INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XII December 2025  
Validating a Design Thinking Learning Model for Developing IoT  
Projects through Expert Evaluation  
Salbiah Zainal*., Rasimah Che Mohd Yusoff., Roslina Ibrahim., Saharudin Ismail  
Faculty of Artificial Intelligence, Universiti Teknologi Malaysia, Malaysia  
*Corresponding author  
DOI: https://doi.org/10.47772/IJRISS.2025.91200011  
Received: 10 December 2025; Accepted: 17 December 2025; Published: 30 December 2025  
ABSTRACT  
The Internet of Things (IoT) connects sensors, embedded devices, and digital platforms to enable intelligent  
interactions between people and their environments. Engaging students in IoT projects exposes them to authentic  
challenges where programming, data analysis, and system design converge. However, many educators struggle  
to guide learners from design to functional prototypes. The absence of validated pedagogical models that link  
creative ideation with technical implementation continues to limit the effectiveness of IoT education. This study  
proposes an initial validated learning model for developing Internet of Things (IoT) projects through a Design  
Thinking (DT) approach. The model integrates DT principles with the Initiator-Before-In-After (IBIA) teaching  
sequence and the Flex blended-learning structure. Expert judgment was used to validate the class activities for  
both lecturers and students based on the proposed conceptual model for developing IoT Projects through DT  
approach. Six experts specialising in DT, learning innovation, and IoT education evaluated the model using the  
Content Validity Index (CVI) and the Content Validity Ratio (CVR), complemented by qualitative feedback.  
Quantitative analysis determined the model’s content validity, while thematic interpretation of expert comments  
informed refinements to strengthen instructional relevance.  
Keywords: Design Thinking, Internet of Things (IoT), Expert evaluation, Blended learning, Content validity  
INTRODUCTION  
Digital transformation is reshaping the ways people learn, work, and interact within society. The growing  
presence of artificial intelligence, the Internet of Things (IoT), and data-driven systems requires graduates who  
are capable of thinking critically, designing creatively, and acting responsibly within complex technological  
environments. These attributes, often described as essential future skills for a digital society, combine technical  
fluency with creativity, ethical understanding, and social adaptability (OECD, 2023; Vuorikari et al., 2022). In  
this context, universities are expected to design learning environments that connect disciplinary knowledge with  
authentic technological practice. One emerging direction is the use of Flex Learning, which blends asynchronous  
online study with interactive face-to-face engagement. This model allows students to prepare before class  
through online materials, collaborate during in-class sessions, and consolidate their understanding through  
reflection and post-activity review.  
Design Thinking (DT) has become a central pedagogical approach for cultivating these future-ready capabilities.  
It encourages empathy, experimentation, and reflection through iterative problem solving that can be applied  
across both digital and physical learning spaces. The philosophy behind DT is rooted in experiential and  
constructivist learning theories, where students are viewed as active creators of knowledge rather than passive  
receivers (Kolb, 2015; Razzouk & Shute, 2012). In a blended learning setting, online modules can support  
conceptual exploration and background research, while classroom sessions provide opportunities for hands-on  
prototyping and collaborative feedback. This combination promotes continuous engagement, allowing learners  
to connect theory with practice and strengthen their sense of agency. Despite its pedagogical strengths, studies  
that systematically integrate and validate DT within IoT-based project learning remain limited (Nordin et al.,  
2024; Zainal et al., 2021).  
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The Internet of Things connects sensors, embedded devices, and digital platforms to create intelligent interaction  
between humans and their environments (Guerra-Manzanares & Bahsi, 2023; Corpuz, Cruz, Palomar, Tamayo,  
Bongao, Enojas & Morgado, 2025). When students learn through IoT projects, they engage with authentic design  
challenges that merge programming, data analytics, and systems thinking. A flexible learning structure supports  
these activities: asynchronous modules introduce technical foundations and coding tutorials, while face-to-face  
sessions enable collaborative troubleshooting and prototype development. Such arrangements help learners  
cultivate problem-solving ability, computational reasoning, and design literacy (Liebkemann, 2021;  
McCormack, 2021). However, educators often face challenges in guiding students from conceptual ideas to  
working prototypes. The absence of validated teaching models that link creative ideation with technical  
implementation continues to limit learning outcomes (Henriksen et al., 2021; OECD, 2023)  
Malaysia and other ASEAN countries are shifting from a focus on Fourth Industrial Revolution skills toward  
cultivating broader future-oriented digital and innovation competencies. National initiatives such as the Malaysia  
Digital Education Policy (2023), the Malaysia Education Blueprint 2013-2025 and UNESCO’s Digital Learning  
Compass (2023) emphasize quality education, creativity, digital literacy, and ethical engagement in technology-  
enhanced learning. These initiatives highlight the need for teaching models that integrate both conceptual  
understanding and technological practice within flexible, blended environments. Universities are therefore  
encouraged to adopt learning structures that combine self-directed online engagement with collaborative  
classroom experiences to develop well-rounded, innovative graduates.  
The present study responds to this educational demand by validating a structured model for IoT project  
development using a DT framework for blended learning. The proposed model aligns the three principal DT  
phases (Inspiration, Ideation, and Implementation) with the Initiator-Before-In-After (IBIA) teaching sequence  
to ensure continuity between online preparation, classroom collaboration, and post-activity reflection. Expert  
judgment is used to assess the clarity and relevance of each component using the Content Validity Index (CVI)  
supported by qualitative evaluation. Through this validation process, the model is refined to ensure alignment  
with pedagogical objectives and professional practice. The outcome contributes to an evidence-based framework  
that supports creativity, empathy, collaboration, and technological fluency as key components of future-oriented  
learning in a digital society.  
Related Works  
Design Thinking In Teaching and Learning  
Design Thinking (DT) is a human-centered approach to problem-solving that emphasizes creativity, empathy,  
and iterative prototyping. It integrates the way designers think and work into other disciplines to generate  
innovative solutions to complex problems. DT approach was then popularized and systematized by David  
Kelley, Tim Brown, and their team at IDEO, a global design and innovation consultancy. They applied Design  
Thinking in business, engineering, and education contexts, making it widely known as a structured process  
involving stages such as empathize, define, ideate, prototype, and test (Laverty & Littel, 2022). Now, DT has  
gained wide recognition as a framework for fostering innovation, creativity, and problem-solving in education  
(Baran & AlZoubi, 2024). It promotes learner-centred exploration through empathy, ideation, and prototyping,  
which correspond to higher-order cognitive processes in Bloom’s taxonomy (Razzouk & Shute, 2012). Research  
has shown that DT enables students to approach complex and ambiguous problems while developing  
collaboration and metacognitive reflection (Henriksen et al., 2021). Figure 1 shows the basic phases of DT.  
