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.

This research aims to explore students' learning obstacles in solving fraction problems in elementary school. This qualitative research used a case study method. The research subjects involved 30 third-grade elementary school students from two different schools in Bandung City, Indonesia. The instruments utilized were test and non-test. The test technique was done by giving fractional material questions, while the non-test technique was in the form of interviews. The data collected were then analyzed employing the three stages of the Miles and Huberman model, including data reduction, data presentation, and drawing conclusions. The study findings revealed that students experienced epistemological obstacles, where students experienced limited knowledge, ranging from the basic concepts of fractions, fractional arithmetic operations, and fraction problem-solving. Based on these findings, teachers can construct learning designs with appropriate didactic situations as a follow-up to minimize the occurrence of similar learning obstacles in future fractional learning materials.

This research analyzed the involvement of teachers, parents, and school committees in improving scientific attitudes in science learning using Rasch model analysis. A survey method was used in this quantitative study. Participants in the study were selected using a purposive sample of 174 teachers, parents, and school committees in Sleman and Kebumen Regencies, Indonesia. A questionnaire was used in data collection to determine the involvement of teachers, parents, and school committees in improving scientific attitudes toward science learning. The questionnaires were completed using a Likert scale of 1-4, and the data were then analyzed using the Rasch model. The result showed that all participants were the average logit items (+1.03 logit). The reliability was 0.89, indicating a positive response to improving students' scientific attitudes. The results of the Rasch model analysis suggested that the involvement of parents, teachers, and school committees in improving scientific attitudes differed according to their roles. Each instrument element was analyzed in more detail in the Rasch model. Participants' roles were reflected in the specific involvements of teachers in learning, parents at home with children, and school committees participating in school policy-making.

This research aims to examine the effect of coding education on the analytical thinking skills of gifted students. The participants are 18 students, 11 to 12 years old. An embedded experimental mixed design was used in the research. The data collection was carried out with the Analytical Thinking Skill Scale to determine the difference in the analytical thinking skills of the study group before and after the coding training; the Analytical Thinking Skill Observation Form to determine the analytical thinking skill levels during the implementation process; and the semi-structured interview form to get their opinions on the coding training. The data were analyzed with a pre-test-post-test quasi-experimental design, descriptive analysis, and content analysis. The results show that coding education developed the participants' analytical thinking skills. The difference in the analytical thinking skills of the study group was not statistically significant in terms of gender. According to the students' views on the coding education application process, there was an improvement in the sub-dimensions of sorting, classification, comparison, and evaluation in analytical thinking skills; coding education developed problem-solving and thinking skills, was useful, encouraged students to choose a profession, and was entertaining, as well as negative opinions such as being difficult, boring, and requiring a lack of time.

The purpose of this study was to compile and statistically analyze the results of research studies that examined students' problem-solving skills in worksheets. The research method used was a meta-analysis. The study search was conducted from 2013 to 2022 in Google Scholar and the Garuda portal databases. The search yielded 40 studies that met the inclusion criteria for extraction from research and development, experimental, and quasi-experimental. From the extracted results, 45 comparisons of data were examined. Microsoft Excel was used to calculate the effect size of the problem-solving worksheet. This study yielded a value of 1.281 for the entire study, indicating that the worksheet had a significant and positive impact on students' problem-solving skills. The results indicate the need to develop worksheets to improve students' problem-solving skills.

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.

Many education and learning experts currently recommend the flipped classroom model as an alternative to learning after the COVID-19 pandemic. This study aims to explore the impact of the flipped classroom model on social skills and problem-solving skills for cadets. This research used a sequential mix method involving 50 maritime students in semester 7 of the Engineering Study Program at the Maritime Sciences Polytechnic Makassar, South Sulawesi, Indonesia. Researchers used two main instruments, namely problem-solving skill tests and interviews. Furthermore, in the quantitative analysis, the researcher ran paired sample t-tests and one-way Multivariate Analysis of Covariance (MANCOVA) using the SPSS 25.00 program. In addition, researchers also analysed qualitative data from interviews using thematic analysis techniques. The results showed that the flipped classroom model proved to have a positive effect on the problem-solving skills of maritime students. Other findings state that the cadets also respond positively to the flipped classroom model. Researchers recommend that teachers use the flipped classroom model, especially in dealing with learning in the post-pandemic era, like today.

This study investigated the influence of CThink4CS² Module on computational thinking (CT) skills of form four chemistry students. The CThink4CS² Module integrated CT with the Engineering Design Process (EDP) in chemistry class. This study utilized quantitative research methods and quasi-experimental design. Quantitative data were collected using the Computational Thinking Skill Test (CTST) which consisted of algorithmic reasoning, abstraction, decomposition, and pattern recognition constructs. A total of 73 students were in the treatment group (n=39) and control group (n=34). Experimental data were described by means of descriptive analysis and inferential analysis employing two-way MANOVA analysis. The results of the analysis indicated significant differences in CT skills between groups; students in the treatment group demonstrated better results compared to those in the control group. The paper provides insight into the integration of CT and EDP as effective pedagogical strategies for inculcating CT skills.

The incorporation of Computer Science teaching in educational systems has increased in recent years. Given international interest, Chile has promoted projects to promote the development of students' digital skills. Focusing on this new educational context, this research reports the results regarding the identification of computational concepts and practices that can be articulated with the contents and skills of the curriculum. of Chilean mathematics. for first grade of primary school based on the use of the Bee-Bot robot. For this, the study followed a qualitative approach, developing a case study of the Chilean study program with the content analysis technique and using, as analysis categories, computational concepts and practices from the field of educational computing. In total, 30 learning objectives of the study program were analyzed. The results indicate that, although there is little articulation between computational concepts and first grade content proposed in the curriculum, there is greater articulation between computational practices and mathematical skills suggested in the Chilean curriculum. It is concluded that Computational Thinking can be developed from the earliest school levels using the Bee-Bot robot (or similar), and this is demonstrated by the structure of the Chilean primary mathematics curricular program.

The role of artificial intelligence (AI) in education remains incompletely understood, demanding further evaluation and the creation of robust assessment tools. Despite previous attempts to measure AI's impact in education, existing studies have limitations. This research aimed to develop and validate an assessment instrument for gauging AI effects in higher education. Employing various analytical methods, including Exploratory Factor Analysis, Confirmatory Factor Analysis, and Rasch Analysis, the initial 70-item instrument covered seven constructs. Administered to 635 students at Nueva Ecija University of Science and Technology – Gabaldon campus, content validity was assessed using the Lawshe method. After eliminating 19 items through EFA and CFA, Rasch analysis confirmed the construct validity and led to the removal of three more items. The final 48-item instrument, categorized into learning experiences, academic performance, career guidance, motivation, self-reliance, social interactions, and AI dependency, emerged as a valid and reliable tool for assessing AI's impact on higher education, especially among college students.