Students’ Performance and Self-Efficacy Using the Design Thinking Model in Grade 7 Science

Students’ Performance and Self-Efficacy Using the Design Thinking Model in Grade 7 Science | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Students’ Performance and Self-Efficacy Using the Design Thinking Model in Grade 7 Science Haula Mangintay, Angelos Sofianidis, Rolly James Cheng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8331005/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Science education must not only promote student performance but also improve learners’ self-efficacy. Traditional instructions, while time-tested in content delivery, often limits student engagement and confidence. This study examined the effects of the Design Thinking Model (DTM) on students’ performance and self-efficacy in Grade 7 Science compared to the Expository Instruction Model (EIM). Using a quasi-experimental pretest-posttest design, 80 students from Zapakan National High School, BARMM, were assigned equally between DTM and EIM groups. Data were gathered through validated teacher-made tests and a standardized self-efficacy questionnaire. Findings revealed that while both groups significantly improved students’ performance, DTM students achieved higher mastery levels and better learning gains than their EIM counterparts. DTM students also reported very high efficacy, indicating greater confidence in their ability to understand and apply scientific concepts. The collaborative, iterative, and problem-centered nature of DTM seemed to cultivate deeper understanding and higher motivation than the structured, teacher-centered approach of EIM. This implies that Design Thinking not only enhances academic achievement but also builds learners’ self-belief and resilience. The study highlights the potential of DTM as a transformative instructional approach. It suggests embedding design thinking processes into science instruction to promote creativity, critical and analytical thinking, and learner autonomy aligned with 21st century skills development. Design Thinking Model Academic Performance Self-Efficacy Science Education Figures Figure 1 Figure 2 Figure 3 Introduction The search for new and effective teaching methods remains an important focus in education today, as schools work to help students develop the skills needed to succeed in a more complex and connected world. Among these innovations, the Design Thinking Model (DTM) has emerged as an innovative instructional approach, which can encourage creativity, strengthen problem-solving skills, and improve learner engagement across disciplines. Unlike Inquiry-Based Learning (IBL), which emphasizes exploration of scientific phenomena through questioning and experimentation, or Problem-Based Learning (PBL), which focuses on solving pre-defined, authentic problems through reasoning, DTM integrates both processes but extends them toward creative solution-building and reflection on user needs (Jordan & Elwood, 2022; Avsec, 2021). Rooted in iterative problem-solving processes, DTM emphasizes empathy, ideation, prototyping, and testing, enabling students to engage actively with real-world challenges while cultivating critical and creative thinking skills (Tsai et al., 2023). A growing body of literature shows the wide-ranging applicability of DTM across educational contexts. Li and Zhan (2022) report its successful integration in elementary and high school contexts, where students exhibited greater motivation and practical engagement in learning. Similarly, Guaman-Quintanilla et al. (2023) demonstrate that DTM in higher education positively correlates with student creativity and problem-solving skills, suggesting its transformative impact across academic levels. Research by Elwood and Jordan (2022) and Kijima et al. (2021) further highlights the ability of DTM to nurture creativity and critical problem-solving in STEAM education, promoting inclusive environments that encourage diverse perspectives in innovation. It engages learners not only in discovering knowledge but also in designing purposeful outcomes, thereby linking cognitive understanding with affective and social dimensions of learning (Tsai et al., 2023). Despite promising evidence of its role in improving creativity and academic performance, an important aspect that requires further study is its impact on learners’ self-efficacy, that is, the belief in one’s ability to successfully complete tasks and achieve learning goals. Students with higher self-efficacy are more likely to engage deeply with challenging concepts and sustain motivation, making it a critical affective outcome alongside achievement. While studies account that DTM improves students’ conceptual understanding in science and mathematics (Simeon, Samsudin, & Yakob, 2022; Hassan, 2023), as well as critical thinking and motivation (Anggraeni et al., 2023; Abdelrahman, 2020), there is still an important gap on how DTM affects confidence, resilience, and self-efficacy. While numerous studies have documented DTM’s contributions to creativity, collaboration, and conceptual understanding (Li & Zhan, 2022; Simeon et al., 2022; Quintanilla et al., 2023), few have directly examined its impact on learners’ self-efficacy within basic science education. This gap is significant given the role of self-efficacy in determining students’ persistence, resilience, and achievement in STEM subjects. Science education often presents learners with abstract and challenging concepts, where low self-efficacy can hinder performance and motivation (Bal-Tastan et al., 2018; Ugwuanyi et al., 2020). By introducing DTM into science instruction, educators may offer environments where learners feel empowered to take meaningful risks, reflect on their progress, and develop confidence in their problem-solving capabilities (Hsia & Hwang, 2020; Liu et al., 2021). The present study responds to this need by exploring the role of the Design Thinking Model on students’ performance and self-efficacy in Science 7. Specifically, it sought to answer the following research questions: 1. How does the academic performance of students taught through the Design Thinking Model differ from those taught through the Expository Instructional Model? 2. How does each instructional approach affect students’ improvement in science performance? 3. How does the Design Thinking Model influence students’ self-efficacy compared to the Expository Instructional Model? Theoretical Framework Constructivist and experiential learning theories, which suggest that knowledge is acquired through experience, introspection, and social interaction, are closely related to the Design Thinking Model (DTM). Piaget’s constructivism views learners as engaging in a kind of cognitive disequilibrium when they encounter problems that challenge their existing mental frameworks (schemas) and eventually lead to the change of their understanding (conceptual change). Similarly, Vygotsky's social constructivism emphasizes the value of guided interaction and group work even more. He refers to this as the zone of proximal development and finds that learning is most successful when students engage in meaningful engagement with teachers and other students to solve real problems. In addition, Kolb's experiential learning theory (1984) suggests that learning is an ongoing process that involves four stages: active experimentation, abstract conceptualization, reflective observation, and concrete experience. In this sense, DTM becomes a practical application of these principles, as it places the learners in authentic, collaborative, and iterative tasks that reflect how conceptual change occurs in science learning. Thus, Design Thinking appears as a teaching approach that not only embraces but also puts the principles into practice within the frames of this theoretical continuum. The five stages of the DTM, namely empathy, definition, ideation, prototyping, and testing, encourage active participation and promote feedback loops and teamwork among students. This helps them clarify their concepts through repeated reflection and feedback (Tsai et al., 2023; Jordan & Elwood, 2022). In the case of DTM in science education, students are given the opportunity to work with scientific concepts while addressing real-world problems; they will learn through experiments and be assisted in developing their understanding through innovative solutions, which will ultimately deepen their comprehension of the concepts. DTM is a constructivist instructional model because of its socially mediated and iterative approach, where active engagement, collaboration, and cycles of testing and revision serve as tools for conceptual understanding and conceptual change. Moreover, Bandura’s (1997) concept of self-efficacy refers to an individual’s belief in their capability to prepare and perform the series of actions needed to realize the target performance. This belief system strongly influences learning because it shapes students’ effort, persistence, and ability to bounce back when facing academic challenges. According to Bandura, self-efficacy is developed through four basic sources of self-efficacy, namely, mastery experiences (successes that build confidence), vicarious experiences (observing peers succeed), verbal persuasion (encouragement from teachers and classmates), and physiological and emotional states (feelings that affect confidence). Design Thinking Model (DTM) supports these sources of self-efficacy more fully than the conventional Expository Instructional Models (EIM). In DTM, students engage in practical, hands-on, and iterative tasks where ideas are prototyped, tested, revised, and improved, providing repeated mastery experiences as they witness their solutions becoming more effective (Stith et al, 2020; Moursy, 2024; Gahoonia & Gad, 2024). Collaborative brainstorming, peer demonstrations, and shared problem-solving give better vicarious experiences, while teacher facilitation and group discussions offer consistent verbal persuasion. The creative, exploratory, and supportive nature of DTM also reduces anxiety and promotes positive emotional engagement, reinforcing self-belief and willingness to take risks. On the other hand, because of