Figure 1: Basic Phases of Design Thingking (Brown, 2008)  
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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XII December 2025  
In teaching and learning environments, DT bridges the gap between abstract theoretical knowledge and practical  
application. Studies in engineering, design, and computer science indicate that DT-driven activities enhance  
students’ ability to transfer knowledge across contexts and improve engagement through experiential learning  
(Wrigley & Straker, 2015).Moreover, Panke (2019) highlighted that DT fosters ethical reasoning and digital  
citizenship, especially when learners engage in projects involving emerging technologies such as artificial  
intelligence and the Internet of Things. By placing learners at the centre of problem exploration, DT develops  
critical dispositions for adaptive expertise, aligning well with 21st-century competencies.  
Despite its increasing adoption, the instructional design of DT courses often lacks systematic validation. Many  
reported applications focus on classroom implementation without evaluating the internal consistency or  
theoretical alignment of DT stages with measurable outcomes (Jiang & Pang, 2023; Zainal et al., 2021).  
Consequently, rigorous validation studies are necessary to ensure that DT frameworks remain credible tools for  
educational innovation.  
IoT Project-Based Learning  
The Internet of Things (IoT) represents a transformative domain within computing and engineering education,  
connecting devices, data, and human interactions. IoT-oriented learning requires students to integrate hardware  
programming, data communication, and system design, which aligns naturally with project-based learning (PBL)  
principles(Atlam & Wills, 2018; Iqbal, 2023). PBL provides a context in which learners can construct knowledge  
through authentic challenges, designing prototypes that collect and analyse real-time data (Rodriguez‐Sanchez  
et al., 2024).  
Empirical studies indicate that IoT-based PBL enhances both technical and cognitive outcomes. Akiyama and  
Cunningham (2018) found that students who engaged in self-directed IoT prototype development demonstrated  
stronger conceptual mastery and collaborative problem-solving skills. Similarly, Purba and Zunidar (2025)  
reported that IoT projects promote computational thinking and the integration of abstract programming concepts  
with tangible systems. Yet, educators often struggle to balance creative autonomy with structured guidance,  
resulting in variable learning quality and incomplete project execution (Trishaank et al., 2024).  
Integrating Design Thinking principles within IoT project work provides a structured pathway to balance  
creativity with analytical rigour. The iterative DT cycle of empathise, define, ideate, prototype, and test offers  
an adaptable scaffold. Thus, supports problem identification, ideation, and iterative refinement. Early research  
by Zainal et al. (2021) and Choi et al. (2024) suggests that DT-IoT integration promotes motivation and  
innovation in students. However, these studies remain exploratory and lack systematic validation through expert  
evaluation. The empirical validation of the instructional activities, especially through expert review, has not been  
systematically addressed..  
Expert Validation in Model Development  
The expert validation is an established procedure for verifying the clarity, relevance, and construct validity of  
educational models. It provides empirical assurance that a proposed framework accurately represents its  
theoretical basis and intended learning outcomes. The Content Validity Index (CVI) and Content Validity Ratio  
(CVR), introduced by Lynn (1986) and refined by Polit and Beck (2006), are among the most widely used  
quantitative measures for such validation. These tools quantify the degree of expert consensus regarding item  
relevance, ensuring methodological transparency and content integrity.  
Several studies illustrate the significance of expert validation in technology-enhanced education. Juan et al.  
(2025) validated a competency model for entrepreneurship education using expert panels, achieving strong  
agreement levels. Likewise, Pueyo‐Garrigues et al. (2021) used expert validation to adapt a professional  
knowledge questionnaire in healthcare education, demonstrating improved reliability. These examples  
underscore that systematic validation enhances both the credibility and pedagogical value of instructional  
frameworks. However, only a few researchers within the field of design-based learning have applied structured  
validation methods to the instructional process itself. Most DT or IoT frameworks are reported descriptively,  
with minimal statistical confirmation of content validity. Incorporating expert evaluation grounded in CVI and  
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CVR analysis provides an evidence-based pathway to strengthen educational design research. The present study  
contributes to this methodological advancement by employing both quantitative indices and qualitative feedback  
to validate a set of structured activities that merge DT and IoT project-based learning  
Conceptual Model  
Theoretical Foundation  
The conceptual model proposed in this study is grounded in two complementary learning theories:  
constructivism and experiential learning. Together, they provide the philosophical rationale for linking  
knowledge construction, reflection, and ethical technological practice in IoT project or prototype development.  
Constructivism emphasises that learners actively build understanding through engagement with authentic  
contexts rather than absorbing information passively. Learning emerges through collaboration, negotiation, and  
adaptation of prior knowledge information (Piaget & Duckworth, 1970; Vygotsky, 1978). Kolb (2015). The  
experiential-learning cycle expands this idea by describing how experience, reflection, conceptualisation, and  
experimentation operate as a continuous process. Within IoT projects, students generate and refine ideas by  
testing prototypes and reflecting on performance, thereby transforming hands-on experience into knowledge.  
Besides constructivism and experiential learning learning theories, DT operationalises these theories by  
providing a structured process of empathising with users, defining problems, ideating, prototyping, and testing.  
Each stage engages students in active experimentation and reflective analysis, turning abstract theory into  
practical competence (Henriksen et al., 2021; OECD, 2023). Moreover, the integration of human-centred design  
adds an ethical and social dimension, ensuring that technological innovation serves real human needs and  
promotes sustainability values (Brown & Wyatt, 2015). Within this theoretical convergence, learners acquire not  
only technical proficiency but also empathy, creativity, and reflective judgement. These qualities are essential  
for a future-oriented digital society, ensuring relevance to contemporary educational priorities.  
The Initiator-Before-In-After (IBIA) teaching strategy provides a structured sequence that reinforces reflection  
and continuity throughout the DT process. It ensures that learning moves logically from preparation to practice  
and then to consolidation. Within the proposed model, IBIA functions as the pedagogical rhythm that anchors  
student activities within each DT phase. The Initiator stage introduces authentic IoT contexts that spark curiosity  
and situate learning in real-world relevance. Educators present short case examples or demonstration problems  
that connect classroom concepts to community or industry needs. During the Before stage, students acquire  
prerequisite knowledge through brief lectures, online modules, or guided readings. This stage prepares learners  
conceptually for the design tasks that follow. The In stage encompasses the main Design Thinking activities,  
including problem analysis, ideation, prototyping, and testing. Students apply theoretical knowledge in  
collaborative settings and receive formative feedback. The final After stage emphasises reflection, peer  
evaluation, and revision of both artefacts and processes, transforming experience into deeper understanding. The  
cyclic nature of IBIA aligns with Kolb’s experiential-learning model, allowing repeated movement between  
action and reflection. It also reduces cognitive overload by alternating structured input with exploratory practice.  
Within IoT education, IBIA supports balanced engagement, ensuring that creativity is grounded in conceptual  
understanding and that learning outcomes remain measurable and transferable.  
Blended learning is an educational approach that combines traditional classroom instruction with online learning  
experiences. It integrates face-to-face teaching, online collaborative activities, and self-paced learning to create  
a flexible and personalized learning environment. This approach aims to leverage the strengths of both in-person  
and digital learning methods to improve student engagement and outcomes Flex model is a type of blended  
learning where content is delivered primarily online, but a teacher is available on-site to provide support as  
needed. The model combines online self-paced learning with in-person support. Students have control over their  
learning pace and schedule (Sukumaran, 2018). The model includes collaborative learning and personalized  
interventions as shown in Figure 2.  
