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Kevser Hava; Özgür Babayigit – Education and Information Technologies, 2025
In recent years, there has been a growing emphasis on integrating Artificial Intelligence (AI) applications in educational settings. As a result, it is essential to assess teachers' competencies in Technological, Pedagogical, and Content Knowledge (TPACK) as it pertains to AI and examine the factors that influence these competencies. This study…
Descriptors: Technological Literacy, Pedagogical Content Knowledge, Artificial Intelligence, Technology Integration
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Munise Seçkin Kapucu; I?brahim Özcan; Hülya Özcan; Ahmet Aypay – International Journal of Technology in Education and Science, 2024
Our research aims to predict students' academic performance by considering the variables affecting academic performance in science courses using the deep learning method from machine learning algorithms and to determine the importance of independent variables affecting students' academic performance in science courses. 445 students from 5th, 6th,…
Descriptors: Secondary School Students, Science Achievement, Artificial Intelligence, Foreign Countries
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Koyuncu, Ilhan – Journal of Curriculum and Teaching, 2020
This study aimed to examine the importance levels of mathematics-specific trend variables in PISA (Programme for International Student Assessment) 2003 and 2012 in predicting mathematics performance across years with a two-step analysis method. The sample of the study was 9703 Turkish students (N[subscript 2003]=4855 and N[subscript 2012]=4848)…
Descriptors: International Assessment, Foreign Countries, Secondary School Students, Achievement Tests
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Yildiz, Muhammed Berke; Börekci, Caner – Journal of Educational Technology and Online Learning, 2020
Education systems produce a large number of valuable data for all stakeholders. The processing of these educational data and making studies on the future of education based on the data reveal highly meaningful results. In this study, an insight was tried to be developed on the educational data collected from ninth-grade students by using data…
Descriptors: Grade Prediction, Academic Achievement, Artificial Intelligence, Grade 9
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Bozak, Ali; Aybek, Eren Can – International Journal of Contemporary Educational Research, 2020
The present study aims to determine which analysis technique-Artificial Neural Networks (ANNs) or Logistic Regression (LR) Analysis-is better at predicting the science literacy success of the 15-year Turkish students who participated in PISA research carried out in 2015 by using learning time spent on science, test anxiety, environmental…
Descriptors: Artificial Intelligence, Networks, Regression (Statistics), Achievement Tests
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Bahadir, Elif – Educational Sciences: Theory and Practice, 2016
The ability to predict the success of students when they enter a graduate program is critical for educational institutions because it allows them to develop strategic programs that will help improve students' performances during their stay at an institution. In this study, we present the results of an experimental comparison study of Logistic…
Descriptors: Regression (Statistics), Prediction, Academic Achievement, Predictor Variables
International Association for Development of the Information Society, 2012
The IADIS CELDA 2012 Conference intention was to address the main issues concerned with evolving learning processes and supporting pedagogies and applications in the digital age. There had been advances in both cognitive psychology and computing that have affected the educational arena. The convergence of these two disciplines is increasing at a…
Descriptors: Academic Achievement, Academic Persistence, Academic Support Services, Access to Computers