1. Introduction
Engineering education increasingly adopts project-based learning (PBL) to foster practical and interdisciplinary skills essential for real-world practice (Krajcik & Shin, 2014; Halverson & Sheridan, 2014). Reviews have highlighted PBL’s effectiveness in promoting active learning, teamwork, and the application of theory to real problems (Lavado-Anguera et al., 2024; Mota et al., 2025). These qualities are vital for future engineers navigating rapid technological and societal change (Prince & Felder, 2006; Brundiers & Wiek, 2013).
However, many PBL implementations remain confined to classroom simulations, limiting authenticity and student motivation (Hmelo-Silver, 2004). Disciplinary silos—such as separating design, electronics, and programming—can hinder holistic learning (Mills & Treagust, 2003). There is increasing recognition that engineering education must also address social relevance by engaging with real communities and users (Brundiers et al., 2010; OECD, 2018).
In response, new PBL models emphasize community engagement, interdisciplinary collaboration, and real-world impact (Bolick et al., 2024; Kondo et al., 2023). These are particularly suited to Japan’s National Institute of Technology (KOSEN) (National Institute of Technology, 2024), which emphasizes hands-on training and local collaboration (Tsutsumi et al., 2024).
This study examines a community-integrated PBL case at a Japanese KOSEN, where students designed a rideable 5-inch gauge railway system. By integrating mechanical design, motor control, and computer vision, the project provided a platform for deep interdisciplinary learning. The findings offer insight into how small-scale, resource-conscious PBL can support meaningful, transferable learning aligned with community needs.
2. Theoretical Background
2.1. Overview
This study draws on four interconnected theoretical frameworks to analyze the educational impact of the railway system project: experiential learning, situated learning, self-regulated learning (SRL), and constructive alignment. Each framework helps explain how students learn through authentic tasks, collaboration, reflection, and alignment. These models were chosen for both their individual and complementary value in supporting PBL at KOSEN. Figure 1 summarizes their integration and their correspondence to the project environment.
2.2. Design-Based and Experiential Learning Foundations
Kolb’s experiential learning theory (Kolb, 1984) views knowledge as developing through cycles of experience, reflection, abstraction, and experimentation. In this project, students repeated construction, discussion, refinement, and testing, supporting both conceptual and technical development.
The iterative design process also exemplifies a design-based learning (DBL) approach, where students engage in continuous problem-solving around real-world constraints (Gómez Puente et al., 2013; Krajcik & Shin, 2014). In this study, students used OpenCV (version 4.9.0) and Python (version 3.11) to build a control system, thus integrating digital tools with mechanical design. These activities match educational efforts to use digital fabrication like 3D printing in STEM curricula (Stern et al., 2019). Such tools increased student ownership, deepened learning, and linked theory to practice.
Overall, these frameworks guided course design by promoting iterative, applied learning through technology-driven design challenges.
2.3. Situated and Scaffolded Learning
The authentic setting—designing a system for community use—illustrates situated learning, where knowledge is built through social participation (Lave & Wenger, 1991). Presenting at public events and collaborating with stakeholders let students practice engineering in a real community.
Vygotsky (1981)’s Zone of Proximal Development (ZPD) highlights the value of scaffolding and peer collaboration. In this project, students supported each other based on their expertise, creating a collaborative environment that encouraged both individual and group progress. Such alignment with real-world audiences and shared tasks increased students’ motivation, the relevance of their work, and the transferability of their skills.
2.4. Self-Regulated Learning
Self-regulated learning (SRL) involves planning, goal-setting, self-monitoring, and adaptation. Zimmerman (2002) identifies SRL as the foundation of autonomous learning. In a PBL context, SRL enables students to handle complex tasks and take ownership of their learning paths (English & Kitsantas, 2013). In this project, students assumed independent roles, communicated via Microsoft TEAMS, and iteratively improved their subsystems. These behaviors demonstrate metacognitive development and ongoing engagement with learning tasks. The explicit use of rubrics and feedback further supported SRL, increasing clarity and self-efficacy (Panadero et al., 2023; Andrade, 2005). This is consistent with other STEAM-based PBL projects, where SRL has been shown to boost motivation and autonomy (López Carrillo et al., 2024). By structuring the course to foster agency, reflection, and iterative problem-solving, the learning design closely followed SRL principles.