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Figure 2: Flex model of blended learning (Sukumaran, 2018)  
The key components of the Flex model, as shown in the diagram are:  
Self-paced Online Learning: This is the "backbone" of the Flex model where students primarily engage with  
digital learning materials and activities at their own pace in a physical classroom or on campus. This allows  
advanced students to move ahead and struggling students to take more time as needed.  
Collaborative Learning: While most instruction is online, teachers facilitate group projects, discussions, and  
other collaborative activities. This helps students develop social and interpersonal skills.  
Personalised Interventions: The teacher-of-record is on-site to provide face-to-face support on an as-needed  
basis. This can include small-group instruction, individual tutoring, and guidance to help students who are  
struggling or need help. The teacher acts as a mentor and guide rather than the primary deliverer of content  
The Flex model of blended learning provides the delivery framework that supports the DT and IBIA processes.  
In this structure, online components deliver fundamental knowledge such as IoT architecture, sensor  
programming, or design-thinking theory through asynchronous learning modules. Classroom sessions are  
reserved for collaborative experimentation, prototype construction, and consultation with instructors. This  
arrangement aligns with principles of self-regulated learning, allowing students to study theoretical materials at  
their own pace while using face-to-face time for higher-order application (Garcia Moreno, 2024; Horn & Staker,  
2014). The approach also accommodates diverse learning preferences and provides accessibility for students  
who may face time or location constraints.  
By combining flexibility with structured guidance, the model ensures continuity between digital and physical  
learning spaces. It positions the instructor as a mentor who facilitates inquiry, monitors progress, and provides  
formative feedback through both online and offline platforms. The Flex blended-learning architecture provides  
the environmental structure that supports both the DT cycle and the IBIA teaching sequence. It combines online  
and face-to-face modalities to create a continuous learning experience that connects conceptual understanding  
with hands-on application. Within this arrangement, digital modules deliver foundational knowledge  
asynchronously, while classroom time is devoted to collaboration, prototype development, and consultation.  
Online components include multimedia lessons, coding simulations, and formative quizzes that allow students  
to learn at their own pace and revisit complex topics as needed. The physical classroom serves as a collaborative  
workshop where students test ideas, assemble hardware, and interact with instructors for immediate feedback.  
This dual structure enables differentiated learning paths and promotes self-regulation, two principles central to  
learner autonomy (Horn & Staker, 2014).  
Research in blended education indicates that flexibility improves engagement and knowledge retention when  
supported by mentoring and feedback loops (Garcia Moreno, 2024). In the proposed model, the instructor acts  
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as a facilitator who monitors student progress across both digital and physical environments. The Flex approach  
therefore, strengthens the alignment between individual learning, teamwork, and reflective practice. It also  
enhances accessibility, enabling students to manage their own learning schedules while maintaining structured  
guidance within the project life cycle. Table 1 shows the roles and learning activities within the Flex Model.  
Table 1: Roles and Learning Activities within the Flex Model  
Environment  
Lecturer Role  
Student Activities  
Learning Focus  
Classroom (face- Facilitator  
and Prototype, collaborate, receive Application,  
feedback reflection  
teamwork,  
to-face)  
mentor  
Online  
(asynchronous)  
Content curator and Study tutorials, complete coding Conceptual understanding and  
monitor tasks, and discuss in forums preparation  
Conceptual validation logic and synthesis  
The conceptual model integrates theory, pedagogy, and delivery in a structure designed for both teaching  
application and empirical validation. Because instructional models represent theoretical propositions about how  
learning occurs, they require systematic testing to confirm coherence and relevance. The present framework is  
therefore constructed with explicit mechanisms for validation through expert judgement. Quantitative and  
qualitative evaluation provides evidence that the model’s components are pedagogically sound and practically  
feasible.  
The validation process follows the principles of design-based research, where theory development and empirical  
testing evolve iteratively. The Content Validity Index (CVI) and Content Validity Ratio (CVR) are applied to  
measure expert consensus on the relevance, clarity, and appropriateness of each activity (Elangovan &  
Sundaravel, 2021; Polit & Beck, 2006). Expert commentary further contextualises these scores, allowing  
refinement of the learning steps and ensuring alignment with constructivist and experiential principles.  
Validation thus operates as both methodological verification and theoretical refinement, confirming that the  
framework functions as an evidence-based pedagogical design.  
In synthesis, the model establishes a pathway from theoretical foundations to practical implementation.  
Constructivism and experiential learning define the epistemological base, DT structures the learning process,  
IBIA provides pedagogical rhythm, and the Flex model delivers an adaptive environment. Together, they form  
a cohesive framework that supports creativity, technical competence, and reflective digital citizenship as the key  
attributes of learners in the evolving digital society.  
Figure 3 shows the conceptual model for developing IoT projects through the DT approach. The model  
integrates DT principles with the Initiator-Before-In-After (IBIA) teaching sequence and the Flex blended-  
learning structure. Anchored in the principles of DT, Flex, and IBIA instructional strategies, the model seeks to  
cultivate essential 21st-century competencies among students, including creativity, collaboration, technological  
fluency in IoT, and ethical awareness. These outcomes align with the goal of nurturing learners who are  
innovative, adaptable, and ethically responsible in a technology-driven environment.  
Figure 3: Conceptual Model for Developing IoT Projects Through DT Approach  
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METHODOLOGY  
Research Design  
This study employed a design-based initial validation approach to examine the pedagogical soundness and clarity  
of the proposed framework implemented within a Flex Learning environment. Design-based research is suitable  
for developing and refining instructional models because it integrates empirical evidence with theoretical  
reasoning in iterative cycles of improvement (McKenney & Reeves, 2018). In this study, validation combined  
quantitative techniques, including the Content Validity Index (CVI) and the Content Validity Ratio (CVR), with  
qualitative expert feedback to ensure both theoretical alignment and practical relevance.  
The procedure comprised three sequential stages. First, a validation instrument was developed directly from the  
conceptual model. Second, six domain experts independently reviewed the learning components and rated their  
relevance, clarity, and feasibility. Third, the quantitative and qualitative data were synthesised to refine the  
framework for instructional application. This structured process ensured that the framework was not only  
conceptually coherent but also feasible for classroom use.  
Paticipants  
Six experts were selected using purposive sampling, following established guidelines for content-validation  
research (Lynn, 1986; Polit & Beck, 2006). Three experts specialised in Design Thinking and educational  
innovation, while three represented IoT and computing education. All participants held postgraduate  
qualifications and had at least five years of relevant academic or professional experience. Expert selection was  
based on three criteria: disciplinary expertise in Design Thinking or IoT, familiarity with project-based or  
blended learning environments, and willingness to provide detailed feedback. This balanced composition of  
pedagogical and technical experts ensured that the evaluation addressed both educational design and  
technological integration, enhancing the comprehensiveness of the validation outcomes.  