its shortcomings, EIM might not be as successful as a teacher-centered approach that provides more opportunities for peer modeling and autonomy, limiting the conditions that strengthen self-efficacy. Figure 1 shows the integrative theoretical framework for the study. Figure 1 shows how the Design Thinking processes of empathy, ideation, prototyping, and testing, which actively involve students in collaborative and iterative problem-solving, are supported by constructivist and experiential learning principles (Piaget, Vygotsky, Kolb). In view of the way that students observe peers, get feedback, work in a supportive environment, and experience success repeatedly, these processes in turn reinforce Bandura's (1997) four sources of self-efficacy: mastery, vicarious learning, verbal persuasion, and positive affective states. Improved academic performance and greater confidence in learning science are the results of these constructivist processes combined with increased self-efficacy. Taken together, these theoretical perspectives imply that the Design Thinking Model improves learning by promoting self-efficacy and encouraging constructivist processes. These collaborative, hands-on, and iterative design cycles allow students to build and refine scientific understanding while gaining mastery experiences that enhance resilience and confidence. Active participation, social interaction, and reflective problem-solving are encouraged through this student-centered approach, which deepens conceptual understanding and cultivates a strong sense of competence. DTM thus appears to raise self-efficacy by supporting conceptual change through experiential learning and opportunities for success, peer modeling, encouragement, and positive emotional engagement. Methods Research Design This study employed a non-equivalent control group quasi-experimental design, a type of pretest-posttest design commonly used in a natural classroom settings where randomization is not feasible. Two intact Grade 7 classes were designated as the experimental group (taught using the Design Thinking Model) and the control group (taught using the Expository Instructional Model). Both groups completed the same pretest and posttest measures in academic performance and self-efficacy, allowing comparisons of learning gains within each group and between groups.The design allowed the researcher to establish baseline equivalence through pretest scores and to examine the causal influence of the instructional model despite the absence of full randomization. Participants and the context Eighty (80) Grade 7 students of Zapakan National High School, Bangsamoro Autonomous Region in Muslim Mindanao (BARMM) were involved in the study. 40 are under the Design Thinking Model (DTM) and another 40 for Expository Instructional Model (EIM). Grade 7 Science in the Philippine K to 12 Basic Education Curriculum (BEC) introduces learners to foundational scientific concepts across biology, chemistry, physics, and earth and space science, with a thrust on developing transversal skills and the ability to apply scientific principles to real-world situations. As mandated by the Department of Education, the Science curriculum follows a spiral progression, where key concepts are revisited with increasing complexity in higher grade levels. Instructional time typically spans one academic quarter (8-10 weeks) per unit, emphasizing both conceptual understanding and performance-based competencies. Zapakan National High School operates within a public school system that reflects the resource and contextual realities of many schools in BARMM, such as limited laboratory facilities and large class sizes. These conditions make it necessary to adopt strategies that are flexible, student-centered, and feasible in low resource settings, conditions under which the DTM is particularly suitable. The participants were heterogeneous in academic readiness and came from diverse socio-cultural backgrounds, representing typical learners in public junior high schools. Their inclusion provided a realistic context for examining the effectiveness of DTM in comparison to the traditionally used EIM in Philippine science classrooms. Research Instruments The study used three (3) primary instruments: (1) a modified self-efficacy questionnaire, (2) a teacher-made test that was validated to assess academic performance, and (3) a learning plan that outlined how the two instructional models were to be implemented. The same Science 7 content areas were covered by the 77 multiple-choice questions on the pretest and posttest. Following reliability testing and validation by subject matter experts, the test resulted to a Cronbach's α of 0.839, indicating high internal consistency (Field, 2009). The DepEd mastery level classification, which ranges from low mastery (0–16) to full mastery (65–80), was used for scoring. To provide clarity on how learners’ confidence was measured, the study adapted Self-Efficacy Questionnaire for Online Learning (SeQoL) used was developed by Tsai et al. (2020) and Shen et al. (2013) and also used by Cheng (2023) for blended and classroom-based contexts. Its high internal consistency is confirmed by its Cronbach's α of 0.918. Sample items asked students to rate statements, reflect Bandura’s (1997) four sources of self-efficacy, such as “I can create a plan to complete the given assignments”, and “I understand complex concepts” (mastery experience); “I pay attention to other students’ social actions” and “I develop friendship with classmates” (vicarious experience); “I clearly ask my questions to instructor” and “I express my opinions to other students respectfully”(verbal persuasion); and “I am willing to face challenges” and “I initiate social interactions” (affective states). Intervention Design While the EIM group got conventional, teacher-led training, the DTM group's sessions included empathy-based challenges, ideation, prototyping, and testing exercises. The learning plan, similar to the study of James (2024) used Gagne’s 9 Events of Instruction to elucidate the flow or sequence of instruction between EIM group and DTM group, respectively (Figure 3). Data Gathering Procedure Prior to the commencement of the study, ethical clearance (ERC Code OVCRE–ERC–004–2025) was obtained from the Office of the Vice Chancellor for Research and Extension at Mindanao State University- Maguindanao. To guarantee ethical compliance and voluntary involvement, informed consent was obtained from each participant as well as their parents or guardians. Before the intervention, a pretest was given to both groups to determine their starting levels of academic achievement and self-efficacy. The modified self-efficacy questionnaire and a validated teacher-made Science 7 evaluation were part of the pretest. An independent samples t-test was performed on the pretest results to verify baseline equivalency between the Design Thinking Model (DTM) and Expository Instructional Model (EIM) groups. An independent-samples t-test found no significant difference between the DTM (M = 22.10, SD = 3.80) and EIM (M = 22.00, SD = 3.90) groups at pretest, t(78) = 0.12, p = .90. The DTM and EIM instructional models were used for a quarter of the academic year after the pretest. Upon completion of the instructional period, a posttest was administered to both groups. The posttest mirrored the pretest in format and content, allowing for a direct comparison of academic performance. Additionally, the self-efficacy questionnaire was re-administered to measure any changes in students' confidence levels following the instructional interventions. Data from the pretest and posttest assessments, as well as the self-efficacy questionnaires, were collected and de-identified to protect the identity of participants. Performance and self-efficacy differences within and between groups were then examined using descriptive and inferential statistics. Statistical Treatment of Data Initial analysis involved descriptive statistics, including mean scores, standard deviations, and frequency distributions for both the pretest and posttest results, as well as self-efficacy scores. To assess the significance of the differences in academic performance and self-efficacy scores within each group (DTM and EIM) from pretest to posttest, paired sample t-tests were conducted. Table 1 presents how the self-efficacy scores were qualitatively interpreted to determine the students’ level of self-efficacy. To compare the posttest performance and self-efficacy scores between the two groups (DTM vs. EIM), independent sample t-tests were performed. This analysis aimed to determine whether there were significant differences in outcomes attributable to the instructional model used. A significance level of p < 0.05 was set for all statistical tests to determine the threshold for statistical significance. Table 1. Qualitative Interpretation of Self-Efficacy Scores Scale Qualitative Interpretation 8.51-10.00 Very High Efficacy 7.01-8.50 High Efficacy 5.51-7.00 Above Moderate Efficacy 4.01-5.50 Moderate Efficacy 2.51-4.00 Below Moderate Efficacy 1.01-2.50 Low Efficacy 0.00-1.00 Very Low Efficacy Results Performance of DTM and EIM Students in their Pretest and Posttest Shown in Table 2 are the mean scores of students taught using DTM and EIM in their pretest and posttest in Science 7. The analysis of the pretest results indicated that 92.3% (37 out of 40) of DTM and 75% (30 out of 40) of EIM students demonstrated “near mastery” with the scores ranging from 17-32. Table 2. Mean Scores of Students Using DTM and EIM in Their Pretest and Posttest DTM EIM Scale pretest posttest pretest posttest Interpretation f % f % f % f % 65 - 80 0 0 3 7.5 0 0 0 0 Full Mastery 49 - 64 0 0 37 92.5 0 0 13 32.5 Near Full Mastery 33 - 48 0 0 0 0 2 5 27 67.5 Mastery 17 - 32 37 92.5 0 0 30 75 0 0 Near Mastery 00 -16 3 7.5 0 0 8 20 0 0 Low Mastery Total 40 100 40 100 These pretest results indicate that both groups began at relatively similar levels, with most students in the “near mastery” level. Notably, fewer DTM students (7.5%) than EIM students (20%) fell into “low mastery,” suggesting slightly stronger baseline readiness for the DTM cohort. Importantly, no learners in