Gawi: Journal of Action Research

Exploring Computational Thinking Skills in Pre-Service Science Teachers: A Rasch Model Analysis

Riskawati Riskawati — 2026-06-11

Computational Thinking (CT) has been increasingly recognized as a key competency in science education, especially in preparing future educators to foster 21st-century skills such as problem-solving and digital literacy. However, limited CT instruction in undergraduate programs has resulted in inadequate readiness among pre-service science teachers, particularly in contexts like Indonesia. This study addresses the gap in the evaluation of CT skills among pre-service teachers by using robust psychometric approaches. This article investigates the psychometric properties of a CT assessment tool, examines the CT ability levels of pre-service science teachers, and evaluates the presence of gender-based item bias. The study employed a quantitative, cross-sectional design utilizing Rasch model analysis to assess instrument reliability, item fit, and differential item functioning (DIF) across gender. A total of 419 pre-service science teachers from two universities in Indonesia were selected using stratified random sampling to ensure proportional representation across academic levels. Data collection and analysis: Data were collected using the 28-item Computational Thinking Test (CTt) and analyzed using Winsteps software for Rasch modeling, including item/person reliability, separation, fit statistics, Wright maps, and DIF analysis. Results indicated high item reliability and good model fit, though person reliability was relatively low, suggesting limited ability discrimination. Several items exhibited gender-based DIF, with some favoring males and others favoring females. The CTt shows strong potential for measuring CT, though refinement is needed to improve its ability to differentiate between skill levels and ensure gender fairness. CT is increasingly recognized as an essential competency in science education to support problem-solving, analytical reasoning, and 21st-century digital literacy skills. However, empirical evidence regarding the measurement quality of CT instruments among pre-service science teachers in Indonesia remains limited. This study aimed to evaluate the psychometric properties of the CTt, describe the distribution of CT skills among pre-service science teachers, and examine gender-based item bias using the Rasch model. A quantitative cross-sectional design was employed involving 419 pre-service science teachers from two Indonesian universities selected through stratified random sampling. Data were analyzed using Winsteps software to examine reliability, separation index, item fit, Wright map distribution, and DIF. The results showed that the CTt demonstrated high item reliability and good model fit, indicating that the instrument is appropriate for measuring CT skills. However, person reliability was relatively low, suggesting limited sensitivity in distinguishing individual ability levels. Wright map analysis indicated that most respondents were distributed at a moderate ability level. In addition, DIF analysis identified several items with potential gender bias, favoring both male and female respondents. Overall, the CTt shows strong potential as an instrument for measuring CT skills, although further refinement is needed to improve measurement sensitivity and ensure gender fairness. These findings contribute to the development of Rasch model–based CT assessment in pre-service science teacher education.

Research Trends in Ethnoscience in Science Education: A Bibliometric Analysis Based on the Scopus Database

Parham Saadi — 2026-06-11

This study aims to analyze the development of ethnoscience research in science learning using publications indexed in Scopus. The study used a descriptive bibliometric approach to documents on ethnoscience and science learning published between 1998 and 2026. The analysis focused on publication growth trends, the most productive affiliations and countries, primary publication sources, the most influential authors, and a keyword co-occurrence map using VOSviewer. The analysis shows that ethnoscience research in science learning experiences has fluctuated but has been increasing, with the largest spike in 2025. Publication productivity is dominated by Indonesia at both the country and institutional levels, with Indonesian universities occupying the top position as the most productive affiliates. At the source level, publications are concentrated in journals oriented towards science education and contextual learning innovation. Citation analysis shows that scientific influence remains concentrated in a small number of authors, while the keyword map indicates that ethnoscience is a key node connected to the themes of science education, project-based learning, scientific literacy, critical thinking skills, indigenous science, and context-based learning. These findings confirm that ethnoscience has evolved from simply integrating local knowledge into learning to a multidimensional, contextual, and relevant pedagogical approach aimed at strengthening 21st-century competencies. This research has implications for the importance of expanding international collaboration, strengthening publication quality, and developing more innovative and impactful ethnoscience-based science learning models