Research Instrument  
The validation instrument was designed to evaluate each activity and sub-phase of the conceptual model. It  
consisted of structured items rated on a five-point Likert scale, where 1 indicated “not relevant,” 2 “somewhat  
relevant,” 3 “moderately relevant,” 4 “very relevant,” and 5 “highly relevant.” Open-ended questions were also  
included to capture qualitative comments and improvement suggestions. Items were grouped according to two  
dimensions: the three Design Thinking phases (Inspiration, Ideation, and Implementation) and the IBIA teaching  
sequence (Initiator, Before, In, and After). This arrangement enabled precise identification of strong and weak  
components within each learning phase. The instrument was pilot-reviewed by two independent educators to  
confirm clarity and alignment before distribution to the expert panel. Appendix A shows the instrument which  
consists of the activities in a design thinking learning model for developing IoT Projects.  
Data Collection  
Data were collected electronically to enable participation from geographically dispersed experts. Each expert  
received an information sheet, consent form, and a digital version of the conceptual model and rating instrument.  
Participants were given two weeks to complete the review and return their responses via a secure online form.  
Follow-up correspondence clarified any uncertainties and ensured consistency across interpretations.  
Quantitative ratings were entered into a spreadsheet for CVI and CVR computation, while qualitative comments  
were imported into NVivo for thematic analysis. Combining both data types supported triangulation between  
numerical validity measures and interpretive feedback, thereby strengthening the study’s reliability.  
Data Analysis  
The analysis involved both quantitative and qualitative components. For quantitative evaluation, the Item-Level  
CVI (I-CVI) was calculated as the proportion of experts assigning a rating of 4 or 5 to each item. The Scale-  
Level CVI (S-CVI/Ave) was obtained by averaging all I-CVI values, while the S-CVI/UA measured the  
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proportion of items that achieved universal agreement. Thresholds of 0.78 for I-CVI and 0.90 for S-CVI/Ave  
were used as indicators of acceptable content validity (Polit & Beck, 2006).  
The CVR was calculated using Lawshe (1975) formula, with 0.78 as the minimum acceptable value for a panel  
of six experts. Qualitative data were analysed thematically, focusing on three aspects: conceptual clarity,  
pedagogical relevance, and practicality. Expert suggestions were reviewed to identify areas of improvement and  
to refine the model for instructional implementation. The integration of statistical and thematic evidence allowed  
for a balanced interpretation of the model’s strengths and limitations.  
RESULTS  
Expert Profiles  
Six experts representing the domains of design-thinking pedagogy, learning innovation, IoT engineering, and  
computing education participated in the review. Each expert independently evaluated fourteen activities that  
composed the model. Quantitative ratings and qualitative reflections were gathered to examine both the internal  
consistency and the contextual practicality of the framework. The procedure followed the principles of design-  
based research, in which knowledge is refined through iterative cycles of analysis and reflection (McKenney &  
Reeves, 2018). Two-thirds of the experts have the highest academic qualification in their field. This suggests a  
high level of expertise, research experience, and academic achievement among the group. The expert group  
comprises two-thirds academicians and one-third consultants. The majority of the experts come from academic  
institutions (e.g., universities, research centers), while the rest work in a consulting or professional services role.  
This is important since in decision-making, project validation, or research, this kind of expert mix shows a  
balance between theory and practice, but with a stronger academic influence. Most of the insights, opinions, or  
contributions may be shaped by research, theory, and teaching experience. Academicians typically bring deep  
subject knowledge, long-term research focus, and an understanding of emerging trends. Practical industry input  
is present means there's still a strong representation of practical, real-world, or industry-focused experience. The  
team has a balanced expertise from DT also from IoT with an average working experience of 10 years. The panel  
of experts, each with an average of 10 years of professional experience, provided well-informed insights based  
on both theoretical understanding and practical application. They have a high level of experience, suggesting  
that they have had enough time to develop deep expertise, handle complex problems, and gain a solid  
understanding of their industry or field. They can provide the reliability and maturity in judgment. Their  
opinions, decisions, or contributions are likely to be informed by practical experience, not just theory. With 10  
years of experience, these experts are likely in a phase where they combine updated knowledge with practical  
insight, making their input especially valuable. The profiles of the experts are as described in Table 2.  
Table 2: Experts Profile  
Id  
Highest Qualification  
Doctor of Philosophy  
Position  
Field Of Expertise  
Working Experience (Years)  
15  
Expert 1  
Academician DT  
Academician DT  
Expert 2 Doctor of Philosophy  
Expert 3 Doctor of Philosophy  
Expert 4 Bachelor Degree  
Expert 5 Bachelor Degree  
Expert 6 Doctor of Philosophy  
10  
7
Consultant  
Consultant  
DT  
IoT  
14  
8
Academician IoT  
Academician IoT  
8
Quantitative Validation Results  
All fourteen items achieved strong ratings from the expert panel. The Scale-Level Content Validity Index (S-  
CVI/Ave) was 0.96, exceeding the accepted benchmark of 0.90 for educational model validation. The Scale-  
Level Universal Agreement (S-CVI/UA) reached 0.83, indicating substantial agreement among the experts.  
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Individual Item-Level CVI (I-CVI) values ranged from 0.83 to 1.00, confirming that every item met or surpassed  
the 0.78 threshold for acceptable content validity (Polit & Beck, 2006).  
The Ideation and Implementation phases received the highest consistency scores, reflecting experts’ confidence  
in their instructional clarity and feasibility. Slightly lower but still satisfactory ratings were observed in the  
Inspiration phase, particularly for empathy and contextual exploration activities. These variations were  
considered a sign of critical engagement rather than disagreement, showing that experts evaluated the activities  
thoughtfully within their own disciplinary perspectives. Table 3 shows the overall quantitative results.  
Table 3: The Overall Quantitative Results  
Index  
Value  
Interpretation With Six Experts  
Full scale assessed  
Number of items 14  
S-CVI/Ave  
S-CVI/UA  
0.96  
0.83  
Excellent overall content validity  
Good universal agreement; a few items invite revision  
I-CVI (range)  
≥ 0.83 (reported across items) All items above common 0.78 cut-off for n=6  
The statistical results verify that the structural components and descriptions within the framework were clearly  
defined and pedagogically coherent. The slight dispersion of scores across phases indicates productive  
professional judgment rather than error, showing that experts considered the contextual nuances of each activity.  
Overall, the quantitative findings confirm that the model’s structure is internally consistent and conceptually  
robust.  
Qualitative experts Results  
While the numerical indices established statistical validity, the accompanying qualitative feedback revealed the  
specific pedagogical dimensions requiring refinement. Experts’ comments are presented verbatim and grouped  
according to the relevant DT phase.  
Inspiration Phase  
Experts unanimously agreed on the value of beginning the process with empathy-based problem discovery but  
encouraged richer contextual examples to support learners’ understanding.  
“Expose students to more real-life context. Add more examples of projects that have clear problems and the  
project’s application.” (Expert 5)  
“More examples to grasp the programming concept and the application of IoT.” (Expert 3)  
These remarks emphasise the importance of authentic contexts as catalysts for engagement and conceptual  
transfer. Consequently, two concise case exemplars one technical and one social were proposed to precede  
empathy-mapping activities.  
