Eclet. Quim. 50 | e-1566, 2025 https://doi.org/10.26850/1678-4618.eq.v50.2025.e1566 ISSN 1678-4618 page 1/10 1University of Technology Malaysia, Faculty of Educational Sciences and Technology, Johor Bahru, Malaysia. 2Darulaman Campus Teacher Education Institute, Jitra, Malaysia. +Corresponding author: Norliyana binti Aris, Phone: +6013-3881723, Email address: liyanaaris23@gmail.com Original Article, Education in Chemistry and Correlated Areas Determining design thinking elements in chemistry education: A Fuzzy Delphi method Norliyana binti Md. Aris1+ , Nor Hasniza binti Ibrahim1 , Noor Dayana binti Abd Halim1 , Nurul Hanani binti Rusli1 , Muhammad Nidzam bin Yaakob2 Abstract Creating a high-quality learning environment where students can solve real-world problems and be receptive is essential for fostering students’ innovation competencies. Using appropriate pedagogical strategies and classroom activities is a crucial aspect of Malaysian education. This article uses the Fuzzy Delphi Method (FDM) to design chemistry classroom teaching strategies based on the design thinking paradigm. This research involves 12 experts in purposive sampling to form a diverse panel encompassing expertise in Chemistry Education, Curriculum, Module Development, Research, and Innovation. Using the Fuzzy Delphi method (FDM), the data were analyzed. Four elements for exploratory constructs, two elements for construct interpretation, four elements for ideation, two elements for execution, and three elements for construct evolution met the FDM requirements, according to the findings. Its threshold value is less than 0.2, the expert consensus is less than 75%, and the average score of the fuzzy number is over 0.5. Encouraging design thinking in chemistry classes and thereby enhancing students’ innovation skills, this research unquestionably induces a paradigm shift in teaching practice. Article History Received May 19, 2024 Accepted September 19, 2024 Published February 23, 2025 Keywords 1. design thinking; 2. design & development; 3. innovation competencies; 4. teachers’ pedagogy; 5. chemistry education. Section Editors Habiddin Habiddin Natany Dayani de Souza Assai Highlights Designed innovative chemistry teaching strategies using the Fuzzy Delphi method. Promoted design thinking to boost students’ innovation competencies in chemistry. Emphasizes real-world problem-solving and inquisitive learning in chemistry. Integration of design thinking to foster student innovation skills. Verified expert consensus on the design thinking framework in chemistry education. This manuscript was partly presented at the meeting of the International Conference on Mathematics and Science Education (ICoMSE) 2023. https://doi.org/10.26850/1678-4618.eq.v50.2025.e1566 https://ror.org/026w31v75 mailto:liyanaaris23@gmail.com mailto:liyanaaris23@gmail.com mailto:p-norhaniza@utm.my mailto:noordayana@utm.my mailto:nurul.hanani.utm@gmail.com mailto:dr_nidzam@ipgkda.edu.my https://orcid.org/0000-0001-6231-4708 https://orcid.org/0000-0003-2446-8153 https://orcid.org/0000-0003-3707-767X https://orcid.org/0000-0002-4424-490X https://orcid.org/0009-0005-1422-4966 Original Article, Education in Chemistry and Correlated Areas https://doi.org/10.26850/1678-4618.eq.v50.2025.e1566 ISSN 1678-4618 page 2/10 1. Introduction Innovation competency emphasizes the need for an educational strategy promoting active learning and real-world problem-solving (Hero et al., 2017) and highlights the seamless technological integration of teaching and learning transitions (Falloon, 2020). Academic scholars propose the inclusion of innovation competence and its various dimensions in the curriculum to cultivate innovation competence through educational practices (Bascopé et al., 2019; Durall et al., 2022). There is consensus in the research that education positively impacts students’ creativity and innovation (Hu et al., 2016; Ovbiagbonhia et al., 2020). In contemporary days, it is imperative for academics and educational authorities to actively promote the development of students’ inventive competency (Ojeda et al., 2021). To foster the development of students’ innovation competence, educators require clear principles for designing instructional approaches and curricula that effectively enhance expected competence (Franco et al., 2019; Herodotou et al., 2019). Nevertheless, there is a massive gap between the curricular aspirations in facing real-world needs and the actual learning outcomes and competencies of students in chemistry education (Hero, Pitkäjärvi, and Matinheikki-Kokko 2021; OCDE 2018). Additionally, the current educational setting may not offer a conducive environment for fostering students’ competency in innovation (Keinänen and Kairisto-Mertanen, 2019). Studies have shown that chemistry students struggle to explain phenomena based on knowledge (Kanapathy et al. 2019) and