Through the last decades, Artificial Intelligence (AI) has revolutionized the field of education and transformed traditional teaching approaches. This study aimed to examine how university students adopt AI tools in their learning processes and the role of digital literacy (DL) in this process through the lens of the Technology Acceptance Model (TAM). In this context, this study measured the impact of DL on university students' acceptance of AI technologies and their intention to use such technologies in the future. The data was collected from university students (N = 154) at a university in Western Türkiye during the fall semester of 2023. Data collection was conducted using two separate online forms; the first form included items adapted from the Digital Literacy Scale developed by Bayrakçı and Narmanlıoğlu (2021) to measure digital literacy levels, while the second form included items adapted from the UTAUT study by Venkatesh et al. (2003). The hypothesis testing results showed that students with higher levels of DL perceived the usefulness and ease of use of AI tools more positively, which positively affected their intention to adopt AI-based tools. The study also found that perceived usefulness and ease of use were important in shaping students' attitudes and behavioural intentions towards AI. When students perceive AI as a valuable tool for learning and find it easy to interact with, they are more willing to use it. This study suggests that DL plays a significant role in the acceptance of AI-based tools among university students, and accordingly, the TAM is a practical and accurate model to explore students’ potential engagement with AI in the learning process.

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 mining methods. The data contains demographic information about students and their families, studying routines, behaviours of attending learning activities, and their epistemological beliefs about science. Thus, this research aimed to solve a classification problem, two-class (successful or unsuccessful according to the exam result) was tried to be estimated from the collected data. In the study, the prediction accuracy of the supervised classification algorithms were compared and it was defined which variables were effective in the formation of classes. When the prediction accuracy of machine learning algorithms was compared, the findings indicated that the Neural Network algorithm (98.6%) had the highest score. The information gain coefficient of the variables was examined to determine the factors affecting the prediction accuracy. It was revealed that demographic variables of the family, scientific epistemological beliefs of the student, study routines and attitudes towards some courses affected the classification. It can be concluded that there was a relationship between these variables and academic success. Studies on these variables will support students' academic success.

In this study, the relationship between high school students' test anxiety, academic procrastination behaviours, family attitudes and academic achievements were examined; the predicting of test anxiety, academic procrastination and family attitudes on the academic achievement was also investigated. The participants of the study were composed of 496 high school students. The end-of-term grade average of the students was defined as the criterion of their academic achievements. The students' test anxiety was measured by the Revised Test Anxiety Scale and the academic procrastination behaviours by the Academic Procrastination Scale. The family attitudes were measured by determining the frequency of the activities that support the academic achievement of the students. Data were analysed by correlation and regression methods. The findings indicate that there was a negative correlation between “achievement score” and “anxiety”, “test irrelevant thinking” and “academic procrastination” while there was a positive correlation between “achievement score” and “family attitude”. The findings also revealed that “tension”, “bodily symptoms” and “family attitude” significantly predicted the “end-of-term grade average” positively, while “worry”, “test irrelevant thinking” and “academic procrastination” significantly predicted the “achievement score” negatively. The findings were discussed in the light of literature along with the suggestions for further studies.

The purpose of this study is to examine the partial mediating effect of epistemological beliefs in the correlation between preferences towards educational philosophy and the curriculum design approach of pre-service teachers. For this purpose, pre-service teachers’ educational philosophy preferences, curriculum design orientation, and epistemological beliefs have been analyzed, and a model has been developed. Three different instruments have been implemented to randomly chosen 568 pre-service teachers in several departments in Necatibey Faculty of Education of Balıkesir University in Turkey. In the analyzing process, first, a measurement model has been developed, and compatibility of the model with real data has tested, second, Pearson correlation coefficients have calculated, and the levels have defined, third, t values have calculated for the direct effect between preference for educational philosophies and curriculum design orientation preference, and at last, the epistemological beliefs have added to the model, and the moderating role on them has calculated. Based on findings, it can be concluded that from the pre-service teachers perspective; there is a positive correlation between widely accepted educational philosophies, and curriculum design orientations and there is a positive correlation between epistemological beliefs in effort and contemporary educational philosophies. In line with that, there is a positive correlation between epistemological belief in effort, and student-oriented design and problem-oriented design. Besides educational philosophy preferences (P=.14) and epistemological beliefs (P=.18) have a separate effect on curriculum design orientation, and epistemological beliefs (CIs = - .029 ~ .588) do not have a mediating role on the effect of educational philosophy preferences.

In this study, a cluster analysis was performed by creating a data set from students' personality traits and academic procrastination behaviours. Correlation analysis was done to examine the relationship between the variables, and the characteristics of the formed clusters and the association of the clusters with the perceived socioeconomic status were examined. Cluster analysis is a simple and practical method for classifying a set of complex data based on certain variables and making them more meaningful and using the results as an aid to decision-making. Clustering algorithms handle such data effectively, making it more meaningful. Following the analysis, it was revealed that two clusters had formed. The first of the clusters includes 65.2 % of the sample population; the level of procrastination and the mean score of neurotic personality traits were calculated higher than the other cluster. The remaining part of the sample population (34.8 %) constitutes the second cluster. The mean scores of studying systematically habits and extroversion, agreeableness, conscientiousness, and openness to experience personality traits of the students forming this cluster are higher than the other cluster. No association was observed between the clusters and the perceived socioeconomic levels of the students. The distributions of socioeconomic levels within the clusters are similar to each other. When the correlations of these variables are examined; positive relationships were found between the level of procrastination and neurotic personality traits. Procrastination behaviour and neurotic personality traits were also negatively correlated with other variables.

Generative Artificial Intelligence (GenAI) has emerged as a transformative technology in recent years, fundamentally reshaping traditional pedagogical approaches and educational environments. The objective of this study was to investigate the adoption of AI tools by pre-service mathematics and science teachers in their learning processes, as well as to assess the influence of academic self-efficacy (ASE) on this adoption, framed through the Technology Acceptance Model (TAM). Specifically, this research evaluated the effect of ASE on the acceptance of AI technologies by pre-service mathematics and science teachers and their intentions to utilize these technologies in the future. Data were collected from a sample of 146 pre-service mathematics and 91 pre-service science teachers (N=237) at an educational faculty in a university located in Western Türkiye during the spring semester of 2024. The data collection employed two distinct instruments: the first instrument comprised items from the Academic Self-Efficacy Scale to assess levels of academic self-efficacy, while the second instrument included items adapted from the TAM, TAM2 (Technology Acceptance Model), and UTAUT (Unified Theory of Acceptance and Use of Technology) frameworks. The results of hypothesis testing indicated that pre-service teachers with elevated levels of ASE had a more favorable perception of the usefulness and ease of use of GenAI tools, which in turn positively influenced their intention to adopt AI-based technologies. Furthermore, the study revealed that perceived usefulness and ease of use significantly shaped pre-service teachers' attitudes and behavioral intentions toward AI. When pre-service teachers recognize GenAI as a beneficial learning resource and find it user-friendly, their willingness to engage with it increases. This study posits that ASE is a critical factor in the acceptance of GenAI-based tools among pre-service mathematics and science teachers, thereby affirming the TAM as a relevant and effective model for examining pre-service teachers' potential engagement with AI in educational contexts.