either group achieved “full” or “near full mastery,” confirming that the intervention was timely and necessary. The posttest results, however, reveal descriptive differences. DTM produced a concentrated upward shift, with all students moving into either “near full mastery” (92.5%) or “full mastery” (7.5%). By contrast, EIM learners improved substantially but plateaued: most achieved “mastery” (67.5%), while a third reached “near full mastery,” and none attained “full mastery.” Performance Improvement among DTM and EIM Students The paired sample t-test results confirm that both instructional models, namely Design Thinking Model (DTM) and Experiential Instructional Model (EIM), significantly improved students’ performance in Science 7. Specifically, as shown in Table 3a, the DTM group exhibited an increase from a pretest mean of 22.10 to a posttest mean of 59.18, yielding a mean difference of 37.08.The difference in mean scores between the pretest and posttest of DTM group was statistically significant, t (df) = 45.48, p < .001, affirming the positive effect of DTM on learning gains. Similarly, Table 3b reveals that the EIM group also demonstrated a significant performance increase since the difference in mean scores between the pretest and posttest of EIM group was statistically significant, t (df) =24.28, p < .001, which likewise confirm that EIM has also positive impact on students. Comparing their posttest, there was a statistically significant difference in the mean scores observed between the DTM and EIM conditions, t (df) = 15.40, p < .001, as shown in Table 3c. Table 3. Performance Comparison of DTM and EIM Students Table 3a. Performance Improvement among DTM Students Mean Mean difference t-value p-value Decision Pretest 22.10 37.08 45.48 0.000* Reject Ho Posttest 59.18 Table 3b. Performance Improvement among EIM Students Mean Mean difference t-value p-value Decision Pretest 22.00 24.30 24.28 0.000* Reject Ho Posttest 46.30 Table 3c. Posttest Performance between DTM and EIM Students Mean Mean difference t-value p-value Decision DTM 59.18 15.40 45.48 0.000* Reject Ho EIM 46.30 *significant at 5% level Self-Efficacy Scores between DTM and EIM As presented in Table 4, the difference in self-efficacy scores between the DTM and EIM groups was statistically significant, t (df) = 11.43, p < .001. The self-efficacy level of students taught through the Design Thinking Model (DTM) was very high while the level of self-efficacy of those taught using the Expository Instructional Model (EIM) was high. Table 4. Comparability of the self-efficacy scores between the DTM and EIM students Self-Efficacy Mean Description Mean Difference t-value p-value DTM 9.51 Very High Efficacy 1.95 11.43 0.000* EIM 7.57 High Efficacy *Significant at 5% level Discussion Enhanced Conceptual Understanding through Iterative Learning The results revealed that students exposed to the Design Thinking Model (DTM) achieved significant learning gains than those taught using the Expository Instructional Model (EIM). This finding shows how iterative, empathic, and prototype-driven learning processes in DTM promote deeper conceptual understanding. Consistent with Simeon et al. (2022), students who engaged in iterative design cycles demonstrated stronger retention and mastery of science concepts, as each phase of ideation, prototyping, and testing allowed them to revisit, refine, and internalize knowledge through experience. Similarly, Patel et al. (2024) emphasized that design thinking integrates both critical and creative thinking, enabling learners to construct meaning actively rather than merely receive information. These suggest that while both methods enhanced performance, DTM was more effective in pushing learners to higher achievement level and ensuring no one remained at lower levels. Its iterative, collaborative, and problem-centered nature provided stronger scaffolding for deep understanding, whereas EIM supported consolidation but limited progression to the topmost level of mastery. This likewise aligns with prior studies showing that design-driven learning can lead to higher levels of student involvement and motivation compared to traditional methods. For example, Simeon et al. (2022) found that employing a design approach in STEM contexts improved students’ comprehension of complex physics concepts and increased engagement, which are critical indicators of sustained learning. From a constructivist lens, this finding aligns with Piaget’s and Vygotsky’s principle that meaningful learning emerges when students actively build knowledge through social interaction and problem-solving. In DTM, learners build conceptual understanding by tackling authentic challenges, iterating on their designs, and reflecting on feedback. This experiential and cyclical process helps reveal and correct misconceptions, that can facilitate conceptual change and higher-order thinking. In contrast, EIM’s linear and teacher-centered format tends to promote surface learning and memorization (Lubna et al., 2024), which, while effective for short-term recall, may not support deeper retention or transfer (Leekhot et al., 2024). Thus, the higher posttest performance of the DTM group reflects not only better mastery but also comparable cognitive engagement, confirming that iterative, student-centered learning environments promote lasting understanding in science education. Self-Efficacy as a Function of Active Participation The study also revealed that DTM students reported significantly higher levels of self-efficacy compared to those in the EIM group. This outcome can be interpreted through Bandura’s (1997) four sources of efficacy, namely mastery experience, vicarious experience, verbal persuasion, and physiological states. Mastery experiences were central to the DTM process, as learners designed, tested, and refined their prototypes, where each successful iteration reinforcing confidence in their ability to manage complex scientific tasks. Collaboration provided vicarious experience, allowing students to observe peers’ competence and model success, while constructive feedback and peer dialogue offered persuasive encouragement that validated progress and promoted persistence. The findings of our study are aligned with Ohly’s et al. (2017) findings who observed that university students engaged in design-based activities exhibited substantial increases in creative self-efficacy, while Yang and Hsu (2020) reported comparable results in design-integrated coursework. These findings emphasize that constructive, hands-on, and iterative learning environments promote learners’ belief in their capabilities, cultivating engagement and resilience. Likewise, Tsai and Wang (2021) confirmed that students’ disposition toward design thinking positively correlated with higher self-efficacy, suggesting that such approaches cultivate intrinsic motivation and confidence. In contrast, EIM’s structured and teacher-led orientation limits opportunities for self-directed learning and peer modeling. James (2024) and Oyiza (2023) noted that although traditional models like the 5E approach may enhance confidence in specific tasks, they lack the participatory and feedback-rich dynamics that strengthen self-belief across contexts. Horst (2021) further argued that expository strategies often reduce learner agency, potentially decreasing motivation. By comparison, DTM promotes autonomy, collaboration, and sustained engagement, which are factors shown to enhance self-efficacy (Avsec et al., 2023; Nguyen et al., 2024). The active co-construction of knowledge through DTM thus not only enhances cognitive skills but also nurtures the emotional and motivational dimensions critical to scientific inquiry. Educational Significance and Practical Implications While both instructional approaches improved student outcomes, the Design Thinking Model yielded a larger effect size and more significant pedagogical impact. Beyond measurable gains, DTM promoted 21st-century competencies such as creativity, critical thinking, collaboration, and self-directed learning. These skills are vital to addressing complex, real-world problems (Li & Zhan, 2022; Avsec, 2021). These strengthen the case for embedding design thinking principles in science instruction to complement and extend traditional approaches. The results affirm that DTM not only enhances academic performance but also supports transferable skills and dispositions central to contemporary education (Cutumisu et al., 2020; Chin et al., 2019). As Patel et al. (2024) explained, the synthesis of critical and design thinking nurtures innovative mindsets, moving learners beyond rote understanding toward adaptive expertise. Therefore, teachers adopting DTM must transition from information transmitters to learning facilitators who will be guiding students through cycles of empathy, ideation, prototyping, and reflection. At a systemic level, integrating DTM into national science curricula aligns with competency-based reforms emphasizing creativity, resilience, and learner autonomy. For students, the approach points toward self-regulation and ownership, thus, empowering them to become confident problem-solvers and reflective thinkers. It also implicates that DTM stands not merely as a teaching method but as a transformative framework that bridges cognitive mastery with affective growth, reinforcing the dual goal of science education, i.e. to cultivate both what learners know and what they believe they can achieve. Limitations This study acknowledges some limitations that may guide the interpretation of results. First, the research involved a relatively small sample of 80 Grade 7 students from a single public school in the Bangsamoro Autonomous Region in Muslim Mindanao (BARMM). While this sample provided valuable insights into classroom practice, the results may not be broadly generalizable to other areas, different grade levels, or school contexts with different learning cultures and resources. Second, the implementation period given only one quarter of the school year. This relatively small time may not be enough to fully capture the long-term effects of the Design Thinking Model (DTM) on students’ retention, transfer of learning, or sustained