2.5. Constructive Alignment
Biggs’s constructive alignment model (Biggs, 1996, 2014) stresses the importance of coherence among learning objectives, teaching methods, and assessment. In this project, targeted learning outcomes—such as integrating mechanical design, electronics, and vision systems—were embedded in course activities and assessments. Student presentations, peer feedback, and system testing offered authentic opportunities to measure these outcomes. The course design avoided isolated tasks, instead offering challenges that required coordinated, interdisciplinary effort. As shown in Table 1, this alignment ensured that learning processes, instructional supports, and evaluation mechanisms all worked together toward the same educational goals.
3. Context and Project Overview
3.1. Educational Setting
This study was conducted in the “Advanced Precision Design Engineering” course at a Japanese National Institute of Technology (KOSEN). The course aimed to foster interdisciplinary design thinking and applied problem-solving skills. A 15-week project-based learning (PBL) format was adopted (see Table 2), combining lecture-based instruction with team-based design and prototyping (Biggs, 1996; Gómez Puente et al., 2013). Each weekly session (90 minutes) included both theoretical instruction and hands-on implementation. The curriculum progressed from basic measurement and CAD modeling to motor control and image processing using Python and OpenCV. This integration reflects the design-based learning model (Krajcik & Shin, 2014) and aligns with constructive alignment theory (Biggs, 2014). The inclusion of public presentations and community outreach further reinforced real-world relevance and experiential learning (Brundiers & Wiek, 2013; Halverson & Sheridan, 2014). Similar STEAM-oriented practices had previously been implemented at the same institution, such as a bamboo helicopter project integrating 3D CAD and 3D printing (Tsutsumi et al., 2024), providing a foundation for this more advanced PBL project.
3.2. Project Objectives
The project tasked students with designing and constructing a rideable 5-inch gauge railway system capable of pulling two passenger coaches and carrying approximately ten individuals. The system was shown at local events and a partner high school, providing an authentic audience for feedback and testing (Brundiers et al., 2010). The design goals included a target speed of 5 km/h (walking pace), a brushless DC motor with at least 3 Nm of torque, continuous operation for up to 10 hours, and modular maintainability. Importantly, the system needed to be safe, reliable, and transportable for public demonstrations. These constraints offered a concrete engineering challenge rooted in real-world application, consistent with previous implementations of community-based PBL (Ríos et al., 2010).
3.3. Technical Overview
The railway system was divided into four key technical subsystems: Mechanical design: A steel chassis and wheel assembly modeled in SolidWorks (2024) CAD software (see Figure 2) Motor control: A brushless DC motor (3 Nm) connected via a 1:13 gear ratio and controlled with an off-the-shelf driver Power unit: A modular lead-acid battery array with swappable units Vision system: A real-time curve detection algorithm implemented in Python using OpenCV (see Figure 3 and Figure 4)
Integrating design, control, and sensing under real constraints offers a holistic approach, seldom seen in past studies (cf. Kondo et al., 2023; Stern et al., 2019). Students emphasized reproducibility and modular design for long-term educational value.
3.4. Collaborative Roles
Three students participated in the course, each assuming a distinct technical role based on individual expertise: Mechanical Lead: Responsible for system architecture and CAD modeling (SolidWorks) Circuit Lead: Managed wiring, motor control integration, and procurement Vision Lead: Developed image processing and system-level integration in Python
This role-based collaboration promoted accountability, distributed expertise, and peer teaching, in line with frameworks for cooperative learning in engineering education (Gómez Puente et al., 2013). Weekly meetings and milestone presentations facilitated feedback and helped synchronize efforts. These interactions illustrate the value of real-world application and community feedback in enhancing socially relevant, situated learning experiences (Brundiers et al., 2010).