solve problems in real-world contexts or generate original ideas from learned concepts (Broman et al., 2018). The importance of innovation skills in addressing global challenges, particularly in chemistry, has been well recognized (Garcia‐Martinez, 2021). Chemistry significantly advances many Sustainable Development Goals (SDGs) outlined by the United Nations to foster a more sustainable future by 2030. These goals include nanotechnology, sustainable energy transition, smart cities, innovative industries, and addressing social and environmental concerns (Anastas and Zimmerman, 2018). The emphasis on innovation within the National STEM Action Plan for 2017–2025 underscores the importance of cultivating expertise in innovation. The collaboration between the Federal Ministry of Science, Technology, and Innovation (MOSTI) and the Malaysian Ministries of Education (KPM) and Higher Education (KPT) encompasses various areas such as innovation, cultural research, and the enhancement of teaching and learning quality. The discipline of chemistry education equips students with the necessary skills and knowledge to foster innovation in several domains as the demand for creativity grows across multiple industries (Droescher, 2018; García-Pérez et al., 2021; Gomollón- Bel, 2020). Previous research shows that chemistry educators’ instructional techniques and classroom practices have still to cultivate innovation and problem-solving skills (Keinänen and Kairisto-Mertanen, 2019) effectively. Additionally, restricted resources and teachers’ heavy workloads pose obstacles to enhancing innovation competence in the classroom (Lo et al., 2019). Given the critical role that chemistry educators must play, this study uses a Fuzzy Delphi approach, which gathers views from a diverse panel of experts, synthesizes their knowledge, and coordinates different perspectives to create a strategic framework. This framework aims to integrate elements of design thinking into the chemistry curriculum, thereby increasing students’ innovation efficiency in a dynamic educational environment. 2. The potential of design thinking in stimulating innovation competencies The key to developing this innovation competency is creating a quality learning environment that allows students to solve real-world problems and be curious and open-minded (Keinänen et al., 2018). The question here is how the development of innovation competence and maximizing digital technology through one method can impact the development of students’ innovation competence. Scholars, among them, have proposed several solutions to apply the design thinking approach as a modern learning paradigm in the classroom (Hsiao et al., 2017; Koh et al., 2015; Zupan et al., 2018) support this viewpoint, states that when teachers use a design thinking approach to create learning materials and lectures for students, they improve student learning. The quality of the classroom improves. Design thinking should be one of the solution methods to provide students with the ability to solve problems innovatively (Pruneau et al., 2021; Scott et al., 2021). The development of innovation competence can be encouraged by creating a learning environment that promotes student engagement with real-world challenges and encourages curiosity and creativity (IDEO, 2012; Keinänen et al., 2018). Ultimately, design thinking effectively develops students’ innovation competencies (Androutsos and Brinia, 2019; Raber et al., 2018). Design thinking gives students a structured framework for developing innovation skills (Garcia- Vaquero, 2021; Lynch et al., 2021). By embracing design thinking, students are equipped with a structured yet inventive problem- solving methodology, enabling them to approach challenges creatively and systematically. Within this structured guidance, educators play a crucial role, instilling in students the art of methodical problem-solving that fosters innovation (Jan et al., 2017). However, in the context of Malaysia, elements of the design thinking approach are still not disclosed to Science and Mathematics teachers (Adam and Halim, 2019) and teachers are still unclear about the design thinking approach and how it can be applied in the classroom (Noh and Karim, 2021; Noh, 2020) to encourage the development of students’ innovation competencies. Therefore, this study aimed to provide concrete solutions to stimulate and enhance student innovation competencies and employ the Fuzzy Delphi Method to determine the elements of Design Thinking in Chemistry Classroom Teaching Strategies. The research questions that need to be answered are: 1. What are the elements of design thinking implementation in chemistry classroom education through expert consensus? 2. What are this item’s values and rankings for each element based on an expert consensus? 