self-efficacy. Longer-term interventions could provide a more comprehensive picture of how design thinking influences continued motivation and academic growth. Finally, the quasi-experimental design of the study using intact classes, which was proper in natural school settings, restricted the random assignment possibility. While pretest analysis confirmed baseline comparability, potential classroom or teacher-related variables cannot be completely ruled out. Conclusions This study compared the effectiveness of the Design Thinking Model (DTM) to that of the Expository Instructional Model (EIM) in Science 7, focusing on student performance and self-efficacy. Both instructional models significantly improved students’ test performance, but DTM resulted a greater mean gain and “higher mastery” levels. Moreover, students exposed to DTM reported significantly higher self-efficacy than their EIM counterparts, showing that DTM can enhance academic achievement alongside their self-belief in their abilities. This contributes to the international science education research by showing how iterative, collaborative, and student-centered design processes can enhance both performance and confidence in a developing-country context. These findings suggest that integrating DTM into science instruction and teacher training can support 21st century skills such as creativity, critical thinking, collaboration, and communication. However, as the study was limited to a single school and one-quarter implementation, future research should examine the long-term and broader impacts of DTM across diverse settings and grade levels. Despite these limitations, the results provide evidence that design thinking can transform science classrooms into learning spaces where students not only gain scientific concepts more deeply but also develop the confidence to inquire, create, and innovate. Declarations Conflict of Interests The authors declare no conflict of interests related to this study. Funding The authors declare that this study was conducted without specific external financial grant from any funding agency in the public, commercial, or not-for-profit sectors. Authorship and Contributions All authors have met the established criteria for authorship, having made significant contributions to the work. Author 1 contributed to the conceptual design, data collection, and analysis. Authors 2 and 3 contributed to data interpretation, manuscript drafting, and critical revisions. 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M., & Hsu, T. F. (2020). Integrating design thinking into a packaging design course to improve students’ creative self-efficacy and flow experience. Sustainability, 12(15), 5929. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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06:45:48","extension":"html","order_by":39,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":121961,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8331005/v1/919101d7cb284a04fb94fd7e.html"},{"id":98425433,"identity":"9be96d12-4552-4daf-81ef-ae803a7a8b9b","added_by":"auto","created_at":"2025-12-17 16:34:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":71437,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eIntegrative Framework of Constructivism, Design Thinking, and Self-Efficacy in Science Learning\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8331005/v1/5149f6e0ebbb3ba1b8577188.png"},{"id":98038613,"identity":"2e4be53f-95db-48ea-bae4-a0f2a0c8ec9d","added_by":"auto","created_at":"2025-12-12 06:45:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":95648,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSchematic Diagram of the Study\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8331005/v1/9b4a9e07895094362e929bf0.png"},{"id":98038610,"identity":"2be493a5-3da3-4934-8951-b547e6691a06","added_by":"auto","created_at":"2025-12-12 06:45:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":122818,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFlow of Instruction for EIM and DTM intervention\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8331005/v1/f23cb821df326a1273c9ace4.png"},{"id":98623604,"identity":"464d1e8a-170d-4d16-84ad-a675f70659df","added_by":"auto","created_at":"2025-12-19 17:07:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1012318,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8331005/v1/8cde0d85-31c2-4c8f-802f-ffcdba7bcd3d.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eStudents’ Performance and Self-Efficacy Using the Design Thinking Model in Grade 7 Science\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe search for new and effective teaching methods remains an important focus in education today, as schools work to help students develop the skills needed to succeed in a more complex and connected world. Among these innovations, the Design Thinking Model (DTM) has emerged as an innovative instructional approach, which can encourage creativity, strengthen problem-solving skills, and improve learner engagement across disciplines. Unlike Inquiry-Based Learning (IBL), which emphasizes exploration of scientific phenomena through questioning and experimentation, or Problem-Based Learning (PBL), which focuses on solving pre-defined, authentic problems through reasoning, DTM integrates both processes but extends them toward creative solution-building and reflection on user needs (Jordan \u0026amp; Elwood, 2022; Avsec, 2021). Rooted in iterative problem-solving processes, DTM emphasizes empathy, ideation, prototyping, and testing, enabling students to engage actively with real-world challenges while cultivating critical and creative thinking skills (Tsai et al., 2023).\u003c/p\u003e\n\u003cp\u003eA growing body of literature shows the wide-ranging applicability of DTM across educational contexts. Li and Zhan (2022) report its successful integration in elementary and high school contexts, where students exhibited greater motivation and practical engagement in learning. Similarly, Guaman-Quintanilla et al. (2023) demonstrate that DTM in higher education positively correlates with student creativity and problem-solving skills, suggesting its transformative impact across academic levels. Research by Elwood and Jordan (2022) and Kijima et al. (2021) further highlights the ability of DTM to nurture creativity and critical problem-solving in STEAM education, promoting inclusive environments that encourage diverse perspectives in innovation. It engages learners not only in discovering knowledge but also in designing purposeful outcomes, thereby linking cognitive understanding with affective and social dimensions of learning (Tsai et al., 2023).\u003c/p\u003e\n\u003cp\u003eDespite promising evidence of its role in improving creativity and academic performance, an important aspect that requires further study is its impact on learners\u0026rsquo; self-efficacy, that is, the belief in one\u0026rsquo;s ability to successfully complete tasks and achieve learning goals. Students with higher self-efficacy are more likely to engage deeply with challenging concepts and sustain motivation, making it a critical affective outcome alongside achievement. While studies account that DTM improves students\u0026rsquo; conceptual understanding in science and mathematics (Simeon, Samsudin, \u0026amp; Yakob, 2022; Hassan, 2023), as well as critical thinking and motivation (Anggraeni et al., 2023; Abdelrahman, 2020), there is still an important gap on how DTM affects confidence, resilience, and self-efficacy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile numerous studies have documented DTM\u0026rsquo;s contributions to creativity, collaboration, and conceptual understanding (Li \u0026amp; Zhan, 2022; Simeon et al., 2022; Quintanilla et al., 2023), few have directly examined its impact on learners\u0026rsquo; self-efficacy within basic science education. This gap is significant given the role of self-efficacy in determining students\u0026rsquo; persistence, resilience, and achievement in STEM subjects. Science education often presents learners with abstract and challenging concepts, where low self-efficacy can hinder performance and motivation (Bal-Tastan et al., 2018; Ugwuanyi et al., 2020). By introducing DTM into science instruction, educators may offer environments where learners feel empowered to take meaningful risks, reflect on their progress, and develop confidence in their problem-solving capabilities (Hsia \u0026amp; Hwang, 2020; Liu et al., 2021).\u003c/p\u003e\n\u003cp\u003eThe present study responds to this need by exploring the role of the Design Thinking Model on students\u0026rsquo; performance and self-efficacy in Science 7. Specifically, it sought to answer the following research questions:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e1. How does the academic performance of students taught through the Design \u0026nbsp; \u0026nbsp; Thinking Model differ from those taught through the Expository \u0026nbsp;Instructional Model?\u003c/p\u003e\n\u003cp\u003e2. How does each instructional approach affect students\u0026rsquo; improvement in \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;science performance?\u003c/p\u003e\n\u003cp\u003e3. How does the Design Thinking Model influence students\u0026rsquo; self-efficacy \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; compared to the Expository Instructional Model?\u003c/p\u003e"},{"header":"Theoretical Framework","content":"\u003cp\u003eConstructivist and experiential learning theories, which suggest that knowledge is acquired through experience, introspection, and social interaction, are closely related to the Design Thinking Model (DTM). Piaget\u0026rsquo;s constructivism views learners as engaging in a kind of cognitive disequilibrium when they encounter problems that challenge their existing mental frameworks (schemas) and eventually lead to the change of their understanding (conceptual change). Similarly, Vygotsky\u0026apos;s social constructivism emphasizes the value of guided interaction and group work even more. He refers to this as the zone of proximal development and finds that learning is most successful when students engage in meaningful engagement with teachers and other students to solve real problems. In addition, Kolb\u0026apos;s experiential learning theory (1984) suggests that learning is an ongoing process that involves four stages: active experimentation, abstract conceptualization, reflective observation, and concrete experience.