Adaptive Action-Based Management Model for Inclusive Early Childhood Education

Rini Triana — 2026-06-11

This study aimed to develop an Adaptive Action-Based Inclusive School Management Model to enhance the efficacy of inclusive early childhood education (ECE) in Banda Aceh, Indonesia. Employing a qualitative school-based action research design across three inclusive ECE institutions, the study involved 13 purposively selected participants, including school principals, educators, special assistant teachers, parents, and an education office representative. The methodology was operationalized through two iterative cycles of planning, action, observation, and reflection, with interventions targeting inclusive management optimization via structured planning, stakeholder coordination, and systemic monitoring. Qualitative data derived from in-depth interviews, participant observation, and document analysis were processed utilizing the interactive qualitative analysis model of Miles, Huberman, and Saldaña, supported by NVivo software. planning practices were collaborative and child-centered but insufficiently documented, implementation was adaptive and outcome-oriented yet weakly coordinated, and evaluation effectively monitored child development but lacked institutional integration. Major constraints included limited government support, infrastructure, and teacher capacity, while adaptive strategies relied on collaboration, communication, and internal initiatives. The study proposes an Adaptive Action-Based Inclusive School Management Model characterized by iterative improvement, collaborative engagement, adaptive decision-making, and outcome-oriented evaluation. The results highlight the importance of strengthening systematic management practices and policy support to ensure sustainable inclusive education.

The Influence of Artificial Intelligence (AI) Utilization on Learning Motivation and Creativity of Students

Syahril — 2026-06-11

The development of AI in education has a significant impact on changing how students learn, think, and create, particularly in enhancing access to information and learning effectiveness. The purpose of this study is to analyze the influence of AI on the learning motivation and creativity of FKIP students at Universitas Lambung Mangkurat. This study employs a quantitative research method with a random sampling technique, involving 125 students enrolled in the Scientific Writing course. Data collection was conducted by distributing a Likert-scale questionnaire with 4 response options to measure the variables of AI, learning motivation, and student creativity. The data analysis technique utilizes the Structural Equation Model (SEM) to determine the influence of the AI variable on student learning motivation and creativity. The results indicate that the utilization of Artificial Intelligence (AI) has a positive and significant influence on student learning motivation. However, the utilization of AI does not have a direct significant influence on student creativity. Conversely, learning motivation is proven to have a positive and significant influence on creativity and acts as a mediating variable. In conclusion, the enhancement of student creativity is more effectively achieved through the increase of learning motivation facilitated by the utilization of AI. Therefore, it is recommended that educators integrate AI with active learning strategies, such as project-based and problem-based learning, and encourage students to use AI as a tool for exploring ideas rather than merely for task completion.

Mapping a Bounded Scopus Corpus on STEAM-Related Learning Processes: A Bibliometric Analysis (2015–2026)

Qamariah Qamariah — 2026-06-11

This study maps the development and thematic structure of a bounded Scopus corpus on STEAM-related learning processes through bibliometric analysis. Data were retrieved from the Scopus database on March 26, 2026, using a predefined TITLE-ABS-KEY search strategy restricted to English-language journal articles in the final publication stage. The final corpus consisted of 115 documents published between 2015 and 2026. The analysis combined performance indicators and author-keyword co-occurrence analysis to examine publication growth, leading countries, affiliations, sources, productive authors, influential documents, and thematic structures within the retrieved corpus. The results show that publication output increased substantially after 2020 and reached its highest level in 2025, while the 2026 count remained provisional, as it reflected a partial publication year. The United States emerged as the leading contributor, whereas the International University of La Rioja and Universitas Negeri Jakarta were the most productive affiliations. Computer Applications in Engineering Education was the most productive source. Document-level citation analysis showed that teacher professional development, educational robotics, challenge-based learning, computational thinking, project-based learning, and technology-enhanced learning environments constitute influential foundations of the corpus. Author-keyword co-occurrence analysis identified thematic concentrations related to multilingual pedagogical practices, teacher knowledge, teacher professional development, inclusive science education, digital pedagogy, literacy development, and educational equity. These findings indicate that the retrieved corpus is characterized less by broad STEAM research in general and more by pedagogical, teacher-capacity, literacy, and learning-process-oriented issues. This study contributes a transparent bibliometric profile of a bounded Scopus corpus and demonstrates how retrieval logic influences thematic structures in bibliometric mapping.