3. Research design This research adopts the Fuzzy Delphi technique to gain expert approval. The Fuzzy Delphi technique, or the Fuzzy Delphi method (FDM), is a measurement and tool developed or modified from the Delphi method. As a result, FDM is not a new method because it is based on the classic Delphi method, which has been widely used and accepted in many studies (Cone and Unni, 2020; Jamil and Noh, 2020). The selection of experts is significant in meeting the consensus of experts in this FDM as it involves a process of agreement from a group of experts to verify, evaluate, reject, or add elements to the framework. Thus, selecting experts is https://doi.org/10.26850/1678-4618.eq.v50.2025.e1566 https://doi.org/10.26850/1678-4618.eq.v50.2025.e1566 Original Article, Education in Chemistry and Correlated Areas https://doi.org/10.26850/1678-4618.eq.v50.2025.e1566 ISSN 1678-4618 page 3/10 critical to meet the context of expert consensus in this FDM method. The FDM can be a more effective measurement tool in placing the strength of element selection for design thinking elements in chemistry classroom teaching strategies based on expert consensus. Studies also prove that this method can solve problems with inaccuracy and uncertainty for a study (Bui et al., 2020; Lim et al., 2021; Zhu et al., 2023). Berliner (2004) emphasizes the significance of selecting experts with a minimum of five years of consistent expertise to guarantee a comprehensive understanding of the issues under study. This study recruited a purposeful sample of 12 diverse experts (Table 1) consistent with the recommendation of Adler and Ziglio (1996), who suggest involving 10 to 15 experts in the Delphi method to obtain a high consensus. The criteria for the experts were as follows: 1. Experts in chemistry or STEM education; 2. Expert in curriculum and instruction development; 3. Experts in module development; 4. Experts in development, research, and innovation. Table 1. Fuzzy Delphi method expert demography. Expert Institution Position Experience Field Expertise 1 Public University Senior Lecturer More than 21 years - Chemistry education - Chemistry curriculum development 2 Public University Associates Professor More than 21 years - STEM education - STEM culture study center 3 Public University Associates Professor More than 21 years - STEM Education 4 Public University Senior Lecturer 6–10 years - Chemistry education - Module development 5 Public University Senior Lecturer 11–15 years - Curriculum and instruction development - Module development 6 Public University Senior Lecturer More than 20 years - Module development - Development, research, and innovation 7 Public University Senior Lecturer/ Professional Chemists 15–20 years - Chemistry education 8 Public University Senior Lecturer 11–15 years - Chemistry education - Module development 9 Institute of Teacher Education Senior Lecturer More than 21 years - Curriculum and instruction development - Development, research, and innovation 10 Institute of Teacher Education Lecturer 6–10 years - Development, research, and innovation - Module development 11 Institute of Teacher Education Senior Lecturer 15–20 years - Development, research, and innovation 12 Education Resources and Technology Division of the Malaysian Ministry of Education Assistant director More than 21 years - Chemistry education 4. Data collection and analysis To ensure the empirical nature of this study, the researcher implemented the Fuzzy Delphi method following Fig. 1. Figure 1. Fuzzy Delphi method (FDM) approach. Source: Retrieved from Jamil et al. (2015). Step 1: Determination and selection of experts The researcher sought out specialists who met specific criteria. A letter of appointment and approval as an expert panel was sent to experts willing to participate in the research. Step 2: The construction of the experts’ questionnaire This study created a questionnaire using a standards-based literature review and expert consultations (Sekaran and Bougi, 2016). The fuzzy questionnaire presented is used to obtain expert agreement on the elements in the required activity design components, as shown in Table 2. The questionnaire employed the seven-point Likert scale to reduce ambiguity and increase expert consensus. Noh, Halili and Siraj (2020) and Jamil et al. (2015), revealed that 7-point Likert scales are more accurate than 5-point ones. This scale is transformed into a fuzzy set with unit intervals between 0 and 1. The Triangular Fuzzy Number represents the lowest, reasonable, and maximum m1, m2, and m3 (Fig. 2). Table 2. Number of items for each element in the questionnaire. Implementation of the IDEO Design Thinking Model in Chemistry Learning Design thinking phase Number of item Exploration 4 Interpretation 4 Ideation 4 Execution 3 Reflection 4 Source: Elaborated by the authors using the literature review: IDEO (2012); Kalkbrenner and Horton-Parker (2016); Ovbiagbonhia