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this sense, DTM becomes a practical application of these principles, as it places the learners in authentic, collaborative, and \u0026nbsp; iterative tasks that reflect how conceptual change occurs in science learning. Thus, Design Thinking appears as a teaching approach that not only embraces but also puts the principles into practice within the frames of this theoretical continuum. The five stages of the DTM, namely empathy, definition, ideation, prototyping, and testing, encourage active participation and promote feedback loops and teamwork among students. This helps them clarify their concepts through repeated reflection and feedback (Tsai et al., 2023; Jordan \u0026amp; Elwood, 2022). In the case of DTM in science education, students are given the opportunity to work with scientific concepts while addressing real-world problems; they will learn through experiments and be assisted in developing their understanding through innovative solutions, which will ultimately deepen their comprehension of the concepts. DTM is a constructivist instructional model because of its socially mediated and iterative approach, where active engagement, collaboration, and cycles of testing and revision serve as tools for conceptual understanding and conceptual change.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMoreover, Bandura\u0026rsquo;s (1997) concept of self-efficacy refers to an individual\u0026rsquo;s belief in their capability to prepare and perform the series of actions needed to realize the target performance. This belief system strongly influences learning because it shapes students\u0026rsquo; effort, persistence, and ability to bounce back when facing academic challenges. According to Bandura, self-efficacy is developed through four basic sources of self-efficacy, namely, mastery experiences (successes that build confidence), vicarious experiences (observing peers succeed), verbal persuasion (encouragement from teachers and classmates), and physiological and emotional states (feelings that affect confidence). Design Thinking Model (DTM) supports these sources of self-efficacy more fully than the conventional Expository Instructional Models (EIM). \u0026nbsp; In DTM, students engage in \u0026nbsp; practical, hands-on, and iterative tasks where ideas are prototyped, tested, revised, and improved, providing repeated mastery experiences as they witness their solutions becoming more effective (Stith et al, 2020; Moursy, 2024; Gahoonia \u0026amp; Gad, 2024).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCollaborative brainstorming, peer demonstrations, and shared problem-solving give better vicarious experiences, while teacher facilitation and group discussions \u0026nbsp;offer consistent verbal persuasion. The creative, exploratory, and supportive nature of DTM also reduces anxiety and promotes positive emotional engagement, reinforcing self-belief and willingness to take risks. On the other hand, because of its shortcomings, EIM might not be as successful as a teacher-centered approach that provides more opportunities for peer modeling and autonomy, limiting the conditions that strengthen self-efficacy. Figure 1 shows the integrative theoretical framework for the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 1 shows how the Design Thinking processes of empathy, ideation, prototyping, and testing, which actively involve students in collaborative and iterative problem-solving, are supported by constructivist and experiential learning principles (Piaget, Vygotsky, Kolb). In view of the way that students observe peers, get feedback, work in a supportive environment, and experience success repeatedly, these processes in turn reinforce Bandura\u0026apos;s (1997) four sources of self-efficacy: mastery, vicarious learning, verbal persuasion, and positive affective states. Improved academic performance and greater confidence in learning science are the results of these constructivist processes combined with increased self-efficacy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTaken together, these theoretical perspectives imply that the Design Thinking Model improves learning by promoting self-efficacy and encouraging constructivist processes. These collaborative, hands-on, and iterative design cycles allow students to build and refine scientific understanding while gaining mastery experiences that enhance resilience and confidence. \u0026nbsp; Active participation, social interaction, and reflective problem-solving are encouraged through this student-centered approach, which deepens conceptual understanding and cultivates a strong sense of competence. DTM thus appears to raise self-efficacy by supporting conceptual change through experiential learning and opportunities for success, peer modeling, encouragement, and positive emotional engagement.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eResearch Design\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study employed a non-equivalent control group quasi-experimental design, a type of pretest-posttest design commonly used in a natural classroom settings where randomization is not feasible. Two intact Grade 7 classes were designated as the experimental group (taught using the Design Thinking Model) and the control group (taught using the Expository Instructional Model).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBoth groups completed the same pretest and posttest measures in academic performance and self-efficacy, allowing comparisons of learning gains within each group and between groups.The design allowed the researcher to establish baseline equivalence through pretest scores and to examine the causal influence of the instructional model despite the absence of full randomization.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipants and the context\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEighty (80) Grade 7 students of Zapakan National High School, Bangsamoro Autonomous Region in Muslim Mindanao (BARMM) were involved in the study. 40 are under the Design Thinking Model (DTM) and another 40 for Expository Instructional Model (EIM). Grade 7 Science in the Philippine K to 12 Basic Education Curriculum (BEC) introduces learners to foundational scientific concepts across biology, chemistry, physics, and earth and space science, with a thrust on developing transversal skills and the ability to apply scientific principles to real-world situations. As mandated by the Department of Education, the Science curriculum follows a spiral progression, where key concepts are revisited with increasing complexity in higher grade levels.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInstructional time typically spans one academic quarter (8-10 weeks) per unit, emphasizing both conceptual understanding and performance-based competencies. Zapakan National High School operates within a public school system that reflects the resource and contextual realities of many schools in BARMM, such as limited laboratory facilities and large class sizes. These conditions make it necessary to adopt strategies that are flexible, student-centered, and feasible in low resource settings, conditions under which the DTM is particularly suitable. The participants were heterogeneous in academic readiness and came from diverse socio-cultural backgrounds, representing typical learners in public junior high schools. Their inclusion provided a realistic context for examining the effectiveness of DTM in comparison to the traditionally used EIM in Philippine science classrooms.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Instruments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study used three (3) primary instruments: (1) a modified self-efficacy questionnaire, (2) a teacher-made test that was validated to assess academic performance, and (3) a learning plan that outlined how the two instructional models were to be implemented. The same Science 7 content areas were covered by the 77 multiple-choice questions on the pretest and posttest. Following reliability testing and validation by subject matter experts, the test resulted to a Cronbach\u0026apos;s \u0026alpha; of 0.839, indicating high internal consistency (Field, 2009). The DepEd mastery level classification, which ranges from low mastery (0\u0026ndash;16) to full mastery (65\u0026ndash;80), was used for scoring.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo provide clarity on how learners\u0026rsquo; confidence was measured, the study adapted Self-Efficacy Questionnaire for Online Learning (SeQoL) used was developed by Tsai et al. (2020) and Shen et al. (2013) and also used by Cheng (2023) for blended and classroom-based contexts. Its high internal consistency is confirmed by its Cronbach\u0026apos;s \u0026alpha; of 0.918. Sample items asked students to rate statements, reflect Bandura\u0026rsquo;s (1997) four sources of self-efficacy, such as \u0026ldquo;I can create a plan to complete the given assignments\u0026rdquo;, and \u0026ldquo;I understand complex concepts\u0026rdquo; (mastery experience); \u0026nbsp; \u0026ldquo;I pay attention to other students\u0026rsquo; social actions\u0026rdquo; and \u0026ldquo;I develop friendship with classmates\u0026rdquo; (vicarious experience); \u0026ldquo;I clearly ask my questions to instructor\u0026rdquo; and \u0026ldquo;I express my opinions to other students respectfully\u0026rdquo;(verbal persuasion); and \u0026ldquo;I am willing to face challenges\u0026rdquo; and \u0026ldquo;I initiate social interactions\u0026rdquo; (affective states).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIntervention Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhile the EIM group got conventional, teacher-led training, the DTM group\u0026apos;s sessions included empathy-based challenges, ideation, prototyping, and testing exercises. The learning plan, similar to the study of James (2024) used Gagne\u0026rsquo;s 9 Events of Instruction to elucidate the flow or sequence of instruction between EIM group and DTM group, respectively (Figure 3). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Gathering Procedure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrior to the commencement of the study, ethical clearance (ERC Code OVCRE\u0026ndash;ERC\u0026ndash;004\u0026ndash;2025) was obtained from the Office of the Vice Chancellor for Research and Extension at Mindanao State University- Maguindanao. To guarantee ethical compliance and voluntary involvement, informed consent was obtained from each participant as well as their parents or guardians. Before the intervention, a pretest was given to both groups to determine their starting levels of academic achievement and self-efficacy. The modified self-efficacy questionnaire and a validated teacher-made Science 7 evaluation were part of the pretest. An independent samples t-test was performed on the pretest results to verify baseline equivalency between the Design Thinking Model (DTM) and Expository Instructional Model (EIM) groups. An independent-samples t-test found no significant difference between the DTM (M = 22.10, SD = 3.80) and EIM (M = 22.00, SD = 3.90) groups at pretest, t(78) = 0.12, p = .90.\u003c/p\u003e\n\u003cp\u003eThe DTM and EIM instructional models were used for a quarter of the academic year after the pretest. Upon completion of the instructional period, a posttest was administered to both groups. The posttest mirrored the pretest in format and content, allowing for a direct comparison of academic performance. Additionally, the self-efficacy questionnaire was re-administered to measure any changes in students\u0026apos; confidence levels following the instructional interventions. Data from the pretest and posttest assessments, as well as the self-efficacy questionnaires, were collected and de-identified to protect the identity of participants. Performance and self-efficacy differences within and between groups were then examined using descriptive and inferential statistics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Treatment of Data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInitial analysis involved descriptive statistics, including mean scores, standard deviations, and frequency distributions for both the pretest and posttest results, as well as self-efficacy scores. To assess the significance of the differences in academic performance and self-efficacy scores within each group (DTM and EIM) from pretest to posttest, paired sample t-tests were conducted. Table 1 presents how the self-efficacy scores were qualitatively interpreted to determine the students\u0026rsquo; level of self-efficacy. To compare the posttest performance and self-efficacy scores between the two groups (DTM vs. EIM), independent sample t-tests were performed. This analysis aimed to determine whether there were significant differences in outcomes attributable to the instructional model used. A significance level of p \u0026lt; 0.05 was set for all statistical tests to determine the threshold for statistical significance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Qualitative Interpretation of Self-Efficacy Scores\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"412\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 171px;\"\u003e\n \u003cp\u003eScale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 241px;\"\u003e\n \u003cp\u003eQualitative Interpretation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 171px;\"\u003e\n \u003cp\u003e8.51-10.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 241px;\"\u003e\n \u003cp\u003eVery High Efficacy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 171px;\"\u003e\n \u003cp\u003e7.01-8.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 241px;\"\u003e\n \u003cp\u003eHigh Efficacy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 171px;\"\u003e\n \u003cp\u003e5.51-7.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 241px;\"\u003e\n \u003cp\u003e\u0026nbsp;Above Moderate Efficacy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 171px;\"\u003e\n \u003cp\u003e4.01-5.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 241px;\"\u003e\n \u003cp\u003eModerate Efficacy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 171px;\"\u003e\n \u003cp\u003e2.51-4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 241px;\"\u003e\n \u003cp\u003eBelow Moderate Efficacy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 171px;\"\u003e\n \u003cp\u003e1.01-2.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 241px;\"\u003e\n \u003cp\u003e\u0026nbsp;Low Efficacy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 171px;\"\u003e\n \u003cp\u003e0.00-1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 241px;\"\u003e\n \u003cp\u003eVery Low Efficacy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePerformance of DTM and EIM Students in their Pretest and Posttest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShown in Table 2 are the mean scores of students taught using DTM and EIM in their pretest and posttest in Science 7. The analysis of the pretest results indicated that 92.3% (37 out of 40) of DTM and 75% (30 out of 40) of EIM students demonstrated \u0026ldquo;near mastery\u0026rdquo; with the scores ranging from 17-32. \u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"463\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\" style=\"width: 463px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eMean Scores of Students Using DTM and EIM in Their Pretest and Posttest\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; DTM\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"bottom\" style=\"width: 264px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; EIM\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 56px;\"\u003e\n \u003cp\u003eScale\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;pretest\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp; posttest\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;pretest\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp; posttest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 106px;\"\u003e\n \u003cp\u003eInterpretation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003ef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003ef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003ef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 37px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\n \u003cp\u003ef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e65 - 80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41px;\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 37px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eFull Mastery\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e49 - 64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41px;\"\u003e\n \u003cp\u003e92.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 37px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e32.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eNear Full Mastery\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e33 - 48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 37px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e67.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eMastery\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e17 - 32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e\n \u003cp\u003e92.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 37px;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eNear Mastery\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e00 -16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 37px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003eLow Mastery\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 36px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 37px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 32px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 39px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThese pretest results indicate that both groups began at relatively similar levels, with most students in the \u0026ldquo;near mastery\u0026rdquo; level. Notably, fewer DTM students (7.5%) than EIM students (20%) fell into \u0026ldquo;low mastery,\u0026rdquo; suggesting slightly stronger baseline readiness for the DTM cohort. Importantly, no learners in either group achieved \u0026ldquo;full\u0026rdquo; or \u0026ldquo;near full mastery,\u0026rdquo; confirming that the intervention was timely and necessary. The posttest results, however, reveal descriptive differences. DTM produced a concentrated upward shift, with all students moving into either \u0026ldquo;near full mastery\u0026rdquo; (92.5%) or \u0026ldquo;full mastery\u0026rdquo; (7.5%). By contrast, EIM learners improved substantially but plateaued: most achieved \u0026ldquo;mastery\u0026rdquo; (67.5%), while a third reached \u0026ldquo;near full mastery,\u0026rdquo; and none attained \u0026ldquo;full mastery.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePerformance Improvement among DTM and EIM Students\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe paired sample t-test results confirm that both instructional models, namely Design Thinking Model (DTM) and Experiential Instructional Model (EIM), significantly improved students\u0026rsquo; performance in Science 7. Specifically, as shown in Table 3a, the DTM group exhibited an increase from a pretest mean of 22.10 to a posttest mean of 59.18, yielding a mean difference of 37.08.The difference in mean scores between the pretest and posttest of DTM \u0026nbsp;group was statistically significant, \u003cem\u003et\u003c/em\u003e(df) = 45.48, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, affirming the positive effect of DTM on learning gains. Similarly, Table 3b reveals that the EIM group also demonstrated a significant performance increase since the difference in mean scores between the pretest and posttest of EIM group was statistically significant, \u003cem\u003et\u003c/em\u003e(df) =24.28, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, which likewise confirm that EIM has also positive impact on students. Comparing their posttest,\u0026nbsp;there was a statistically significant difference in the mean scores observed between the DTM and EIM conditions, \u003cem\u003et\u003c/em\u003e(df) = 15.40, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, as shown in Table 3c.