Online Learning and Analytics

The current research provides a thorough exploration into different methods of machine learning used to predict educational performance using a variety of data sources. This research studies methods for predicting academic performance and displays the difference in performance of each model, including performance meas…

• AYÇA AKIN

Artificial Intelligence (AI) integration in online education has transformed student motivation, interaction, collaboration, and personalized learning. This qualitative systematic review analyzed 66 empirical studies (2020–2025) from Web of Science and Google Scholar using thematic analysis. Findings show that AI enha…

Abstract The predictive modeling of student achievement in national education systems faces a persistent conflict between the need for large-scale data utility and the stringent requirements of student data privacy. This study addresses this challenge through a high-performance personalized federated learning framewor…

The rapid adoption of artificial intelligence tools in education has outpaced systematic risk assessment development. Teachers lack practical frameworks to evaluate potential harms in classroom applications. While UNESCO, OECD, the EU AI Act, and NIST provide institutional guidelines, these remain inadequately operati…

The rapid advancement and widespread adoption of artificial intelligence (AI) technologies in higher education has made visible the question of what role these tools play in educational processes. In particular, international students, who study outside their home countries and often face various adaptation challenges…

This study examines preservice teachers' experiences using generative AI tools for curriculum-aligned question design. A qualitative phenomenological design was employed with 32 preservice teachers at a public university during 2024–2025. Following a training session on ChatGPT and Google Gemini, participants generate…

Background Information regarding the prediction of dentistry students’ academic performances using machine learning (ML) techniques is lacking. The objective of this study was to predict the academic performance of pre‐clinical dentistry students using ML techniques based on their pre‐university information. Methods A…

Optical forms, which are widely used assessment tools in education, have the potential to reflect students' cognitive, sensory, and behavioral characteristics. This study aims to go beyond the traditional use of Optical Mark Recognition (OMR) systems by examining students’ marking behaviors as a predictor of academic…

Open high schools fulfill a critical function by offering flexible educational pathways for students encountering diverse socioeconomic and personal challenges. Nevertheless, escalating dropout rates represent a significant concern, necessitating robust predictive models to facilitate early interventions. This study e…

The Selection and Placement of Students to be Sent Abroad for Graduate Studies (YLSY) scholarship program is implemented by Türkiye to develop qualified human resources and foster scientific advancement. Based on this objective, the present study aims to evaluate the effectiveness of the program by identifying the fac…

The purpose of this study is to examine science teachers’ views on artificial intelligence (AI) technologies, their areas of use, and their pedagogical approaches. Conducted within a qualitative research design, the study involved semi-structured interviews with 24 science teachers from different educational levels, s…

Preservice teachers (PSTs) often have limited preparation for integrating artificial intelligence (AI) in pedagogically meaningful and ethically responsible ways, while teacher education programs face practical constraints in providing sustained, practice-based AI preparation. This exploratory single-case study examin…