et al., (2023); Auernhammer and Roth (2021); Pande and Bharathi (2020); Abidin (2020). Figure 2. Triangular Fuzzy Number. Source: Retrieved from (Siraj et al., 2021). https://doi.org/10.26850/1678-4618.eq.v50.2025.e1566 https://doi.org/10.26850/1678-4618.eq.v50.2025.e1566 Original Article, Education in Chemistry and Correlated Areas https://doi.org/10.26850/1678-4618.eq.v50.2025.e1566 ISSN 1678-4618 page 4/10 The field experts rated their agreement with the assertions, facilitating content validation through this process. This fuzzy approach allows for a more refined analysis, accommodating the uncertainty inherent in human judgment. For example, when an expert rates “Students cooperate effectively during group assignments” with a score of 5 on a 7-point Likert scale, indicating agreement, the Triangular Fuzzy Number (TFN) translates it into a fuzzy scale range (0.50, 0.70, 0.90) representing a 50% agreement value for m1, 70% for m2 and 90% for m3. This range captures the potential variation in expert opinion, reflecting a single point of agreement and a spectrum that can be slightly skewed toward a percentage of agreement. Ambiguity at this scale improves data accuracy, makes analysis more robust, and reflects the real-world complexity inherent in expert judgment. Step 3: Dissemination and data collection This phase involved distributing surveys to recognized experts using one of two methods: There are two main ways to engage experts: Meeting with each expert in person or using email for conversation and information sharing. Step 4: Conversion of Likert scale to fuzzy scale The linguistic variables are converted into fuzzy triangular numbers, and each criterion is allocated a fuzzy rij number to indicate the expert’s competence. Data Average Value was determined using a Delphi Fuzzy analysis template intended for Microsoft Excel (Eq. 1). 𝐼 = 1 … 𝑚, 𝑗 = 1, … 𝑛, 𝐾 = 1 … . , 𝑘 𝑎𝑛𝑑 (1) 𝑟𝑖𝑗 = 1/𝐾 (𝑟¹𝑖𝑗 ± 𝑟²𝑖𝑗 ± 𝑟ᴷ𝑖𝑗) Step 5: Data analysis To obtain the agreement and consensus of the expert panel, three main conditions must be met, which rely on the triangular fuzzy number and the defuzzification process. The condition for triangular fuzzy numbers is to involve the threshold value (d) and the percentage of expert agreement. For the defuzzification process, there is only one condition: the fuzzy score value (A). These three conditions will be analyzed using Microsoft Excel. Table 3 shows the interpretation of the Triangular score values of the Fuzzy Number and Defuzzification Process to measure the consensus of the expert group. Table 3. Interpretation of score values for acceptance conditions based on expert agreement Fuzzy Delphi method data analysis (FDM). Condition Process Criterion Value Interpretation 1 Triangular fuzzy number Threshold value (d) Threshold (d) ≤ 0.2 (equal or less than 0.2) Accepted (Chen, 2000; Cheng and Lin, 2002) 2 Triangular fuzzy number The percentage of expert agreement Percentage of expert agreement ≥ 75% (equal to or greater than 75%) Accepted (Chu and Hwang, 2008; Murry and Hammons, 1995) 3 Defuzzification process The fuzzy score value (A) The fuzzy score value (A) ≥ 0.5 (α– cut value equal to or greater than 0.5) Accepted (Ranking) (Bodjanova, 2006; Tang and Wu, 2010) a: Determining Threshold Value (d) Each item’s threshold value (d) must be less than or equal to 0.2 to reflect experts’ consensus (Cheng and Lin, 2002). The Eq. 2 calculated the distances between two fuzzy numbers, m = (m1, m2, m3) and n = (n1, n2, n3). 𝑑(�̃�, �̃�) = √ 1 3 [(𝑚1 − 𝑛1)2 + (𝑚2 − 𝑛2)2 + (𝑚3 − 𝑛3)2] (2) b: Expert Consensus Percentage Expert consensus must exceed 75% to indicate agreement. Non-agreement items were eliminated (Garriga et al., 2016). c: Defuzzification Process The defuzzification method determined item scores and rankings. The symbol for defuzzification is Amax. The fuzzy score (A) must be greater than the median value (α-cut value) of 0.5 (Tang and Wu, 2010) to indicate expert agreement and item acceptance. If (A) exceeds 0.5, it signifies expert consensus to accept the item in the question (Bodjanova, 2006). This α-cut- based decision-making process is a critical determinant in accepting or rejecting items within the study (Eq. 3). 