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Performance Comparison of DTM and EIM Students\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3a.\u0026nbsp;\u003c/strong\u003ePerformance Improvement among DTM Students\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"442\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 111px;\"\u003e\n \u003cp\u003eMean difference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003et-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69px;\"\u003e\n \u003cp\u003eDecision\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003ePretest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e22.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 111px;\"\u003e\n \u003cp\u003e37.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 59px;\"\u003e\n \u003cp\u003e45.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003eReject Ho\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003ePosttest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e59.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"bottom\" style=\"width: 442px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3b.\u003c/strong\u003e Performance Improvement among EIM Students\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 111px;\"\u003e\n \u003cp\u003eMean difference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003et-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69px;\"\u003e\n \u003cp\u003eDecision\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003ePretest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e22.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 111px;\"\u003e\n \u003cp\u003e24.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 59px;\"\u003e\n \u003cp\u003e24.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003eReject Ho\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003ePosttest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e46.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"bottom\" style=\"width: 442px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 3c. Posttest\u0026nbsp;\u003c/strong\u003ePerformance between DTM and EIM Students\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 111px;\"\u003e\n \u003cp\u003eMean difference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 59px;\"\u003e\n \u003cp\u003et-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 63px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 69px;\"\u003e\n \u003cp\u003eDecision\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003eDTM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e59.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 111px;\"\u003e\n \u003cp\u003e15.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 59px;\"\u003e\n \u003cp\u003e45.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003eReject Ho\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 71px;\"\u003e\n \u003cp\u003eEIM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e46.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e*significant at 5% level\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eSelf-Efficacy Scores between DTM and EIM\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs presented in Table 4, the difference in self-efficacy scores between the DTM and EIM groups was statistically significant, \u003cem\u003et\u003c/em\u003e(df) = 11.43, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001. The \u0026nbsp;self-efficacy level of students taught through the Design Thinking Model (DTM) was very high while the level of self-efficacy of those taught using the Expository Instructional Model (EIM) was high. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Comparability of the self-efficacy scores between the DTM and EIM students\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"441\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003eSelf-Efficacy\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003eMean Difference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 45px;\"\u003e\n \u003cp\u003et-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003eDTM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e9.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eVery High Efficacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 82px;\"\u003e\n \u003cp\u003e1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 45px;\"\u003e\n \u003cp\u003e11.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 65px;\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003eEIM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46px;\"\u003e\n \u003cp\u003e7.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eHigh Efficacy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;*Significant at 5% level \u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003eEnhanced Conceptual Understanding through Iterative Learning\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results revealed that students exposed to the Design Thinking Model (DTM) achieved significant learning gains than those taught using the Expository Instructional Model (EIM). This finding shows how iterative, empathic, and prototype-driven learning processes in DTM promote deeper conceptual understanding. Consistent with Simeon et al. (2022), students who engaged in iterative design cycles demonstrated stronger retention and mastery of science concepts, as each phase of ideation, prototyping, and testing allowed them to revisit, refine, and internalize knowledge through experience. Similarly, Patel et al. (2024) emphasized that design thinking integrates both critical and creative thinking, enabling learners to construct meaning actively rather than merely receive information.\u003c/p\u003e\n\u003cp\u003eThese suggest that while both methods enhanced performance, DTM was more effective in pushing learners to higher achievement level and ensuring no one remained at lower levels. Its iterative, collaborative, and problem-centered nature provided stronger scaffolding for deep understanding, whereas EIM supported consolidation but limited progression to the topmost level of mastery. This likewise aligns with prior studies showing that design-driven learning can lead to higher levels of student involvement and motivation compared to traditional methods. For example, Simeon et al. (2022) found that employing a design approach in STEM contexts improved students\u0026rsquo; comprehension of complex physics concepts and increased engagement, which are critical indicators of sustained learning.\u003c/p\u003e\n\u003cp\u003eFrom a constructivist lens, this finding aligns with Piaget\u0026rsquo;s and Vygotsky\u0026rsquo;s principle that meaningful learning emerges when students actively build knowledge through social interaction and problem-solving. In DTM, learners build conceptual understanding by tackling authentic challenges, iterating on their designs, and reflecting on feedback. This experiential and cyclical process helps reveal and correct misconceptions, that can facilitate conceptual change and higher-order thinking. In contrast, EIM\u0026rsquo;s linear and teacher-centered format tends to promote surface learning and memorization (Lubna et al., 2024), which, while effective for short-term recall, may not support deeper retention or transfer (Leekhot et al., 2024). Thus, the higher posttest performance of the DTM group reflects not only better mastery but also comparable cognitive engagement, confirming that iterative, student-centered learning environments promote lasting understanding in science education.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSelf-Efficacy as a Function of Active Participation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study also revealed that DTM students reported significantly higher levels of self-efficacy compared to those in the EIM group. This outcome can be interpreted through Bandura\u0026rsquo;s (1997) four sources of efficacy, namely mastery experience, vicarious experience, verbal persuasion, and physiological states. Mastery experiences were central to the DTM process, as learners designed, tested, and refined their prototypes, where each successful iteration reinforcing confidence in their ability to manage complex scientific tasks. Collaboration provided vicarious experience, allowing students to observe peers\u0026rsquo; competence and model success, while constructive feedback and peer dialogue offered persuasive encouragement that validated progress and promoted persistence.\u003c/p\u003e\n\u003cp\u003eThe findings of our study are aligned with Ohly\u0026rsquo;s et al. (2017) findings who observed that university students engaged in design-based activities exhibited substantial increases in creative self-efficacy, while Yang and Hsu (2020) reported comparable results in design-integrated coursework. These findings emphasize that constructive, hands-on, and iterative learning environments promote learners\u0026rsquo; belief in their capabilities, cultivating engagement and resilience. Likewise, Tsai and Wang (2021) confirmed that students\u0026rsquo; disposition toward design thinking positively correlated with higher self-efficacy, suggesting that such approaches cultivate intrinsic motivation and confidence.\u003c/p\u003e\n\u003cp\u003eIn contrast, EIM\u0026rsquo;s structured and teacher-led orientation limits opportunities for self-directed learning and peer modeling. James (2024) and Oyiza (2023) noted that although traditional models like the 5E approach may enhance confidence in specific tasks, they lack the participatory and feedback-rich dynamics that strengthen self-belief across contexts. Horst (2021) further argued that expository strategies often reduce learner agency, potentially decreasing motivation. By comparison, DTM promotes autonomy, collaboration, and sustained engagement, which are factors shown to enhance self-efficacy (Avsec et al., 2023; Nguyen et al., 2024). The active co-construction of knowledge through DTM thus not only enhances cognitive skills but also nurtures the emotional and motivational dimensions critical to scientific inquiry.