Assist. Prof. Dr. Görkem CEYHAN
Biyografi
Dr. Ceyhan graduated from the Department of Mathematics at Fırat University in 2005. In 2010, he began working as a research assistant in the Department of Measurement and Evaluation in Education at Muş Alparslan University. He completed his master’s degree at Van Yüzüncü Yıl University, where his thesis examined university students’ reflective thinking levels using the CART algorithm. Dr. Ceyhan earned his PhD from Gazi University, where he compared the classification performance of data mining algorithms using PISA data. He has presented papers and published articles in the field of data mining. He has also taken part in projects funded by the Ministry of National Education (MoNE), TÜBİTAK, UNICEF, the European Union, and YEŞİLAY. His work focuses on measurement and evaluation, with both national and international contributions. He currently serves as an Assistant Professor at Muş Alparslan University.
Educational Data Mining: Methods, Applications, and Research Perspectives
This workshop, designed for academics and graduate researchers interested in the field of educational data mining, aims to provide a holistic learning experience that integrates theoretical knowledge with practical application. Within the scope of the workshop, widely used methods for deriving meaningful insights from educational data will be addressed, focusing on decision trees, artificial neural networks, logistic regression, and support vector machines.
Participants will gain a systematic understanding of the types of research questions these methods address, when they should be preferred, their advantages and limitations, and their applications in the field of education. In addition, discussions will be conducted through example scenarios related to current application areas such as predicting student achievement, identifying at-risk students, learning analytics, and data-driven decision-making in assessment and evaluation processes.
The workshop aims to provide participants with practical insights into how they can position data mining techniques within their own research, while also fostering a perspective that contributes to interdisciplinary studies.