𝐴𝑚𝑎𝑥 = 1 3 × (𝑚1 + 𝑚2 + 𝑚3) 𝐴𝑚𝑎𝑥 = 1 4 × (𝑚1 + 2𝑚2 + 𝑚3) (3) 𝐴𝑚𝑎𝑥 = 1 6 × (𝑚1 + 4𝑚2 + 𝑚3) 5. Findings and results 5.1. What are the elements of design thinking implementation in chemistry classroom education? Dewey’s Experiential Learning Theory (Roberts, 2003) and IDEO’s design thinking model (IDEO, 2012) have been implemented in developing activities based on design thinking in chemistry classes. The activity design is guided by the Curriculum Standard Document and Chemistry Assessment in the Secondary School Standard Curriculum (BPK 2018). Area 6.0 on the topic of acids, bases, and salts is mapped to content standards (SK) with design steps that students will undertake. For activity 1: Design of environmentally friendly washing soap, there are three content standards involved, namely SK 6.1, SK 6.2, and SK 6.3. For activity 2, the smoke filter, Eco involves SK 6.4 and SK 6.7. Based on the findings, there are five elements of design thinking implementation in Chemistry Classroom Teaching Strategies based on the experts’ consensus. The elements are exploration, interpretation, ideation, execution, and reflection. These five phases are used as a structured phase framework to improve the quality of the teaching and learning process through the integration of design thinking. Students learn to approach problems structured and systematically and develop the problem-solving approach required to enhance innovation competencies. Teaching and learning activities are systematically mapped to the design components https://doi.org/10.26850/1678-4618.eq.v50.2025.e1566 https://doi.org/10.26850/1678-4618.eq.v50.2025.e1566 Original Article, Education in Chemistry and Correlated Areas https://doi.org/10.26850/1678-4618.eq.v50.2025.e1566 ISSN 1678-4618 page 5/10 corresponding with a phase of the IDEO Design Thinking Model (IDEO, 2012): Exploration, Interpretation, Ideation, Execution, and Reflection. The parameters of each phase from the IDEO model are integrated into elements of our activity design, which results in a holistic learning experience. Practice examples in Table 4 adapt the activity design types proposed and refined via expert consensus to show how these phases of design thinking can be applied. Table 4. Design thinking elements in chemistry classroom teaching strategies. Item Elements Exploration Phase E1.1 Project Plan: Students build structured plans, assign distributions in groups, and outline plans to follow during their design thinking project assignment. Students make strategies, plan action plans to overcome environmental issues and distribute tasks. E1.2 Induction: The teacher raises the issue of the problem and introduces students to the project ’s objectives, methods, and expectations. Students answer KNOWLEDGE practice questions, and students explore “Acids and Alkalies: How do they affect the Environment”. E1.3 Definition: Students actively define problem issues, clearly outlining the problem statements and objectives to be addressed. Students define related issues and list pollutants that affect the quality of the environment (like sulfur dioxide (SO2) and nitrogen oxides (NOx), which lead to acid rain, alter the pH of water bodies, and harm aquatic life). E1.4 Find Information: Students search for information from various sources and research and collect relevant data on the problem. Students explore and find information on the concept of pH for acid, base, and salt in (laboratory station activity), test the pH levels of different substances, and analyze how acidic or basic solutions can neutralize to mitigate environmental damage. Interpretation Phase E2.1 Interpretation: Students evaluate and interpret information to draw meaningful conclusions. Students connect the concept of pH value and concentration of a solution and suggest possible solutions. E2.2 Synthesis: Students synthesize the information obtained, integrate information, and generate creative solutions to address assigned problems. Combine their understanding of chemical reactions, such as neutralization, with real-world applications to propose methods to reduce the acidity of affected water bodies. E2.3 Analysis: Students analyze the exploration results, breaking down complex information to identify key patterns, trends, or relationships related to a defined problem. Students present ideas, analyze, and make connections about how the pH value of a cleaning solution affects the effectiveness of washing. E2.4 Concept mapping: Students visually organize and connect key concepts and ideas from the information gathered. Map the relationship between different types of acids and bases, their reactions with salts, and their effects on the environment. Ideation Phase E3.1 Concept: Students associate the solution idea and the chemical concepts involved. Students link the chemical concepts of acids, bases, and salts with the solution concept. Students state how the properties of acids, bases, and salts allow solutions that are suggested. E3.2 Post-it Voting: A collaborative decision-making process in which students use Post-it notes to vote for the most appropriate ideas. Evaluate ideas based on feasibility, functionality, and alignment with problems foster collaboration. E3.3 Brainstorm: Collaborative and open sessions where students generate ideas for solving defined problems. Students consider the properties and characteristics of acids, bases, and salts during brainstorming. E3.4 Solution Model: Students suggest a solution model for implementing the selected solution. Visualize