Browsing by Author "Gunbatar, Mustafa Serkan"

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Computational Thinking Within the Context of Professional Life: Change in Ct Skill From the Viewpoint of Teachers
(Springer, 2019) Gunbatar, Mustafa Serkan
The goal of this study is to compare in-service and pre-service teachers' computational thinking skills and to take in-service teachers' opinions about the contribution of professional life to differentiation in this skill. The study was conducted in Turkey. The type of the study is mixed method. Quantitative data were obtained from 870 pre-service teachers enrolled to Van Yuzuncu Yil University and from 143 in-service teachers working in Van province. Qualitative data were obtained from 10 in-service teachers. Quantitative data were collected with Computational Thinking Scales (CTS). Qualitative data were obtained through conducting focus group interview. Results revealed that in-service teachers significantly differentiate from pre-service teachers according to the common effect of the sub dimensions of CTS. On the other hand, according to the results of the comparison conducted based on the main effect of the total score and sub dimensions of the scale; there is no difference according to the sub dimension of problem solving. There is a differentiation on behalf of in-service teachers according to all measurements outside of that. Qualitative data also support these results. In addition, qualitative data present details concerning the reasons of the change in CT within the context of professional life.
The Effect of Knowledge Sharing, Attitude, and Satisfaction on Novice University Students? Online Learning Achievement
(Anadolu Univ, 2023) Keskin, Sinan; Gunbatar, Mustafa Serkan; Cavus, Hayati
In the academic year 2020-2021, students who had been accepted onto a university in Turkiye began their studies with the use of emergency remote teaching (ERT). The aim of this study is to examine the causal relationship between academic achievement, online course satisfaction, attitudes towards online learning and knowledge sharing behaviors of these novice university students in terms of the emergency remote teaching process. This research was designed to make use of correlational research methods. The study group consisted of 437 freshmen students studying in the Faculty of Education at a public university in Turkiye. Research data were collected using the Knowledge Sharing Behavior scale, the Online Course Satisfaction scale, the Online Learning Attitude scale, and a learning achievement test. Research data were analyzed using descriptive statistics, Pearson correlation analysis, and path analysis. The results showed that general acceptance, knowledge receiving, individual awareness and perceived usefulness significantly affected online course satisfaction. On the other hand, knowledge giving and application effectiveness factors did not significantly affect online course satisfaction. It is noteworthy that individual awareness, which compares face-to-face teaching activities with ERT to identify the preferences and awareness of the students, had a negative impact on their satisfaction. Finally, it was determined that online course satisfaction had a significant but low-level effect on learning achievement. In order to increase student satisfaction with regard to the ERT process, it is recommended that the university makes the opportunities more visible and provide support for students' acceptance of the process. Future avenues and precautions about designing the ERT courses have been suggested in light of the research findings.
Generative AI as the New Frontier in Science Education: A Systematic Review of Web of Science Articles
(Springer, 2025) Aydin-Gunbatar, Sevgi; Durukan, Alper; Gunbatar, Mustafa Serkan
This systematic review study investigated the research on the use of generative artificial intelligence (GenAI) tools in science education. The Web of Science database was screened by study titles and abstracts. In total, 41 peer-reviewed articles were included. The researchers created the coding scheme in light of GenAI use in education and science education literature. Results revealed that with 12 studies, most of the research was conducted in chemistry education. The highest frequency of the intended use was to evaluate GenAI tools' performance in tasks (e.g., solving physics problems), whereas very few studies aimed at enriching science teaching (n = 1). Regarding student-centeredness, only five studies let learners use the technology without any direction, whereas in seven studies, learners' use was guided. Concerning the level of use, in 14 studies, GenAI use was at a replacement level. The assessment was primarily focused on the instructional component in 23 papers. Most of the studies conducted with GenAI tools did not include participants (n = 20). Quantitative methods (n = 17) were preferred over qualitative ones (n = 10). Based on the results, the use of GenAI tools in science education seems to be in its infancy. At this stage, interdisciplinary partnerships between technology and science educators are necessary. In future research, science educators should focus more on using GenAI tools to enrich science instruction (e.g., how to enhance learners' arguments, how GenAI tool use affects learners' argument formation, and how GenAI tools can enrich the history and nature of science instruction).
Predictors of Computer Anxiety: a Factor Mixture Model Analysis
(Sage Publications inc, 2009) Marcoulides, George A.; Cavus, Hayati; Marcoulides, Laura D.; Gunbatar, Mustafa Serkan
A mixture modeling approach was used to assess the existence of latent classes in terms of the perceptions of individuals toward Computer anxiety and subsequently predictors of the identified latent classes were examined. The perceptions of individuals were measured using the Computer Anxiety Scale. Mixture models are ideally suited to represent subpopulations or classes of respondents with common patterns of responses. Using data from a sample of Turkish college students, two classes of respondents were identified and designated as occasionally uncomfortable users and as anxious computerphobic users. Results indicated that the best predictors of the identified classes were variables dealing with past computer experiences.
Stem Teaching Intention and Computational Thinking Skills of Pre-Service Teachers
(Springer, 2019) Gunbatar, Mustafa Serkan; Bakirci, Hasan
The aim of the study is to examine the Science, Technology, Engineering and Mathematics (STEM) teaching intention of science and primary school pre-service teachers in terms of Computational Thinking (CT) skill, gender, grade level, daily computer usage, internet usage, smartphone usage, and the department variables. The study employs the correlational survey model. The participants of this research are 440 pre-service teachers at Van Yuzuncu Yl University, Turkey. The STEM teaching intention scale, and the CT skill scale were used for data collection. Chi-Squared Automatic Interaction Detector (CHAID) analysis, independent samples t- test, and single factor variance analysis (ANOVA) was used for data analysis. According to the results; CT has the most significant effect in terms of STEM teaching intentions. Department is also another important variable for STEM teaching intentions. STEM teaching intention measures do not differ according to gender, grade level, daily average computer usage, internet usage and smart phone usage.