Improvement of relative accreditation methods based on data mining and artificial intelligence for higher education

Taştimur, Canan; Karaköse, Mehmet; Akın, Erhan

doi:10.1109/ithet.2016.7760736

Cited by 11 publications

(2 citation statements)

References 14 publications

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“…In recent years, as AI has been highly valued by governments of all countries and promoted to be an important part of national development strategies, AI and HE topics are favored by scholars of all countries. Tastimur, Canan (2016) believed that AI would help improve the validity and accuracy of HE quality evaluation [4]. Ozbey, Nigar (2017) believed that AI can provide useful help for HE to solve some complex problems, and analyzed the factors that AI affects college students' learning and identification process [5].…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of Artificial Intelligence in International Higher Education Field Based on Bibliometrics

Yanting Pan

2024

jes

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Artificial intelligence (AI), as a significant feature of modern development, has changed the form of education, especially with a significant impact on higher education (HE). Clarifying the integration and infiltration of HE and AI is of great significance for the future development of AI in HE. The Citespace is used to make a quantitative and qualitative analysis of the indicators such as authors, institutions, nationalities, disciplines, journals, and highly cited articles of AI research literature -located at HE included in the Web of Science core collection database. The results show that in recent years, AI in the field of HE has received extensive international attention, and the research results are in a growing trend. In the field of international HE, AI focuses on hot topics such as parameter design, deep learning, machine learning human-computer interaction and hybrid learning. And deep learning, learning space simulation design, learning effectiveness measurement, etc. will become an important development direction of international AI research in the field of HE.

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Section: Literature Reviewmentioning

confidence: 99%

Analysis of Artificial Intelligence in International Higher Education Field Based on Bibliometrics

Yanting Pan

2024

jes

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“…According to the paper Naïve Bayes technique produced more accurate results from the other techniques. To get faster and more efficient accreditation process Tastimur, Karakose, and Akin [15] performed an IT-based accreditation model for engineering education. They suggested 10 criterions and used Genetic Algorithm method to train k-Nearest Neighbor classifier.…”

Section: Literaturementioning

confidence: 99%

Classification of Students’ Mathematical Literacy Score Using Educational Data Mining: PISA 2015 Turkey Application

Şehitoğlu¹,

Serkan²,

Demir³

2022

Cumhuriyet Science Journal

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PISA 2015 mathematical literacy score of Turkey is 420 while the average score of all countries is 461. It is understood that; Turkish students’ PISA 2015 mathematical literacy score was lower than the average. The basic reasons for the below average score need to be truly examined and developmental activities should be revealed. The aim of this study is to classify students according to the factors affecting their mathematical literacy score and to reveal the effects of these factors in classification.The data of the study is obtained from 5895 students who participated in PISA 2015. In this study, we used Random Forest, Naïve Bayes Classifier, Logistic Regression, Decision Tree Algorithm and Discriminant Analysis as classifiers. According to the results, Random Forest method produced more accurate scores than other methods with 76.32% accuracy. We also calculated the correct classification rate and determined the factors that positively and negatively affect the classification with discriminant analysis. According to the discriminant analysis home possessions, information and computer technology resources at home and students' expected occupational status were the most positive effective variables on mathematical literacy score. On the other hand, family wealth possessions, student-related factors affecting school climate and anxiety have negative effect on mathematical literacy score.

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Artificial Intelligence a Disruptive Innovation in Higher Education Accreditation Programs: Expert Systems and AACSB

Chedrawi

Howayeck

2019

Lecture Notes in Information Systems and Organisation

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Improvement of relative accreditation methods based on data mining and artificial intelligence for higher education

Cited by 11 publications

References 14 publications

Analysis of Artificial Intelligence in International Higher Education Field Based on Bibliometrics

Analysis of Artificial Intelligence in International Higher Education Field Based on Bibliometrics

Classification of Students’ Mathematical Literacy Score Using Educational Data Mining: PISA 2015 Turkey Application

Artificial Intelligence a Disruptive Innovation in Higher Education Accreditation Programs: Expert Systems and AACSB

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