4. Methodology
4.1. Research Design
This study employed a mixed-methods research design to examine the learning outcomes of a small-scale, community-integrated PBL course. The primary data sources included a 5-item post-project Likert-scale questionnaire (Cronbach’s alpha = 0.72), open-ended student reflections, instructor observations, and Microsoft TEAMS communication logs. These sources allowed triangulation of evidence from cognitive, technical, and behavioral domains. The questionnaire was adapted from validated PBL instruments (Kokotsaki et al., 2016) to assess student collaboration, technical development, and self-regulated learning. Qualitative data provided contextual insights into student experiences, peer dynamics, and project challenges. Given the small sample size (n = 3), the study adopts an exploratory case study approach aimed at generating detailed, context-rich descriptions rather than statistical generalization. This methodology prioritizes depth over breadth and is suited for capturing the nuanced learning processes and social interactions within a resource-conscious, interdisciplinary engineering project.
4.2. Participants and Course Implementation
The participants were three students enrolled in the Advanced Course in Mechanical and Information Systems Engineering at a Japanese National Institute of Technology (KOSEN). Each student had prior experience with CAD, programming, and electronics from earlier coursework. Informed by research on technology-supported learning design (Ifenthaler & Yau, 2020), the course utilized a hybrid model that balanced flexibility with structured collaboration. Team roles were assigned based on student interests and competencies, fostering ownership and balanced collaboration. This approach is consistent with self-regulated learning principles and cooperative learning models in engineering education.
4.3. Data Collection Instruments
To assess the learning process and outcomes, four main data sources were used: Post-course questionnaire: A 5-item Likert-scale survey, supplemented by two open-ended reflection prompts. Instructor observations: Field notes recorded during prototyping sessions and final presentations. Student artifacts: CAD models, Python code, and subsystem integration designs. Communication logs: Archived records capturing discussions, planning, and problem-solving behavior.
These instruments allowed data collection from multiple perspectives and helped identify patterns in student engagement, collaboration, and technical development. Table 3 provides an overview of the data sources, collection methods, data types, and analytical approaches used in the study.
4.4. Data Analysis
Quantitative data from the questionnaire were analyzed using descriptive statistics to identify trends in student engagement and skill development. For qualitative data, thematic analysis was applied to student reflections and instructor observations, focusing on recurring themes such as technical growth, collaboration, and real-world application. Communication logs from Microsoft TEAMS were examined through discourse analysis to trace patterns of planning, initiative, and self-regulation. This triangulated approach aligns with established PBL evaluation practices (Kokotsaki et al., 2016; Mills & Treagust, 2003). The analytical framework drew on prior studies of design-based and experiential learning (Gómez Puente et al., 2013; Krajcik & Shin, 2014) and was guided by theories of self-regulated learning and collaborative reflection (Zimmerman, 2002; English & Kitsantas, 2013). The use of TEAMS also reflects broader trends in digital support for hybrid PBL environments (Zhong & Lyu, 2022). While the small sample size limits generalization, the case offers rich insights into how community-oriented, technology-enhanced PBL can be implemented in vocational education.
5. Results and Analysis
5.1. Quantitative Survey Results
All three students completed a 5-item Likert-scale questionnaire at the end of the project. Item-by-item analysis indicated consistently high scores for engagement and skill development (Appendix A.2). These quantitative findings were substantiated by qualitative data, such as students’ reflections and communication logs. Scores ranged from 4.3 to 5.0 out of 5.0 on a five-point Likert scale, indicating high engagement and perceived growth. Students gave high ratings for collaboration, technical skill development, and motivation, and felt confident applying their learning to real-world situations. These results were not analyzed for statistical significance but served as entry points for deeper qualitative insights. For example, top-rated items like “I collaborated effectively” and “I developed new technical skills” aligned with student reflections and chat records. One noted, “We divided our roles naturally but helped each other when stuck.” Another wrote, “I had never used a motor driver before, but through trial and error I got it to work.” These comments reflect meaningful learning through peer support, productive struggle, and hands-on problem-solving—hallmarks of effective project-based environments (Panadero et al., 2023).