Ideation Phase  
Several reviewers highlighted time allocation and tool flexibility as areas for improvement.  
“Add more time. Can use mind map as a tool.” (Expert 3)  
“If possible, not to fix with flash cards or storyboard only for prototype.” (Expert 3)  
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Such comments underscored the need for varied creative tools to accommodate different cognitive styles.  
Extending the ideation window and introducing alternative brainstorming methods were thus recommended.  
Panel members discussed the inclusivity of idea selection and the importance of specifying target users.  
“The selection could be done once all members presented their ideas. Thus, everybody could have the  
opportunity to share the projects.” (panel remark)  
“Be clear who is actually the end-user?” (Expert 1)  
“Student may present about the motivation for having the solution, marketability of the solution, idea validation  
etc.” (Expert 5)  
These comments reveal the necessity of ensuring democratic participation and explicit user orientation within  
team projects. A structured idea-pitch session with a common evaluation rubric was suggested to address these  
concerns.  
Implementation Phase  
Feedback for the final phase focused on ensuring hands-on data practice and full exposure to IoT workflows.  
“The student could be given an opportunity to deploy the sensors, connect to the cloud, collect the data for few  
days and analyse it.” (Expert 5)  
This observation led to the inclusion of a short deployment and analysis sprint, allowing students to experience  
data collection, interpretation, and ethical considerations in a real operational setting.  
Based on the experts' feedback, we developed the themes and suggested refinement as shown in Table 4.  
Table 4: Expert Feedback Themes and Resulting Refinements  
Theme  
Representative Expert Quote  
Refinement Implemented In Model  
Real-world  
anchoring  
“Expose students to more real-life context. Incorporate  
two domain-specific  
Add more examples …” (Expert 5)  
exemplars and a one-page context brief  
before empathy tasks.  
Conceptual  
scaffolds  
ideation  
“Add more time. Can use mind map as a Extend ideation period and allow multiple  
for tool.” (Expert 3) creative-thinking tools.  
Prototype  
flexibility  
“If possible, not to fix with flash cards …” Permit paper, digital, or hybrid low-  
(Expert 3) fidelity prototypes prior to hardware build.  
Inclusive  
selection  
idea “The selection could be done once all Require every member to pitch ideas;  
members presented …” (Expert 2)  
apply rubric assessing novelty, feasibility,  
and user value.  
User clarity and “Be clear who is actually the end-user?” Add user–problem–value template and  
value proposition (Expert 1); “motivation … marketability desirability-feasibility-viability  
…” (Expert 5) checkpoint.  
Full data lifecycle “Deploy the sensors … collect the data …” Introduce a three-day data-collection  
experience  
(Expert 5)  
sprint with ethics reflection and basic  
analytics.  
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The qualitative dataset provided detailed insights into the pedagogical realism of the model. Each quotation  
represented a distinct category of improvement, collectively ensuring that the revised model remains grounded  
in authentic learning experiences and adaptable to classroom realities.  
DISCUSSION  
The combination of quantitative and qualitative findings demonstrates that the initial proposed model achieved  
high reliability and relevance. Statistical indices confirmed strong agreement among experts regarding content  
validity, while qualitative remarks pinpointed specific instructional enhancements. The collective results verify  
that the model’s theoretical foundations are supported by expert consensus and that its pedagogical features are  
clearly defined and applicable. These results form the empirical basis for the interpretive discussion presented  
in the following section.  
Interpreting the Validation Results  
The validation results confirmed that the Design Thinking–IoT framework developed in this study demonstrates  
strong content validity and pedagogical coherence. Quantitative indices, including the high Scale-Level Content  
Validity Index (S-CVI/Ave = 0.96) and strong Content Validity Ratio (CVR = 0.89), indicate that the  
framework’s activities are clearly defined, relevant, and feasible for classroom use. These results are consistent  
with validation benchmarks reported in educational design research (Elangovan & Sundaravel, 2021; Polit &  
Beck, 2006). The experts’ qualitative feedback further reinforced these findings by highlighting the framework’s  
authenticity, practicality, and adaptability to real-world learning contexts.  
The integration of expert critique into the refinement process enhanced the model’s pedagogical integrity.  
Suggestions such as incorporating contextual case examples, extending ideation time, and including a short IoT  
deployment exercise improved the framework’s capacity to balance conceptual understanding with hands-on  
experimentation. This alignment between quantitative precision and qualitative insight exemplifies the strength  
of design-based research, which emphasises iterative validation through evidence and reflection (McKenney &  
Reeves, 2018).  
Furthermore, the high level of expert agreement across both pedagogical and technical domains suggests that  
the framework succeeds in bridging cognitive and applied dimensions of learning. Similar findings have been  
reported by Henriksen et al. (2021), who observed that DT structures enhance integration between creativity,  
collaboration, and technical problem solving. In this study, the experts’ endorsement indicates that the model’s  
phases of Inspiration, Ideation, and Implementation effectively translate theoretical constructs into practical  
learning experiences. The framework’s blend of flexibility and structure also resonates with findings from  
blended-learning research showing that adaptable learning environments support deeper engagement and  
reflective learning (Boelens et al., 2017; Graham, 2006).  
Overall, the validation outcomes support the model’s readiness for implementation and empirical testing in IoT-  
based classrooms. The consistency of results across both statistical and interpretive domains provides strong  
evidence that the framework is both pedagogically grounded and contextually relevant.  
Constructivism and Social Mediation  
The framework’s effectiveness can be understood through the lens of constructivist learning theory, which posits  
that knowledge is actively constructed through interaction, collaboration, and contextual problem solving (Piaget  
& Duckworth, 1970; Vygotsky, 1978). The activities embedded in the conceptual model, particularly during the  
Inspiration and Ideation phases, encourage learners to build understanding collectively rather than absorb  
information passively. These processes exemplify social constructivism, where meaning emerges through  
dialogue, negotiation, and shared experience (Reigeluth, 2013).  
The experts’ recommendation to introduce richer contextual examples aligns closely with constructivist  
principles. Providing real-world cases encourages learners to link new information with prior experience,  
promoting cognitive scaffolding and meaningful knowledge construction (Bruner, 1997). The inclusion of  
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group-based ideation and prototype evaluation also supports Vygotsky’s concept of the zone of proximal  
development, where learners progress through guided participation and peer interaction.  
By integrating collaborative inquiry within IoT project tasks, the model transforms technical skill development  
into a socially mediated learning process. Learners not only acquire coding or engineering competence but also  
develop communicative and metacognitive skills through group reflection. This dynamic aligns with current  
educational discourse emphasising collaborative intelligence as a key 21st-century competency (OECD, 2023).  
The constructivist grounding of the model thus reinforces its relevance for developing both disciplinary expertise  
and interpersonal competence.  