and sketch a model of their solution for a smoke filter – eco. Label the main components and processes involved in their solution. Execution Phase E4.1 Solution Model Development: Students develop prototypes and detail the selected solution model. E4.2 Do & Test: Students test a prototype to solve a pre-defined problem. Students test the functionality and ability of prototypes to solve air or air pollution problems (acid, base, and agar concepts). List the apparatus and materials needed in the testing experiment. Students must propose their investigative activity procedures. List the apparatus and materials needed for the testing experiment. Students are required to propose their investigative activity procedures. E4.3 Re-Test: Revising and re-evaluating the solution model implemented based on feedback and results. Reflection Phase E5.1 Documentation: Students make project documentation systematically recording and presenting learning outcomes and modifications made throughout the design process carried out. E5.2 Feedback loops: continuous feedback, allowing for repeated improvements based on input from peers, teachers, or self-reflection. E5.3 Sustainability: Students consider developed solutions’ long-term viability and impact on environmental, social, and economic effects. E5.4 Reflection: Students reflect on the entire design process, including challenges, successes, and areas for improvement. Reflect on how design projects in creating environmentally friendly cleaning solutions challenge an understanding of chemical concepts such as pH, chemical reactions, and the properties of acids and bases. Source: Elaborated by the authors from expert views. Some elements did not meet the requirements of the acceptance of elements in the FDM analysis, such as elements in the design of the activities of the interpretation phases. These elements meet the first requirement but do not meet the second requirement; the percentage requirement of the expert agreement should be more than or equal to 75%. The synthesis elements (67%) and concept mapping (50%) in the interpretation phase, as well as Re-test elements (67%) in the implementation phase, meet the first requirement of the threshold value (d) less than 0.2 but have less than 75% of expert agreement. Meanwhile, the sustainability element (d = 0.204) in the reflection phase meets the second requirement but does not meet the first requirement of the threshold value (d) less than 0.2. https://doi.org/10.26850/1678-4618.eq.v50.2025.e1566 https://doi.org/10.26850/1678-4618.eq.v50.2025.e1566 Original Article, Education in Chemistry and Correlated Areas https://doi.org/10.26850/1678-4618.eq.v50.2025.e1566 ISSN 1678-4618 page 6/10 Therefore, as formulated in Table 5, these four elements have been rejected. The results also show the expert’s consensus on the elements in the design thinking component of the activity with the threshold value (d) of the exploration phase between (0.098 to 0.126) and the ideation phase (0.057 to 0.092) and has met the first FDM requirement in the Triangular fuzzy number which is (d) smaller than 0.2. The testing of the percentage of agreement for the second FDM conditions also showed that the design thinking element of the activity for these two phases received a high rate of agreement of 96% and 94% for the percentage of the entire component. Table 5 also presents the consensus reached among experts regarding 15 out of 19 elements in design thinking elements. In total, all four elements in the exploration phase and the ideation phase, two of the four elements in the interpretation phase, two of the three elements in the experimental phase as well as three of the four proposed elements were accepted by consensus for the design component of the activity in the design thinking implementation in chemistry classroom education. Table 5. Expert consensus on design thinking elements. Triangular Fuzzy Numbers Defuzzification Process Item Threshold Value (d) Percentage Expert Consensus (%) m1 m2 m3 Fuzzy Score (A) Ranking Results Exploration Phase E1.1 0.098 92% 0.800 0.942 0.992 0.911 3 Accepted E1.2 0.023 100% 0.883 0.992 1.000 0.958 1 Accepted E1.3 0.042 100% 0.867 0.983 1.000 0.950 2 Accepted E1.4 0.126 92% 0.767 0.917 0.975 0.886 4 Accepted Interpretation Phase E2.1 0.097 92% 0.783 0.933 0.992 0.903 2 Accepted E2.2 0.201 67% 0.617 0.800 0.925 0.781 - Rejected E2.3 0.068 100% 0.833 0.967 1.000 0.933 1 Accepted E2.4 0.147 50% 0.533 0.733 0.900 0.722 - Rejected Ideation Phase E3.1 0.082 83% 0.700 0.883 0.983 0.856 4 Accepted E3.2 0.076 100% 0.800 0.950 1.000 0.917 2 Accepted E3.3 0.057 100% 0.850 0.975 1.000 0.942 1 Accepted E3.4 0.092 92% 0.767 0.925 0.992 0.894 3 Accepted Execution Phase E4.1 0.068 100% 0.833 0.967 1.000 0.933 1 Accepted E4.2 0.068 100% 0.767 0.933 1.000 0.900 2 Accepted E4.3 0.187 67% 0.633 0.808 0.933 0.792 - Rejected Reflection Phase E5.1 0.091 83% 0.717 0.892 0.983 0.864 3 Accepted E5.2 0.095 92% 0.817 0.950 0.992 0.919 1 Accepted E5.3 0.204 83% 0.400 0.600 0.800 0.600 - Rejected E5.4 0.102 83% 0.733 0.900 0.983 0.872 2 Accepted Requirement: Triangular Fuzzy Numbers (1) Threshold Value (d) ≤ 0.2, (2) Percentage of Expert Consensus ≥ 75.0%; Defuzzification Process (3) Fuzzy Score (A) ≥ α – cut = 0.5. 