5.2. Qualitative Reflections
Three high school teachers who attended the final demonstration praised the project’s educational value. One noted, “Students showed strong interest, and it would be beneficial in other STEM classes.” Another remarked, “Interacting with real mechanical systems was exciting and inspiring.” These comments highlight the project’s broader educational impact and support the value of socially embedded learning (Brundiers et al., 2010; Brundiers & Wiek, 2013). Student reflections echoed this, with one stating, “Tuning the vision system was difficult but rewarding. I learned through trial and error.” Overcoming challenges through hands-on experimentation reflects Kolb (1984)’s experiential learning cycle, especially concrete experience and active experimentation. Another commented, “Managing the wiring made me feel like a real engineer.” This comment illustrates situated learning, where authentic tasks in a community help develop identity and make learning more relevant.
Such narratives emphasize authentic, situated learning (Lave & Wenger, 1991) and highlight the motivational effects of engaging with real-world engineering challenges (see Figure 5).
5.3. TEAMS Communication Analysis
Archived communication logs (with selected excerpts presented in Appendix D) provided further evidence of student initiative and planning. For example, messages between students and the instructor demonstrated proactive management of procurement and scheduling:
Student A: “Some parts are missing. I’ll prepare a list.”
Teacher: “Understood. Let me know when it’s ready.”
Such exchanges demonstrate professional-level communication, responsibility, and self-regulation—key components of engineering competence and workplace readiness (Gómez Puente et al., 2013; Zimmerman, 2002).
While not addressing TEAMS specifically, recent learning analytics research emphasizes how digital platforms support asynchronous coordination and engagement (Ifenthaler & Yau, 2020).
5.4. Observational Evidence (Photographs)
Photographic documentation confirmed students’ active participation (see Figure 6). Images captured moments of prototyping, peer-to-peer instruction, and system testing (see Figure 6). These visuals complement the communication logs and survey responses, offering tangible evidence of collaborative, problem-driven engagement.
Key takeaways include: Students actively assembling and testing mechanical components Clear evidence of peer-to-peer instruction and task coordination Final system deployment in an authentic community setting
5.5. Theoretical Alignment
The findings closely align with the theoretical framework outlined in Section 2. Kolb (1984)’s experiential learning was reflected in the iterative design-build-test cycle. Constructive alignment was evident in the coherence between goals, instruction, and assessment (Biggs, 1996). Situated learning emerged through authentic, community-based activities (Lave & Wenger, 1991). Self-regulated learning was seen in students’ ownership of tasks and timeline management (Zimmerman, 2002). The use of digital tools further supported learner agency (Ifenthaler & Yau, 2020). Together, these elements demonstrate that the learning environment effectively supported technical, cognitive, and social development as intended.
6. Discussion
6.1. Educational Implications and Theoretical Contributions
This study demonstrates that integrating project-based learning (PBL) with community engagement fosters both technical skills and broader competencies such as collaboration, communication, and self-regulated learning. These outcomes align with key STEM education goals, including hands-on design, creativity, and problem-solving (Halverson & Sheridan, 2014; Sawyer, 2006). Presenting the railway vehicle to a real audience enhanced authenticity and student motivation (Robinson, 2011; Brundiers & Wiek, 2013). Although the project involved a small team, it produced rich evidence of meaningful learning, consistent with prior findings (Hidayat et al., 2024). However, the unique institutional and cultural context limits its generalizability (Ramírez De Dampierre et al., 2024). Student reflections and observations align closely with the theoretical framework described in Section 2. Their engagement illustrates Kolb (1984)’s experiential learning cycle, Vygotsky’s concept of scaffolding, and the integrative nature of design-based learning (Tsai et al., 2022; Krajcik & Shin, 2014). These findings support the educational value of authentic, interdisciplinary, and socially embedded learning environments. Future implementations may benefit from structured time management tools, scaffolding for heterogeneous learners, and advanced planning for logistics and community collaboration to improve scalability and equity.