Experiential Learning and Iterative Reflection  
Experiential learning provides another key interpretive lens for understanding the validated framework. Kolb  
(2015) conceptualised learning as a continuous cycle of concrete experience, reflective observation, abstract  
conceptualisation, and active experimentation. Each component of the Design Thinking–IoT model corresponds  
to one of these stages. The Inspiration phase generates experience through problem exploration; Ideation  
stimulates abstract conceptualisation; Implementation encourages experimentation and testing; and the After  
phase within the IBIA sequence facilitates reflection and synthesis.  
Experts’ suggestions to allow students to deploy IoT sensors and analyse real data directly support this  
experiential cycle. Such activities promote the translation of conceptual learning into embodied understanding,  
reinforcing the iterative relationship between doing and thinking. As Wilson and Beard (2013) noted, learning  
through experience gains meaning when reflection transforms action into insight. The newly integrated short  
deployment exercise addresses this need by enabling learners to connect the sensory, cognitive, and ethical  
dimensions of IoT applications.  
The iterative structure of the framework also aligns with design-based learning approaches, where  
experimentation and revision are central to knowledge formation (Razzouk & Shute, 2012). By guiding learners  
through repeated cycles of testing and feedback, the model cultivates adaptability and resilience which are  
qualities identified by Di Battista et al. (2023) as critical for future-ready graduates. This dynamic process  
converts the classroom into a laboratory of reflection, where students refine both their products and their  
thinking.  
Furthermore, the IBIA teaching sequence operationalises experiential learning within the blended environment.  
The Before stage supports preparation and concept familiarisation, the In stage anchors experiential engagement,  
and the After stage facilitates reflective documentation. Together, these stages embody Kolb’s assertion that  
experience becomes learning only when acted upon reflectively. Through this alignment, the framework  
integrates active experimentation with reflective observation, producing a balanced cycle of action and thought.  
Human-Centred Design, Ethics, and Societal Responsibility  
Human-centred design (HCD) serves as the ethical foundation of the validated framework, ensuring that  
technological innovation remains aligned with human and societal needs. Brown and Wyatt (2015) argued that  
design processes grounded in empathy and responsibility foster innovations that are both meaningful and  
sustainable. The experts’ call for clearer user definition and emphasis on social relevance reflect this principle.  
By embedding empathy mapping and value identification in the early stages of the framework, learners are  
encouraged to view technology not merely as a tool but as a medium for improving human experience.  
The addition of user-problem-value templates and the inclusion of ethical reflection tasks in the Implementation  
stage advance this human-centred orientation. These refinements encourage students to consider usability,  
inclusivity, and the social implications of IoT solutions. Ethical engagement of this kind is increasingly  
recognised as a critical element of engineering and computing education Müller (2020). Integrating such  
practices within a project-based framework prepares learners to approach innovation with both technical  
expertise and moral sensitivity.  
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From a broader perspective, the framework aligns with global education goals that emphasise digital  
responsibility and sustainability. Antoninis et al. (2023) in Global Edicatio Report and the OECD (2023) both  
advocate educational models that equip students with the capacity to innovate ethically in technology-rich  
environments. By situating empathy and reflection as core components, this model supports those aspirations  
and contributes to the discourse on responsible innovation in higher education.  
The validation findings also reveal that experts perceive the framework’s ethical dimension as a strength rather  
than an adjunct. This indicates a growing recognition that design education must integrate societal responsibility  
into the learning process, not treat it as an external consideration. The Design Thinking–IoT framework thus  
contributes to a pedagogical shift from outcome-oriented project work to reflective, value-driven innovation.  
Such integration of ethical literacy with technological fluency represents a necessary evolution for preparing  
learners to navigate the complexities of digital transformation in society  
CONCLUSION  
This study set out to validate a pedagogical framework that integrates DT, the Initiator-Before-In-After (IBIA)  
teaching sequence, and the Flex blended-learning architecture to guide Internet-of-Things (IoT) project-based  
learning for the digital society. Through a design-based validation process, six experts evaluated the model’s  
clarity, relevance, and feasibility using both quantitative indices and qualitative commentary. The initial  
validation confirmed strong content validity, with S-CVI/Ave = 0.96 and S-CVI/UA = 0.83, and produced  
constructive refinements addressing contextual authenticity, creative flexibility, user definition, and data-ethics  
reflection.  
The study demonstrates that pedagogical innovation must be tested not only for theoretical coherence but also  
for usability in authentic learning environments. The expert-based evaluation strengthened the model’s practical  
components, transforming it from a conceptual design into an empirically grounded framework capable of  
linking creativity, reflection, and ethical responsibility. The results affirm that a Design Thinking–IoT  
framework grounded in experiential and constructivist principles can cultivate the cognitive, technical, and moral  
capacities required of learners in the digital society.  
Theoretical and Methodological Contributions  
Theoretically, this study advances understanding across three domains of educational research.  
First, it deepens the constructivist perspective on technology-enhanced learning by showing that authentic social  
contexts and collaborative decision-making strengthen conceptual understanding. The proposed model positions  
IoT learning within human-centred problem solving, transforming knowledge construction into a socially  
mediated process.  
Second, it extends experiential learning theory by embedding iterative reflection into each learning cycle through  
the IBIA teaching sequence. Reflection before, during, and after practice promotes conceptual consolidation and  
aligns classroom activity with Kolb’s experiential model of learning.  
Third, the study contributes methodologically to design-based research by demonstrating that expert validation  
can serve as a formative mechanism for refining theory. Rather than treating content validity as a final outcome,  
the validation process functioned as an iterative dialogue between theoretical abstraction and pedagogical  
practice. This approach reinforces the link between research and implementation, which is central to educational  
design research (Anderson & Shattuck, 2012; McKenney & Reeves, 2018).  
Pedagogical Implications  
The validated framework offers educators a replicable structure for integrating innovation, ethics, and reflection  
into technical curricula. By combining Design Thinking and IBIA within a blended-learning environment, the  
model allows instructors to orchestrate learning that is both exploratory and structured. It encourages students  
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to take ownership of their learning, to collaborate meaningfully, and to develop empathy for user. These are all  
essential competencies for digital-society citizenship.  
Instructors adopting this framework should emphasise authentic problem briefs that connect IoT technology to  
community or industry challenges. Assessment should be aligned with process and reflection rather than solely  
product outcomes. Evidence such as context briefs, idea-pitch rubrics, and data-ethics reflections can serve as  
authentic indicators of competence. These adjustments encourage learning environments that mirror real-world  
innovation processes and promote sustained engagement.  
Furthermore, the Flex component ensures accessibility and continuity across physical and digital spaces. It  
allows educators to balance asynchronous conceptual learning with synchronous collaborative prototyping, a  
blend shown to enhance self-regulated learning and persistence. Adopting such structures can support institutions  
seeking to redesign courses for hybrid or transnational delivery without compromising pedagogical quality.  
