Computational thinking is a way of thinking that covers 21st century skills and includes new generation concepts such as robotics, coding, informatics and information construction. Computational thinking has reached an important point especially in the field of science in line with the rapid developments in technology. Robotics applications, software-based activities, STEM (Science, Technology, Engineering, Math) education and problem-based studies are some of the areas where this thinking is used. In this study, which is based on this point, it is aimed to develop a scale for computational thinking. Exploratory sequential design, one of the mixed research methods, was used in the study. First of all, a detailed literature review was conducted and needs analysis was carried out. This study consists of two stages. In the first stage, exploratory factor analysis was performed and analyzed with SPSS 23 program. In the second stage, confirmatory factor analysis was performed and analyzed with LISREL 9.2 program. As a result of the study, the goodness of fit indexes of the scale was found. According to this; X²/df value 1.81; NNFI value 0.97; NFI value 0.93; CFI value 0.98; RMR value 0.05; SRMR value 0.04; AGFI value 0.91 and GFI value was found to be 0.93. When the reliability values of the study were examined, Cronbach’s Alpha value was found to be 0.86. As a result of the research, a computational thinking scale consisting of 3 factors and 30 items was developed. This scale was developed for prospective teachers and can be used at all levels of prospective teachers.

This study aimed to develop an instructional design that focuses on programming teaching for gifted and talented students and to investigate its effects on the teaching process. During the development of the instructional design; the steps of Morrison, Ross and Kemp Instructional Design Model were followed. Embedded experimental design, one of the mixed-method research designs, was used in the modeling of the study. The participants consisted of students studying at the Science and Art Center (BILSEM) (experimental group: 13 girls and 12 boys, control group: 10 girls and 15 boys). While the instructional design developed by the researchers was applied to the gifted and talented students in the experimental group, the standard activities used in Information Technologies and Software Courses at BILSEM were applied to the gifted and talented students in the control group. “Computational Thinking Scale (CTS)”, “Torrance Creative Thinking Test (TCTT-Figural)” and “Computer Programming Self-Efficacy Scale (CPSES)” were used to collect the data of the quantitative phase of the study. Qualitative data were gathered by using interview form, observation forms, and design thinking rubric. Two-Factor ANOVA Test, Bonferroni Adjustment Multiple Comparisons Test, and interaction graphs were used to analyze quantitative data while qualitative data were analyzed by content analysis. The quantitative results of the research showed that the instructional design was effective on students' computational thinking and creative thinking skills, but not on programming self-efficacy. Qualitative findings revealed that the instructional design helped the students learn the computational concepts, use computational applications, and develop computational-perspectives. Also, students improved their design thinking skills to a certain level and expressed that they enjoyed the design thinking process, learned the course content, and experienced some difficulties.

The study's goal was to examine the correlation between the computer programming self-efficacy and computational thinking skills of gifted and talented students. The capacity of the computer programming self-efficacy of gifted and talented students to predict their computational thinking skills were also examined. The relational screening model has been implemented in the research. The participants of the study were composed of 106 secondary school gifted and talented students studying the Individual Ability Recognition Program (IAR) at the Science and Art Center in the city center. Typical case sampling was applied for the student identification of the participants, 46 are female and 60 are male. Gifted and talented students' computational thinking skills were assessed using the "Computational Thinking Skills Scale” and the computer programming self-efficacy was measured by using the "Computer Programming Self-Efficacy Scale". Data were analysed by Pearson correlation analysis and simple linear regression analysis in statistical software SPSS 22. Research results found that there was a positive and high correlation between the computer programming self-efficacy and computational thinking skills. The gifted and talented students' computer programming self-efficacy demonstrated 31.5% of the total variance in computational thinking skills. This finding supports the claim which is present in the literature that self-efficacy in computer programming is the affective aspect of computational thinking skills. To predict computational thinking skills, it may be recommended to build multiple models for cognitive and affective skills of gifted and talented students.

Knowledge-based economy is an economic model students need to be prepared for a future economic model that uses knowledge as its main resource. Therefore, this study developed and validated instruments for constructing knowledge-based economy readiness among undergraduate students. This study used an online questionnaire with 120 respondents of economic education students in educational universities in East Java, Indonesia, for exploratory factor analysis and 417 respondents for confirmatory factor analysis. Then, statistical analysis was conducted using exploratory factor analysis in SPSS and confirmatory factor analysis in AMOS. This study first developed five factors of knowledge of economics, readiness for economic challenges, readiness for education, readiness for infrastructure, and readiness for innovation, consisting of 27 items. However, one item was removed because the loading factor was below .50. Consequently, 26 items were retained because the loading factor was significantly greater than .50. The Cronbach's alpha value for each item of the knowledge-based economy readiness construct was >.60 and met all goodness of fit index criteria, which means that it meets the requirements and can measure the construct of knowledge-based economy readiness. Since this study meets the validity and reliability requirements of the constructs leading to knowledge-based economy readiness, these results will help students prepare for the current and future knowledge-based economy. They can be used in developing economic education curricula in higher education.