6.2. Limitations and Future Directions
Despite its contributions, this study has several limitations. First, the small sample size (n = 3) restricts generalizability. However, as an exploratory case study, it aimed to provide in-depth insights into learning processes and team dynamics. Second, the setting—a Japanese KOSEN—offers a valuable example of hands-on engineering education, but further studies are needed to assess its applicability across other institutional and cultural contexts. Third, data collection relied primarily on self-reports, observations, and communication records. Incorporating performance-based assessments, peer evaluations, or follow-up interviews would yield a more comprehensive picture of learning outcomes. Future research should consider larger samples, longitudinal designs, and the role of educational technologies in supporting self-regulated learning and collaboration. Despite these limitations, the study provides a valuable case of how community-connected, interdisciplinary PBL can foster deep learning in resource-constrained environments.
These challenges underscore the need for careful planning and institutional support when adapting such models to larger student populations.Bias from the author’s dual role was mitigated through data triangulation.
7. Conclusions
This study shows that community-integrated PBL can offer valuable interdisciplinary learning, even with limited resources and a small student team. Students developed technical, collaborative, and self-regulation skills while engaging with real-world challenges and community needs. Despite the limited scale, the results suggest that small-scale PBL can foster deep learning and student motivation when linked to authentic contexts.
Ethical review and approval were waived for this study due to the use of only anonymous, non-invasive questionnaires, in accordance with the “Ethical Guidelines for Life Science and Medical Research Involving Human Subjects” (MHLW, Japan; revised 27 March 2023) and after consultation with the institution’s ethics officer.
Informed consent was obtained from all subjects involved in the study.
The data supporting the findings of this study are available within the article. Additional data are available from the author upon reasonable request.
The author thanks the students and community partners for their participation. During the preparation of this manuscript, the author used ChatGPT (OpenAI, GPT-4, 2024 version) to refine grammar and clarity. The author has reviewed and edited the output and takes full responsibility for the content of this publication.
The author declares no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
| PBL | Project-Based Learning |
| SRL | Self-Regulated Learning |
| CAD | Computer-Aided Design |
| KOSEN | National Institute of Technology (Japan) |
Footnotes
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Figure 1 Theoretical framework for PBL implementation. This diagram integrates
Figure 2 CAD Model and Prototype of the Rideable 5-inch Gauge Railway Vehicle.
Figure 3 Curve Detection Result Using OpenCV. Results of curve detection using OpenCV. Thresholding and contour tracking were used to identify rail boundaries in real time.
Figure 4 Flow chart of Curve Detection Algorithm. The flowchart illustrates the sequence of operations in the curve detection system, including image capture, grayscale conversion, HSV thresholding, and decision-making based on centroid movement.
Figure 5 Railway Vehicle Demonstration at High School Event. Students assembling and testing the railway system, illustrating collaborative, hands-on learning.
Figure 6 Students Assembling the Chassis and Drive System. This photograph shows students actively participating in hands-on prototyping and testing of the railway vehicle. It highlights the collaborative, iterative, and problem-solving aspects of the learning process inherent in project-based engineering education.
Mapping of Learning Theories to Project-Based Activities.
| Learning Theory | Core Concept | Manifestation in Project |
|---|---|---|
| Experiential Learning | Learning through concrete-reflective cycles | Design, test, reflect, iterate cycle with hands-on building |
| Situated Learning | Authentic context and community participation | Public exhibition, designing for real high school use |
| Self-Regulated Learning | Goal-setting, monitoring, self-reflection | Independent task planning, timeline control, peer feedback |
| Constructive Alignment | Coherence between objectives, teaching, assessment | Integration of mechanics, coding, and vision system; peer review |
| Zone of Proximal Development (ZPD) | Learning through expert scaffolding | Peer mentoring, task-sharing based on expertise gaps |
| Design-Based Learning (DBL) | Iterative design for real-world problems | Real-time control system using OpenCV and motor integration |
Taken together, these theories offer a robust framework for understanding how students learn by doing, collaborating, and reflecting within an aligned, authentic, and technology-rich environment.