5.2. What are this item’s values and rankings for each element based on an expert consensus? After successfully addressing the initial two requirements, the analysis proceeds to ascertain the fulfillment of the third FDM requirement through defuzzification analysis. Notably, the highest fuzzy score value secures the top rank within each design thinking element. In the defuzzification analysis, a higher fuzzy score means to be the most likely or agreed upon sub-element within each phase of design thinking. Presenting these higher scores first highlights the most critical items, making the key findings more prominent and impactful. The final resulting value (A) is compared against α-cut values; any score below 0.5 reflects expert consensus to reject the item, while scores exceeding 0.5 indicate sufficient literature support to adopt it (Bodjanova, 2006). The high fuzzy scores (ranging from 0.958 to 0.856) in Table 6 confirm broad consensus (Roldan Lopez et al., 2021), which is crucial for applying the design thinking framework to the chemistry education model. Overall, the analysis results indicate a logical structure within the Design Thinking process, with specific priorities for each phase. In the Exploration Phase, the Induction element, which introduces the process, holds the highest importance (A=0.958), followed closely by Definition (problem definition) at 0.950. However, Finding Information (gathering information) ranks the lowest at 0.886, suggesting that a clear understanding of the problem is paramount before collecting data. In the Interpretation Phase, the Analysis element (A=0.933) is considered more critical than Interpretation (A=0.903), emphasizing the necessity of thorough data analysis prior to drawing conclusions. Moving to the Ideation Phase, Brainstorming (creative idea generation) is the highest-ranked element (A=0.942), followed by Post-it Voting (selection of the best ideas) at 0.917, and Solution Model Development (A=0.894). The Concept (basic concept) element ranks the lowest at 0.856, implying that generating and selecting ideas should precede the development of a solution model. In the Execution Phase, Solution Model Development (A=0.933) is prioritized over Do & Test (A=0.900), underlining the importance of having a well-defined solution model before testing. Lastly, in the Reflection Phase, Feedback Loops (continuous feedback) are deemed the most crucial (A=0.919), followed by Reflection (self-reflection) at 0.872, with Documentation being the least important (A=0.864). This suggests that feedback exchanges should take precedence over mere documentation of reflections. In conclusion, this ranking https://doi.org/10.26850/1678-4618.eq.v50.2025.e1566 https://doi.org/10.26850/1678-4618.eq.v50.2025.e1566 Original Article, Education in Chemistry and Correlated Areas https://doi.org/10.26850/1678-4618.eq.v50.2025.e1566 ISSN 1678-4618 page 7/10 emphasizes the sequential and structured approach in Design Thinking, where each phase—understanding the problem, conducting analysis, generating and selecting ideas, modeling solutions, implementing them, and reflecting—must follow a clear order of priority to effectively foster innovative and systematic solutions. Figure 3 displays a visual picture of the ranking formulation of each element of the design of the activity in all phases of design thinking according to the priority position. Table 6. Ranking of elements in the design thinking phase activity to the value of Fuzzy evaluation. Item Design Thinking Activity Components Fuzzy Score (A) Ranking Design Thinking (Exploration) Elements E1.1 Project Plan 0.911 3 E1.2 Induction 0.958 1 E1.3 Definition 0.950 2 E1.4 Find Information 0.886 4 Design Thinking (Interpretation) Elements E2.1 Interpretation 0.903 2 E2.3 Analysis 0.933 1 Design Thinking (Ideation) Elements E3.1 Concept 0.856 4 E3.2 Post-it Voting 0.917 2 E3.3 Brainstorm 0.942 1 E3.4 Solution Model 0.894 3 Design Thinking (Execution) Elements E4.1 Solution Model Development 0.933 1 E4.2 Do & Test 0.900 2 Design Thinking (Reflection) Elements E5.1 Documentation 0.864 3 E5.2 Feedback loops 0.919 1 E5.4 Reflection 0.872 2 Figure 3. Activity Design Thinking Component through Fuzzy Delphi method approach. 