaz, Meltem
dc.contributor.other Computer Engineering
dc.date.accessioned 2024-07-05T15:16:21Z
dc.date.available 2024-07-05T15:16:21Z
dc.date.issued 2022
dc.department Atılım University en_US
dc.department-temp [Al-Khafaji, Mustafa; Eryilmaz, Meltem] Atilim Univ, Comp Engn Dept, Ankara, Turkey en_US
dc.description.abstract This study applies two artificial intelligence methods represented by both the neural network and fuzzy logic to predict student achievement in the exam. The dataset used in this study was taken from an Iraqi engineering college and it represents data of 200 students who have enrolled in the computer science course. Gender, age, resources downloaded, videos viewed, discussion chat joined, exam scores used as the data set. The type of artificial neural network used was pattern neural network. Levenberg-Marquardt's algorithm was used to train the neural networks. On the other hand Sugeno fuzzy inference system was used for the fuzzy logic. The study results showed that the students who spend more time on the learning system have the most success rate. According to the results the neural network accuracy rate 73% and the fuzzy was 88%. This high accuracy rates support that artificial intelligence methods can be used to predict student academic achievement. en_US
dc.identifier.citationcount 0
dc.identifier.doi 10.1007/978-3-030-89880-9_31
dc.identifier.endpage 414 en_US
dc.identifier.isbn 9783030898809
dc.identifier.isbn 9783030898793
dc.identifier.issn 2367-3370
dc.identifier.issn 2367-3389
dc.identifier.scopus 2-s2.0-85119860706
dc.identifier.scopusquality Q4
dc.identifier.startpage 403 en_US
dc.identifier.uri https://doi.org/10.1007/978-3-030-89880-9_31
dc.identifier.uri https://hdl.handle.net/20.500.14411/1630
dc.identifier.volume 359 en_US
dc.identifier.wos WOS:000793730500031
dc.institutionauthor Eryılmaz, Meltem
dc.language.iso en en_US
dc.publisher Springer international Publishing Ag en_US
dc.relation.ispartof 6th Future Technologies Conference (FTC) -- OCT 28-29, 2021 -- ELECTR NETWORK en_US
dc.relation.ispartofseries Lecture Notes in Networks and Systems
dc.relation.publicationcategory Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 2
dc.subject Artificial intelligence en_US
dc.subject Artificial neural network en_US
dc.subject Fuzzy logic en_US
dc.subject e-Learning en_US
dc.title Using Artificial Intelligence Methods to Predict Student Academic Achievement en_US
dc.type Conference Object en_US
dc.wos.citedbyCount 4
dspace.entity.type Publication
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