15-Week Curriculum Schedule.
| Week | Lecture Topics | Project Activities |
|---|---|---|
| 1 | Precision Measurement Introduction | Project Kick-off and Team Formation |
| 2 | Length and Angle Measurement | Initial Concept Sketches |
| 3 | Error and Tolerance | Subsystem Planning |
| 4 | Sensor Calibration | Frame Design (SolidWorks) |
| 5 | Signal Processing Basics | Drive System Design |
| 6 | Data Logging Methods | Circuit Layout |
| 7 | Analog-to-Digital Conversion | Motor Selection and Control Setup |
| 8 | Feedback and Control Theory | Simulation and Assembly Planning |
| 9 | OpenCV Basics | Camera Setup |
| 10 | Image Filtering and Edge Detection | Curve Detection Algorithm Design |
| 11 | Pattern Recognition | Python and Arduino Integration |
| 12 | Robustness Testing | Subsystem Integration |
| 13 | User Testing Preparation | Pilot Operation and Feedback Collection |
| 14 | System Improvement | Final Debugging |
| 15 | Presentation Preparation | Final Presentation and Peer Review |
Overview of Data Collection Instruments and Analysis Methods.
| Data Source | Collection Method | Type | Analysis Method |
|---|---|---|---|
| Questionnaire | End-of-course form | Quantitative | Descriptive statistics |
| Open-ended responses | Written by students | Qualitative | Thematic analysis |
| Observations | Instructor notes | Qualitative | Analytical memoing |
| TEAMS chat logs | Archived communication | Qualitative | Discourse analysis |
Appendix A. Student Questionnaire
Appendix A.1. Questionnaire Items (English Translation)
Q1. I recognized the importance of my work by knowing it would be used at a high school.
(実際に高校で使われることで、自分の作業の重要性を実感できた)
Q2. I felt that my understanding deepened through prototyping and learning from failures.
(試作や失敗を通して、理解が深まったと感じた)
Q3. I was able to understand knowledge outside my area of expertise through role-sharing within the team.
(チーム内の役割分担により、自分の専門以外の知識も理解できた)
Q4. I felt that exchanging opinions with team members led to better design outcomes.
(チームメンバーとの意見交換によって、より良い設計ができたと感じる)
Q5. The purpose of creating something to be used in the community helped me stay motivated.
(地域で使われるものを作るという目的が、やる気の維持につながった)
Response format: 5-point Likert scale (1 = Strongly Disagree, 5 = Strongly Agree)
Appendix A.2. Detailed Questionnaire Results
Mean, Standard Deviation, and Mode for Each Question.
| Question | Mean | Standard Deviation | Mode |
|---|---|---|---|
| Q1 | 4.67 | 0.58 | 5 |
| Q2 | 5.00 | 0.00 | 5 |
| Q3 | 4.33 | 0.58 | 4 |
| Q4 | 5.00 | 0.00 | 5 |
| Q5 | 4.67 | 0.58 | 5 |
Individual Student Scores (Anonymized).
| Student | Q1 | Q2 | Q3 | Q4 | Q5 |
|---|---|---|---|---|---|
| Student A | 5 | 5 | 4 | 5 | 5 |
| Student B | 4 | 5 | 4 | 5 | 4 |
| Student C | 5 | 5 | 5 | 5 | 5 |
Full Student Comments (Original and English Translation).
| Student | Comment |
|---|---|
| A | (JA): カメラの調整で試行錯誤して学ぶことが多かった。難しかったが達成感があった。 |
| B | (JA): バッテリーと配線の管理がリアルなエンジニアリング体験になった。 |
| C | (JA): 部品が遅れてスケジュールを立て直したのは現実的だった。 |
Original responses in Japanese are followed by English translations.
Appendix B. Communication on Production Logistics
Student–Instructor Communication on Production Logistics.
