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Today, most educational institutions have become more interested in big data. Because the importance of extracting useful information from educational data to support decision-making on educational issues has increased day by day. In this context, through educational data mining, this research study aims to reveal the association rules among compulsory courses in the Computer Education and Instructional Technology curriculum within the faculty of education of a state university in Turkey. In this context, the research was conducted with data obtained from 258 preservice teachers who had completed all of their compulsory courses (n = 42) for the Computer Education and Instructional Technology curriculum, having graduated from the Computer Education and Instructional Technology program between 2012 and 2020. According to the experimental results, the academic performance of preservice teachers in some courses could be used as a predictor of their academic performance in other courses. Other findings from the study are discussed in detail, and suggestions put forth for future research. [ABSTRACT FROM AUTHOR]

While infectious diseases were the biggest health threats in the past, both non-communicable diseases and their risk factors have become the biggest health threats today. The elderly are more affected by non-communicable diseases than the young, and they face the problem of multimorbidity. In this context, the aim of the study is to evaluate 1) the prevalence of noncommunicable diseases and risk factors, 2) the number of multimorbidities/risk factors of 2,458 individuals aged 65 and over who participated in the Turkish Health Survey (THS) conducted by the Turkish Statistical Institute (TURKSTAT) in 2019, and 3) to examine the multimorbidity/risk factor patterns by association rules analysis and social network analysis. According to the results of the research, low back and neck disorders and diabetes are diseases with a higher prevalence compared to other diseases. It is determined that lack of physical activity, obesity/overweight, and hypertension are risk factors with higher prevalence compared to other risk factors. Fifty-one percent of the individuals have multimorbidity and 64% have 2-4 risk factors. According to the results of association rules analysis and social network analysis, low back and neck disorders are the most co-existing non-communicable diseases. Among the risk factors, obesity/overweight and lack of physical activity are the most co-existing risk factors. Non-communicable diseases, multimorbidity, and risk factors are important health problems for elderly individuals in Turkey, and policymakers and health care managers should take the necessary precautions. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the university students' usage of smartphones in-class and reveal the relationships among the frequency of use, the purpose of use, reasons for the smartphone using in course, and demographic variables (gender and class). To achieve this aim, we used the correlational survey model. The study sample was selected from the students studying in an Education Faculty of a University in the east of Turkey in the spring semester of 2019-2020 academic year. The sample determined by the snowball sampling method consisted of a total of 577 students. The survey technique was used to collect data. The descriptive and relation analysis were used in the analysis of the data. Frequency and percentage calculations were made for descriptive analysis. For relational analysis, the association rule, one of the data mining methods, was used. It has been revealed that most of the students use their smartphones occasionally and mostly in messages, to do research and connect to social networks. It was observed that the main reasons that motivated students to use their smartphones in the courses were doing research about the course and using them in emergencies. Besides, students gave close answers about the effect of smartphone use on academic performance, and most of the students stated that they did not have difficulty focusing when using smartphones in the courses. Some students also stated that they did not use the phone as it distracted them, and it was forbidden to use it in the course. Results revealed that the boring course, negative attitudes towards the lecturer, and the course cause students to message and connect to social media. It was concluded that the occasional use of smartphones in the courses in cases of emergency and messaging does not affect students' academic performance and does not cause any problems in focusing on the course. Besides, most people who use their smartphones for researching, reading news, and messaging in the courses stated that they use their smartphones for educational purposes. Some suggestions were developed at the end of the study based on the results. [ABSTRACT FROM AUTHOR]

Data Mining is used to describe the totality of techniques which aim to find the unexplored patterns in a set of data. The purpose of data mining is to create models of decision-making devoted to estimations of future behavior based on analysis of past activities. In this study the shopping data of the customers of an authorized service, operating in the automative sector in Turkey, were analyzed using Apriori and FPGrowth Algorithms. This way, it is observed which products were purchased together by customers and in line with this observation, campaigns and promotions were given a direction to increase the profit. [ABSTRACT FROM AUTHOR]

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