Limitations and Future Directions  
The current study represents an initial design–validation phase, focusing on the conceptual development and  
expert validation of the proposed model rather than direct classroom implementation. While the findings provide  
evidence of content relevance, theoretical alignment, and structural coherence, further research is required to  
transition the validated model into authentic educational contexts. Several future research directions are proposed  
to support this progression. The validated model should be implemented in small-scale pilot classroom studies  
to examine its practical feasibility and usability. This phase would allow researchers and practitioners to observe  
how the model functions in real classroom settings, including teachers’ instructional practices, students’  
engagement, and contextual constraints such as time, resources, and curriculum alignment. Feedback gathered  
from teachers and students can be used to refine instructional procedures and implementation guidelines. Despite  
its strengths, the study’s scope was limited to a small panel of six experts within the Malaysian higher-education  
context. While this number met established criteria for content-validity research, future studies should include a  
broader and more international panel to explore cultural and disciplinary variations in interpretation. Finally, the  
model’s adaptability should be tested across domains such as robotics, health-technology innovation, and  
sustainable-energy systems to determine its generalisability. Comparative studies may also explore how Design  
Thinking-based pedagogies interact with AI-supported learning environments to enhance digital-society  
readiness.  
Concluding Reflection  
The initial validation of the conceptual learning model demonstrates that education for the digital society must  
transcend technical instruction. It must integrate empathy, ethics, and reflective practice as foundational  
competencies. The high level of expert consensus achieved in this study confirms that creativity and  
responsibility can coexist within the same pedagogical architecture. The model’s blended, reflective, and human-  
centred structure embodies the educational paradigm needed for societies increasingly defined by intelligent  
technologies.  
Ultimately, this research shows that the future of digital education lies in cultivating reflective innovators and  
individuals who understand not only how to design technologies but also why and for whom they design them.  
The validated model provides a pathway for higher-education institutions to realise this vision, ensuring that  
progress in technological learning remains inseparable from human values, social inclusion, and ethical  
accountability.  
ACKNOWLEDGMENT  
This research is supported by the Ministry of Education Malaysia under the Fundamental Research Grant  
Scheme (Ref: FRGS/1/2021/ICT08/UTM/02/1).  
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REFERENCES  
1. Akiyama, Y., & Cunningham, D. J. (2018). Synthesizing the Practice of SCMC-based Telecollaboration  
2. A Scoping Review. CALICO Journal, 35(1), 49-76. https://www.jstor.org/stable/90016521  
3. Anderson, T., & Shattuck, J. (2012). Design-based research: A decade of progress in education research?  
Educational researcher, 41(1), 16-25.  
4. Antoninis, M., Alcott, B., Al Hadheri, S., April, D., Fouad Barakat, B., Barrios Rivera, M., Baskakova,  
Y., Barry, M., Bekkouche, Y., & Caro Vasquez, D. (2023). Global Education Monitoring Report 2023:  
Technology in education: A tool on whose terms?  
5. Atlam, H., & Wills, G. (2018). Technical aspects of blockchain and IoT. In (pp. 1-39).  
https://doi.org/10.1016/bs.adcom.2018.10.006  
6. Baran, E., & AlZoubi, D. (2024). Design thinking in teacher education: Morphing preservice teachers’  
mindsets and conceptualizations. Journal of Research on Technology in Education, 56(5), 496-514.  
7. Boelens, R., De Wever, B., & Voet, M. (2017). Four key challenges to the design of blended learning:  
A systematic  
literature  
review.  
Educational  
Research  
Review,  
22,  
1-18.  
https://doi.org/https://doi.org/10.1016/j.edurev.2017.06.001  
8. Brown, T., & Wyatt, J. (2015). Design thinking for social innovation. Annual Review of Policy Design,  
3(1), 1-10.  
9. Bruner, J. S. (1997). The culture of education. In The culture of education. Harvard university press.  
10. Choi, H., Kim, H., & Kim, N. (2024). Enhancing creativity through a problem-based design thinking  
project in higher education. Cogent Education, 11. https://doi.org/10.1080/2331186X.2024.2378272  
11. Corpuz, J., Cruz, K. J. S. D., Palomar, J. B., Tamayo, J., Bongao, H. L. C., Enojas, M. J. B., & Morgado,  
J. E. (2025). Household electric monitoring IoT system. Indonesian Journal of Electrical Engineering  
and Computer Science, 40(1), 85-92.  
12. Di Battista, A., Grayling, S., Hasselaar, E., Leopold, T., Li, R., Rayner, M., & Zahidi, S. (2023). Future  
of jobs report 2023. World Economic Forum,  
13. Elangovan, N., & Sundaravel, E. (2021). Method of preparing a document for survey instrument  
validation by experts. MethodsX, 8, 101326. https://doi.org/10.1016/j.mex.2021.101326  
14. Garcia Moreno, H. M. (2024). An Exploratory Case Study of Students’ Blended Learning Experiences  
During Post Emergency Learning.  
15. Graham, C. R. (2006). Blended learning systems. The handbook of blended learning: Global  
perspectives, local designs, 1, 3-21.  
16. Guerra-Manzanares, A., & Bahsi, H. (2023). On the application of active learning for efficient and  
effective  
IoT  
botnet  
detection.  
Future  
Generation  
Computer  
Systems,  
141,  
40-53.  
https://doi.org/https://doi.org/10.1016/j.future.2022.10.024  
17. Henriksen, D., Creely, E., Henderson, M., & Mishra, P. (2021). Creativity and technology in teaching  
and learning: a literature review of the uneasy space of implementation. Educational Technology  
Research and Development, 69(4), 2091-2108.  
18. Horn, M. B., & Staker, H. (2014). Blended: Using disruptive innovation to improve schools. John Wiley  
& Sons.  
19. Iqbal, M. H. (2023). A Review on the Role of IoT in Modern Electrical Engineering Education. Journal  
of Engineering and Computational Intelligence Review, 1(1), 14-22.  
20. Jamaluddin, F., Jamaluddin, A. H., Jamaluddin, F., & Jamaluddin, F. (2025). Malaysia's AI-Driven  
Education Landscape: Policies, Applications, and Comparative Insights for a Digital Future. arXiv  
preprint arXiv:2509.21858.  
21. Jiang, C., & Pang, Y. (2023). Enhancing design thinking in engineering students with project‐based  
learning. Computer Applications in Engineering Education, 31(4), 814-830.  
22. Juan, W., Mukhtar, M. I., & Zhongli, J. (2025). Enhancing Entrepreneurship Education In TVET: A  
Validated Evaluation Scale for Higher Vocational Institution. International Journal of Research and  
Innovation in Social Science, 9(2), 4352-4367.  
23. Kolb, D. (2015). Experiential Learning: Experience as the source of Learning and Development Second  
Edition.  
24. Laverty, M., & Littel, C. (2022). 4.3 Design Thinking. NSCC Foundations of Entrepreneurship.  
25. Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel psychology, 28(4).  
Page 124  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XII December 2025  
26. Liebkemann, S. L. (2021). The Impact of an Interprofessional Web-Based Unfolding Case Study on the  
Knowledge, Skills, and Attitudes of Students via Online, Asynchronous Modules The University of  
North Carolina at Chapel Hill].  
27. Lynn, M. R. (1986). Determination and quantification of content validity. Nursing research, 35(6), 382-  
386.  