| Date | Speaker | Message (English Translation) |
|---|---|---|
| Nov 23 | Teacher | I will place the order after contacting the school, so please wait a little. |
| Nov 27 | Student A | The sprocket has arrived, but the other parts are still pending. |
| Dec 17 | Teacher | Have the aluminum materials arrived yet? I passed the drawings to the workshop to prepare the data. |
| Jan 8 | Student B | The parts have arrived. We will start assembly after class on the 25th, as planned. |
| Jan 15 | Teacher | I’ll order the spring washers, just in case. |
| Jan 15 | Student A | Please wait before placing the order. Some parts are missing. I’ll prepare a list. |
| Jan 15 | Teacher | Understood. Let me know when the missing items are confirmed. |
| Jan 15 | Student A | I’ve created a list of additional items. Please proceed with the order. |
Appendix C. Source Code for Line Detection
def process_image(frame, split_h, split_w, curve_threshold, sharp_threshold):
# Convert to HSV and apply mask to extract rail-like regions
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv, np.array([100, 0, 0]), np.array([255, 255, 255]))
# Define region of interest (top portion) and center split
height, width = frame.shape[:2]
top_h = int(height * (100 - split_h) / 100)
center = int(width * split_w / 100)
top_mask = mask[:top_h, :]
# Count white pixels on left and right sides
left = np.sum(top_mask[:, :center] == 255)
right = np.sum(top_mask[:, center:] == 255)
diff = left – right
# Classify curve based on pixel difference
if abs(diff) < curve_threshold:
result = "Straight"
elif diff > 0:
result = "Sharp Left" if diff > sharp_threshold else "Gentle Left"
else:
result = "Sharp Right" if -diff > sharp_threshold else "Gentle Right"
# Draw guide lines
cv2.line(frame, (0, top_h), (width, top_h), (0, 255, 0), 2)
cv2.line(frame, (center, 0), (center, top_h), (0, 0, 255), 2)
# Show result
cv2.putText(frame, result, (10, height - 20),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2)
return frame, mask, result, diff
Appendix D. Request for Participation and Informed Consent
This questionnaire is being conducted for the purpose of educational improvement and academic research publication.
All responses will be handled anonymously and will not be used to identify any individuals. The results may be statistically analyzed and published in research articles, but no personal information will ever be disclosed.
Your participation is voluntary, and you may stop responding at any time without any penalty.
If you have any questions, please contact the person in charge.
If you have read and understood the above, and you agree to participate, please check “I agree” below.
Andrade, H. G. Teaching with rubrics: The good, the bad, and the ugly. College Teaching; 2005; 53,
Biggs, J. Enhancing teaching through constructive alignment. Higher Education; 1996; 32,
Biggs, J. Constructive alignment in university teaching. HERDSA Review of Higher Education; 2014; 1, pp. 5-22. Available online: https://www.herdsa.org.au/herdsa-review-higher-education-vol-1/5-22 (accessed on 4 April 2025).
Bolick, M. A.; Thomassen, M.; Apland, J.; Spencer, O.; Nicole, F.; Tran, S. K. N.; Voigt, M.; Lazar, K. B. Project-based learning in interdisciplinary spaces: A case study in Norway and the United States. Education Sciences; 2024; 14,
Brundiers, K.; Wiek, A. Do we teach what we preach? An international comparison of problem- and project-based learning courses in sustainability. Sustainability; 2013; 5,
Brundiers, K.; Wiek, A.; Redman, C. L. Real-world learning opportunities in sustainability: From classroom into the real world. International Journal of Sustainability in Higher Education; 2010; 11,
English, M. C.; Kitsantas, A. Supporting student self-regulated learning in problem- and project-based learning. Interdisciplinary Journal of Problem-Based Learning; 2013; 7,
Gómez Puente, S. M.; van Eijck, M.; Jochems, W. Empirical validation of characteristics of design-based learning in higher education. International Journal of Engineering Education; 2013; 29,
Halverson, E. R.; Sheridan, K. The maker movement in education. Harvard Educational Review; 2014; 84,
Hidayat, H.; Anwar, M.; Harmanto, D.; Dewi, F. K.; Orji, C. T.; Isa, M. R. M. Two decades of project-based learning in engineering education: A 21-year meta-analysis. TEM Journal; 2024; 13,
Hmelo-Silver, C. E. Problem-based learning: What and how do students learn?. Educational Psychology Review; 2004; 16,
Ifenthaler, D.; Yau, J. Y.-K. Utilising learning analytics to support study success in higher education: A systematic review. Educational Technology Research and Development; 2020; 68,
Kokotsaki, D.; Menzies, V.; Wiggins, A. Project-based learning: A review of the literature. Improving Schools; 2016; 19,
Kolb, D. A. Experimental learning: Experience as the source of learning and development; Prentice-Hall: 1984.
