International Journal of Science Education and Teaching

Effects of the VIMCC (Visual, Interactive, and Meaning-Centered Conceptualization) Approach on Students’ Conceptual Understanding, Learning Engagement, and Insights in Subatomic Particles

Thu, 23 Jul 2026 00:00:00 +0700

This action research focused on improving Grade 8 students' performance in differentiating subatomic particles by strengthening their conceptual understanding of atomic structure through the VIMCC (Visual, Interactive, and Meaning-Centered Conceptualization) approach. The study was conducted in a public high school in Leyte, Philippines, among selected Grade 8 students who had difficulty differentiating subatomic particles. Initial observations and pre-test results revealed that students had weak prior knowledge of atomic structure, which affected their understanding of subatomic particles. To address this problem, the researchers implemented the VIMCC Approach embedded in a 7E’s lesson plan. This approach integrated Visual tools (YouTube video presentations, color-coded learning materials, and 3D atomic models), Interactive activities (game-based matching task and model manipulation), and Meaning-Centered strategies (concept maps and the Frayer Model). The study employed participatory action research using formative assessments, classroom observations, and focus group discussions as data sources. Results showed significant improvements in students’ conceptual understanding and academic performance, with increasing post-test mean percentage scores in both Cycle 1 and 2. Normalized gain scores and paired t-test results indicated positive learning gains in both cycles. Observations revealed high levels of effort and initiative, accompanied by low levels of disruptive and inattentive behaviors. Focus group discussions further revealed that students perceived the VIMCC-based activities as enjoyable, engaging, easier to understand, and more interesting. The findings highlight the effectiveness of the VIMCC Approach in strengthening secondary students’ conceptual understanding of atomic structure, thereby improving their ability to distinguish among subatomic particles and enhancing their confidence in learning science.

Lived Experiences of Science Educators on AI Use: A Descriptive Phenomenological Study

Rod James Bande, Jannien Dora Cabahit — Tue, 04 Aug 2026 00:00:00 +0700

This study explores the lived experiences of science educators using artificial intelligence (AI), focusing on how these experiences shape their thinking processes. As AI becomes increasingly integrated into education, the study seeks to understand educators’ subjective, first-person experiences beyond measurable instructional outcomes. It specifically examines how educators describe their use of AI and how these experiences relate to cognitive processes in teaching. A descriptive phenomenological design guided by Giorgi’s (2009) method was employed to capture rich experiential accounts. Seven science educators from Baybay City, Leyte, were selected through purposive sampling based on active AI use. Data were gathered through semi-structured interviews conducted both synchronously and asynchronously. Analysis followed Giorgi’s systematic steps: bracketing, meaning unit identification, transformation, and synthesis to derive the essential structure of the phenomenon. Findings reveal AI as a multifaceted support system that enhances pedagogical practice, cognition, and professional growth. Five key constituents emerged: (1) AI as a pedagogical and learning support tool, (2) cognitive transformation in AI use, (3) emotional and experiential engagement, (4) critical thinking and ethical awareness, and (5) reconstruction of teacher identity. Educators reported improved efficiency in lesson preparation, enhanced organization, flexibility, and deeper analytical engagement. Emotional responses included increased confidence and support, alongside caution due to concerns about accuracy and overreliance. Participants also demonstrated active evaluation and adaptation of AI-generated outputs, reinforcing human decision-making in instruction. A notable shift in identity emerged, from content provider to facilitator and lifelong learner. AI functions not merely as a tool but as a transformative presence in science education. It reshapes instructional practices, cognitive engagement, and professional identity. While it enhances efficiency and supports higher-order thinking, its effective integration depends on educators’ critical, ethical, and reflective use. The study highlights the need to balance AI integration with the preservation of human cognition and professional responsibility in education.

Developing Computational Thinking in Primary School Students Through a Smart Farming Context Using the Engineering Design Process

Supachai Kongpui, Tussatrin Wannagatesiri — Tue, 25 Aug 2026 00:00:00 +0700

The purpose of this research was to: (1) develop learning activities using the Engineering Design Process (EDP) within a smart farming context to enhance the computational thinking (CT) skills of primary school students, and (2) compare the students’ CT skills before and after participating in these activities. The participants were 39 fifth-grade students from a large inclusive primary school in Thailand, selected through purposive sampling. The research employed a pre-experimental design. Research instruments included four EDP-based smart farming lesson plans totaling 12 hours of instruction, a CT concept test, and a performance-based CT rubric. The results indicated that the integration of the six-step EDP, ranging from identifying authentic agricultural problems to iteratively debugging automated sensor prototypes, significantly improved students’ CT skills. Quantitative analysis revealed a statistically significant increase in overall CT skill scores, with the post-test mean (M = 19.46, SD = 2.35) being significantly higher than the pre-test mean (M = 11.64, SD = 2.98) at p < .001. Significant improvements were observed across all specific computational components, with the largest improvement occurring in conditional logic. Furthermore, qualitative data from the performance-based rubric and final smart farming projects confirmed that students effectively applied the four pillars of CT: decomposition, pattern recognition, abstraction, and algorithm design to solve real-world agricultural challenges. This study concludes that anchoring computational tasks in authentic, tangible contexts like smart farming successfully bridges the gap between abstract coding and meaningful physical outcomes.