28. Malaysia  
Digital  
Education  
Policy  
(2023).  
https://www.moe.gov.my/storage/files/shares/  
Dasar/Dasar%20Pendidikan%20Digital/Digital%20Education%20Policy.pdf  
29. McCormack, C. M. (2021). Information architecture and cognitive user experience in distributed,  
asynchronous learning: a case design of a modularized online systems engineering learning environment  
Purdue University].  
30. McKenney, S., & Reeves, T. (2018). Conducting educational design research. Routledge.  
31. Müller, V. C. (2020). Ethics of artificial intelligence and robotics.  
32. Nordin, N. S., Junaidi, J., & Hanid, M. F. A. (2024). Integrating problem-based learning and design  
thinking: Innovative approaches to enhancing student engagement. Journal of Research, Innovation, and  
Strategies for Education (RISE), 1(1), 41-57.  
33. OECD. (2023). OECD Skills Outlook 2023: Skills for a Resilient Green and Digital Transition. O.  
Publishing.  
34. Panke, S. (2019). Design Thinking in Education: Perspectives, Opportunities and Challenges. Open  
Education Studies, 1, 281-306. https://doi.org/10.1515/edu-2019-0022  
35. Piaget, J., & Duckworth, E. (1970). Genetic epistemology. American Behavioral Scientist, 13(3), 459-  
480.  
36. Polit, D. F., & Beck, C. T. (2006). The content validity index: are you sure you know what's being  
reported? Critique and recommendations. Research in nursing & health, 29(5), 489-497.  
37. Pueyo‐Garrigues, M., Pardavila‐Belio, M. I., Whitehead, D., Esandi, N., Canga‐Armayor, A., Elosua, P.,  
& Canga‐Armayor, N. (2021). Nurses’ knowledge, skills and personal attributes for competent health  
education practice: An instrument development and psychometric validation study. Journal of advanced  
nursing, 77(2), 715-728.  
38. Purba, L., & Zunidar, Z. (2025). Enhancing Student Creativity through Project-Based Learning in  
Science Education. Electronic Journal of Education, Social Economics and Technology, 6, 862.  
https://doi.org/10.33122/ejeset.v6i2.862  
39. Razzouk, R., & Shute, V. (2012). What Is Design Thinking and Why Is It Important? Review of  
Educational Research, 82(3), 330-348. https://doi.org/10.3102/0034654312457429  
40. Reigeluth, C. M. (2013). Instructional-design theories and models: A new paradigm of instructional  
theory, Volume II. Routledge.  
41. Rodriguez‐Sanchez, C., Orellana, R., Fernandez Barbosa, P. R., Borromeo, S., & Vaquero, J. (2024).  
Insights 4.0: Transformative learning in industrial engineering through problem‐based learning and  
project‐based learning. Computer Applications in Engineering Education, 32(4), e22736.  
42. Sukumaran, S. (2018). Flex-learning-Online or face to face–Learners’ freedom of choice. Global  
Bioethics Enquiry, 28.  
43. Trishaank, K., Praveen Kumar, K., & Ram Mohan Rao, P. (2024). Integrating Computational Thinking  
& Design Thinking in Curriculum Development. International Conference on Work Integrated Learning.  
44. UNESCO. (2023). Recommendation on the ethics of artificial intelligence. United Nations Educational,  
Scientific and Cultural Organization.  
45. Vuorikari, R., Kluzer, S., & Punie, Y. (2022). DigComp 2.2: The Digital Competence Framework for  
Citizens-With new examples of knowledge, skills and attitudes.  
46. Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes (Vol. 86).  
Harvard university press.  
47. Wilson, J. P., & Beard, C. (2013). Experiential learning: A handbook for education, training and  
coaching. Kogan Page Publishers.  
48. Wrigley, C., & Straker, K. (2015). Design Thinking pedagogy: the Educational Design Ladder.  
Innovations  
in  
Education  
and  
Teaching  
International,  
54,  
1-12.  
https://doi.org/10.1080/14703297.2015.1108214  
Page 125  
www.rsisinternational.org  
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XII December 2025  
49. Zainal, S., Yusoff, R. C. M., Abas, H., Yaacub, S., & Zainuddin, N. M. (2021). Review of design thinking  
approach in learning IoT programming. International Journal of Advanced Research in Future Ready  
Learning and Education, 24(1), 28-38.  
APPENDIX  
Appendix A. The Activities In A Design Thinking Learning Model For Developing IoT Projects  
Phase 1: Inspiration  
Lecturer’s Task  
Students’ Task  
Rating  
Rating  
Foundation  
A1  
Introduce the fundamentals of design Explore the fundamentals of IoT programming on  
thinking and IoT programming the internet  
Empathise  
A2  
Discuss with students the purpose of Actively listen to the lecturer’s explanation of the  
the assignment/task.. task’s purpose.  
A3  
Expose students to real-life scenarios Actively observe the real-life situation  
(Example: Temperature sensing and  
humidity).  
A4  
A5  
Divide students into groups to work Discuss and assign roles to ensure balanced  
together as a “design team” (4-5 participation. Each group will assign specific roles,  
students in 1 group).  
with one student acting as the designer, one as the  
timekeeper, and the others taking on the role of users.  
Evaluates students for item A4 using Demonstrate their ability to observe, listen, analyze,  
the evaluation DT rubric, which and reflect on human experiences authentically  
focuses  
on  
how  
well  
students  
understand users’ needs, feelings, and  
contexts.  
Define  
Rating  
A6  
Conducts  
regarding  
brainstorming  
steps, hardware  
session Focus on generating, organizing, evaluating, and  
and refining ideas — while applying knowledge of  
software, and materials to develop IoT technology, design, and user needs.  
project to solve the problems.  
A7  
A8  
Observes and facilitates the session as Analyse the identified problems - fill up the Define  
students carry out their task.  
Problem  
template, Cause and Effect Diagram  
(Ishikawa diagram) or other methods.  
Evaluates students for item A7 using Demonstrate their ability to Analyse the identified  
the evaluation DT rubric regarding on problems.  
how well students understand users’  
problems.  
Phase 2: Ideation  
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ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XII December 2025  
Lecturer’s Task  
Students’ Task  
Rating  
Ideate  
A9  
Conduct group discussions.  
Used tools such as mind map to visually organize  
and expand creative ideas.  
The group critically  
evaluated all proposed solutions and selected the  
most feasible and innovative idea among the three  
alternatives for further development.  
Design  
A10 Observes and facilitates the session as Design a prototype using flash cards/storyboard and  
students carry out their task.  
present their solutions  
A11 Evaluates students using  
the Demonstrate their ability to design solutions.  
evaluation DT rubric regarding how  
viable the students proposed the  
solution  
Test  
A12 Give feedbacks on prototype design.  
Get feedback from the lecturer and peers on  
prototype design.  
Phase 3: Implementation  
Lecturer’s Task  
Students’ Task  
Rating  
Development  
A13 Observes and facilitates the session as Develop an IoT prototype (Example: Temperature  
students carry out their task.  
Sensing and Humidity System)  
A14 Evaluates students using  
the Demonstrate their ability to develop the solutions  
evaluation DT rubric regarding how  
viable the students prototype.  
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