Kondo, A.; Hayashi, H.; Toyoshi, T. Project-based learning of mechanical design utilizing CAE structural analyses. Education Sciences; 2023; 13,
Krajcik, J. S.; Shin, N. Sawyer, R. K. Project-based learning. The Cambridge handbook of the learning sciences; 2nd ed. Cambridge University Press: 2014; pp. 275-297. [DOI: https://dx.doi.org/10.1017/CBO9781139519526.018]
Lavado-Anguera, S.; Velasco-Quintana, P.-J.; Terrón-López, M.-J. Project-based learning (PBL) as an experiential pedagogical methodology in engineering education: A review of the literature. Education Sciences; 2024; 14,
Lave, J.; Wenger, E. Situated learning: Legitimate peripheral participation; 1st ed. Cambridge University Press: 1991; [DOI: https://dx.doi.org/10.1017/CBO9780511815355]
López Carrillo, M. D.; Calonge García, A.; Lebrón Moreno, J. A. Self-regulation of student learning in a STEAM project. Education Sciences; 2024; 14,
Mills, J. E.; Treagust, D. F. Engineering education—Is problem-based or project-based learning the answer?. Australasian Journal of Engineering Education; 2003; 3,
Mota, F. B.; Cabral, B. P.; Braga, L. A. M.; Lopes, R. M. Mapping the global research on project-based learning: A bibliometric and network analysis (2014–2024). Frontiers in Education; 2025; 10, 1522694. [DOI: https://dx.doi.org/10.3389/feduc.2025.1522694]
National Institute of Technology. About KOSEN; National Institute of Technology: 2024; Available online: https://www.kosen-k.go.jp/english/index.html (accessed on 4 April 2025).
OECD. Equity in education: Breaking down barriers to social mobility; OECD: 2018; [DOI: https://dx.doi.org/10.1787/9789264073234-en]
Panadero, E.; Jonsson, A.; Pinedo, L.; Fernández-Castilla, B. Effects of rubrics on academic performance, self-regulated learning, and self-efficacy: A meta-analytic review. Educational Psychology Review; 2023; 35,
Prince, M. J.; Felder, R. M. Inductive teaching and learning methods: Definitions, comparisons, and research bases. Journal of Engineering Education; 2006; 95,
Ramírez De Dampierre, M.; Gaya-López, M. C.; Lara-Bercial, P. J. Evaluation of the implementation of project-based-learning in engineering programs: A review of the literature. Education Sciences; 2024; 14,
Ríos, I. D. L.; Cazorla, A.; Díaz-Puente, J. M.; Yagüe, J. L. Project–based learning in engineering higher education: Two decades of teaching competences in real environments. Procedia—Social and Behavioral Sciences; 2010; 2,
Robinson, K. Out of our minds: Learning to be creative (Fully rev. and updated ed); Capstone: 2011.
Sawyer, R. K. The Cambridge handbook of the learning sciences; Cambridge University Press: 2006.
Stern, A.; Rosenthal, Y.; Dresler, N.; Ashkenazi, D. Additive manufacturing: An education strategy for engineering students. Additive Manufacturing; 2019; 27, pp. 503-514. [DOI: https://dx.doi.org/10.1016/j.addma.2019.04.001]
Tsai, C.-Y.; Shih, W.-L.; Hsieh, F.-P.; Chen, Y.-A.; Lin, C.-L. Applying the design-based learning model to foster undergraduates’ web design skills: The role of knowledge integration. International Journal of Educational Technology in Higher Education; 2022; 19,
Tsutsumi, H.; Fujino, H.; Nakamura, G. STEAM education for second-year students at technical colleges: Creating bamboo-copters with 3DCAD and 3D printers. Journal of JSEE; 2024; 72,
Vygotsky, L. S. Mind in society: The development of higher psychological processes; Harvard University Press: 1981.
Zhong, C.; Lyu, K. Scaffolding junior middle school students’ engagement in online project-based learning during the COVID-19 pandemic: A case study from East China. Sage Open; 2022; 12,
Zimmerman, B. J. Becoming a self-regulated learner: An overview. Theory into Practice; 2002; 41,
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