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEducational Significance and Practical Implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhile both instructional approaches improved student outcomes, the Design Thinking Model yielded a larger effect size and more significant pedagogical impact. Beyond measurable gains, DTM promoted 21st-century competencies such as creativity, critical thinking, collaboration, and self-directed learning. These skills are vital to addressing complex, real-world problems (Li \u0026amp; Zhan, 2022; Avsec, 2021). These strengthen the case for embedding design thinking principles in science instruction to complement and extend traditional approaches.\u003c/p\u003e\n\u003cp\u003eThe results affirm that DTM not only enhances academic performance but also supports transferable skills and dispositions central to contemporary education (Cutumisu et al., 2020; Chin et al., 2019). As Patel et al. (2024) explained, the synthesis of critical and design thinking nurtures innovative mindsets, moving learners beyond rote understanding toward adaptive expertise. Therefore, teachers adopting DTM must transition from information transmitters to learning facilitators who will be guiding students through cycles of empathy, ideation, prototyping, and reflection.\u003c/p\u003e\n\u003cp\u003eAt a systemic level, integrating DTM into national science curricula aligns with competency-based reforms emphasizing creativity, resilience, and learner autonomy. For students, the approach points toward self-regulation and ownership, thus, empowering them to become confident problem-solvers and reflective thinkers. It also implicates that DTM stands not merely as a teaching method but as a transformative framework that bridges cognitive mastery with affective growth, reinforcing the dual goal of science education, i.e. to cultivate both what learners know and what they believe they can achieve.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study acknowledges some limitations that may guide the interpretation of results. First, the research involved a relatively small sample of 80 Grade 7 students from a single public school in the Bangsamoro Autonomous Region in Muslim Mindanao (BARMM). While this sample provided valuable insights into classroom practice, the results may not be broadly generalizable to other areas, different grade levels, or school contexts with different learning cultures and resources. Second, the implementation period given only one quarter of the school year. This relatively small time may not be enough to fully capture the long-term effects of the Design Thinking Model (DTM) on students\u0026rsquo; retention, transfer of learning, or sustained self-efficacy. Longer-term interventions could provide a more comprehensive picture of how design thinking influences continued motivation and academic growth. Finally, the quasi-experimental design of the study using intact classes, which was proper in natural school settings, restricted the random assignment possibility. While pretest analysis confirmed baseline comparability, potential classroom or teacher-related variables cannot be completely ruled out.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study compared the effectiveness of the Design Thinking Model (DTM) to that of the Expository Instructional Model (EIM) in Science 7, focusing on student performance and self-efficacy. Both instructional models significantly improved students\u0026rsquo; test performance, but DTM resulted a greater mean gain and \u0026ldquo;higher mastery\u0026rdquo; levels. Moreover, students exposed to DTM reported significantly higher self-efficacy than their EIM counterparts, showing that DTM can enhance academic achievement alongside their self-belief in their abilities. This contributes to the international science education research by showing how iterative, collaborative, and student-centered design processes can enhance both performance and confidence in a developing-country context. These findings suggest that integrating DTM into science instruction and teacher training can support 21st century skills such as creativity, critical thinking, collaboration, and communication.\u003c/p\u003e \u003cp\u003eHowever, as the study was limited to a single school and one-quarter implementation, future research should examine the long-term and broader impacts of DTM across diverse settings and grade levels. Despite these limitations, the results provide evidence that design thinking can transform science classrooms into learning spaces where students not only gain scientific concepts more deeply but also develop the confidence to inquire, create, and innovate.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of Interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interests related to this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that this study was conducted without specific external financial grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthorship and Contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have met the established criteria for authorship, having made significant contributions to the work. Author 1 contributed to the conceptual design, data collection, and analysis. Authors 2 and 3 contributed to data interpretation, manuscript drafting, and critical revisions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics and Consent\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study involving human participants received ethical approval from the Ethics Research Committee of the Mindanao State University - Maguindanao (ERC Code OVCRE -ERC-004-2025). Written informed consent was obtained from all participants for their involvement in the study and for publication of the findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDue to ethical and confidentiality restrictions as stipulated in the informed consent forms, the data underlying this study are not publicly available. However, data may be made available from the corresponding author upon reasonable request and further information.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdelrahman, R. M. (2020). Metacognitive awareness and academic motivation and their impact on academic achievement of Ajman University students. Heliyon, 6(9). https://www.cell.com/heliyon/fulltext/S2405-8440(20)31036-7\u003c/li\u003e\n\u003cli\u003eAlbay, E. M., \u0026amp; Eisma, D. V. (2021). Performance task assessment supported by the design thinking process: Results from a true experimental research. Social Sciences \u0026amp; Humanities Open, 3(1), 100116.\u003c/li\u003e\n\u003cli\u003eAnggraeni, D. M., Prahani, B. K., Suprapto, N., Shofiyah, N., \u0026amp; Jatmiko, B. (2023). Systematic review of problem based learning research in fostering critical thinking skills. 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I., \u0026amp; Ageda, T. A. (2020). Motivation and self-efficacy as predictors of learners\u0026rsquo; academic achievement. Journal of Sociology and Social Anthropology, 11(3-4), 215-222.\u003c/li\u003e\n\u003cli\u003eYang, C. M., \u0026amp; Hsu, T. F. (2020). Integrating design thinking into a packaging design course to improve students\u0026rsquo; creative self-efficacy and flow experience. Sustainability, 12(15), 5929.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Mindanao State University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Design Thinking Model, Academic Performance, Self-Efficacy, Science Education","lastPublishedDoi":"10.21203/rs.3.rs-8331005/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8331005/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eScience education must not only promote student performance but also improve learners\u0026rsquo; self-efficacy. Traditional instructions, while time-tested in content delivery, often limits student engagement and confidence. This study examined the effects of the Design Thinking Model (DTM) on students\u0026rsquo; performance and self-efficacy in Grade 7 Science compared to the Expository Instruction Model (EIM). Using a quasi-experimental pretest-posttest design, 80 students from Zapakan National High School, BARMM, were assigned equally between DTM and EIM groups. Data were gathered through validated teacher-made tests and a standardized self-efficacy questionnaire. Findings revealed that while both groups significantly improved students\u0026rsquo; performance, DTM students achieved higher mastery levels and better learning gains than their EIM counterparts. DTM students also reported very high efficacy, indicating greater confidence in their ability to understand and apply scientific concepts. The collaborative, iterative, and problem-centered nature of DTM seemed to cultivate deeper understanding and higher motivation than the structured, teacher-centered approach of EIM. This implies that Design Thinking not only enhances academic achievement but also builds learners\u0026rsquo; self-belief and resilience. The study highlights the potential of DTM as a transformative instructional approach. It suggests embedding design thinking processes into science instruction to promote creativity, critical and analytical thinking, and learner autonomy aligned with 21st century skills development.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e","manuscriptTitle":"Students’ Performance and Self-Efficacy Using the Design Thinking Model in Grade 7 Science","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-12 06:45:42","doi":"10.21203/rs.3.rs-8331005/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6d0c293f-d9b4-47f5-a646-31caa01b042f","owner":[],"postedDate":"December 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-12T06:45:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-12 06:45:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8331005","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8331005","identity":"rs-8331005","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}