6. Discussion The results of this study demonstrate the effective utilization of the FDM in developing teaching techniques for chemistry classrooms that align with the design thinking paradigm. The consensus levels among the expert panel, which ranged from 83 to 100%, validate the appropriateness and comprehensiveness of the proposed design thinking principles in the context of chemistry education. The elements classified as exploration, interpretation, ideation, execution, and reflection jointly play a significant role in nurturing students’ innovation competencies. The consensus reached, as indicated by the low threshold values (d ≤ 0.2), demonstrates that experts have agreed on the fundamental elements required for a learning environment to be effective. The value of supporting inquiry-driven learning and problem-solving in the Malaysian school setting is highlighted by this consensus, which is consistent with earlier research that emphasizes the importance of active learning approaches (Halim et al., 2022; Maneeratana et al., 2019). In addition, the examination of defuzzification provides valuable insights into the relative importance of each part of design thinking. The Fuzzy score values, which regularly range from 0.856 to 0.958, emphasize the importance of each element in effectively implementing design thinking ideas in chemistry teaching. The prominence of the exploration element in the exploration portion is particularly noteworthy, as it emphasizes the importance of enabling students to engage with real-world situations to frame challenges appropriately. Likewise, prioritizing analysis and interpretation components highlights the significance of employing critical thinking during the ideation process. The above findings align with prior research that underscores the crucial significance of design thinking in cultivating innovation skills and aptitude for resolving problems (Buhl et al., 2019; Ellah, et al., 2019) 7. Conclusions In conclusion, this research makes a valuable contribution to the advancement of pedagogical approaches in the field of chemistry education in Malaysia. It achieves this by offering empirically supported design thinking components that align with the requirements for fostering students’ innovation competencies. The efficacy of employing the Fuzzy Delphi method to establish consensus among experts serves to enhance the validity of the offered plans. The results emphasize the necessity for educational establishments to integrate design thinking principles, thereby fostering an environment that motivates students to engage in exploration, ideation, and implementation to address authentic challenges. By emphasizing these components, educators can cultivate a dynamic educational setting that encourages creativity, critical thinking, and innovation within the student body. The findings derived from this research can provide a basis for educators, curriculum creators, and policymakers to formulate and execute efficacious approaches for fostering student innovation competencies. Authors’ contribution Conceptualization: Norliyana Binti Md. Aris; Nor Hasniza Binti Ibrahim; Noor Dayana Binti Abd Halim; Data curation: Norliyana Binti Md. Aris; Muhammad Nidzam bin Yaakob; Formal Analysis: Norliyana Binti Md. Aris; Nor Hasniza Binti Ibrahim; Noor Dayana Binti Abd Halim; Funding acquisition: Norliyana Binti Md. Aris; Investigation: Norliyana Binti Md. Aris; Methodology: Norliyana Binti Md. Aris; Muhammad Nidzam bin Yaakob; Project administration: Norliyana Binti Md. Aris; Nurul Hanani Binti Rusli; Resources: Norliyana Binti Md. Aris; Nor Hasniza Binti Ibrahim; Noor Dayana Binti Abd Halim; Software: Not applicable. Supervision: Nor Hasniza Binti Ibrahim; Noor Dayana Binti Abd Halim; Validation: Nor Hasniza Binti Ibrahim; Noor Dayana Binti Abd Halim; Visualization: Norliyana Binti Md. Aris; Nurul Hanani Binti Rusli; Writing – original draft: Norliyana Binti Md. Aris; Nor Hasniza Binti Ibrahim; Noor Dayana Binti Abd Halim; Nurul Hanani Binti Rusli; Writing – review & editing: Norliyana Binti Md. Aris; Nor Hasniza Binti Ibrahim; Noor Dayana Binti Abd Halim. https://doi.org/10.26850/1678-4618.eq.v50.2025.e1566 https://doi.org/10.26850/1678-4618.eq.v50.2025.e1566 Original Article, Education in Chemistry and Correlated Areas https://doi.org/10.26850/1678-4618.eq.v50.2025.e1566 ISSN 1678-4618 page 8/10 Data availability statement All data sets were generated or analyzed in the current study. Funding Not applicable. Acknowledgments We would like to express our sincere gratitude to the editors of Eclética Química for their valuable feedback and insightful suggestions, which significantly improved the quality of this manuscript. Our thanks also go to the technical team for their support in manuscript formatting, and technical assistance throughout the research process Conflict of interest The authors declare that there is no conflict of interest. References Abidin, M. D. S. Z. Panduan Asas Pemikiran Reka Bentuk Produk (UiTM Press). UiTM Press. 2020. Adam, N. A.; Halim, L. 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