Developing a robotics-based advocacy framework for electronics engineering using an integrated KANO–IPA–QFD approach
DOI:
https://doi.org/10.58712/ie.v3i1.47Keywords:
electronics engineering, robotics-based advocacy, educational robotics, industry 4.0, importance performance analysisAbstract
Electronics Engineering (ECE) remains strategically important to national technological capability and workforce development in the Philippines, yet student interest in the field is often weak and insufficiently structured. Although educational robotics has been widely associated with positive STEM-related outcomes, fewer studies have examined robotics as a discipline-specific advocacy strategy, and even fewer have translated student-valued robotics attributes into a structured framework for promoting a specific engineering discipline. Addressing this gap, this study developed a Robotics-Based Advocacy Framework for Electronics Engineering using an integrated KANO Model, Importance Performance Analysis (IPA), Quality Function Deployment (QFD) approach within a multiphase mixed-method framework development design. In Phase 1, 25 robotics learning attributes were analyzed. KANO results identified three Attractive attributes, hands-on robot building, sensor integration, and confidence-building tasks, while Better–Worse analysis showed the highest satisfaction gains for affordable robotics kits (56.19%), sensor integration (55.56%), and hands-on robot building (55.14%), with the strongest dissatisfaction risk found in clear learning modules and tutorials (-23.36%). IPA showed that 14 attributes were located in Quadrant I, while only community-centered robotics (F22) fell in Quadrant II, marking it as the primary enhancement area. QFD ranked Experiential Robotics Ecosystem first (64.5422; 22.32%), followed by Structured Curriculum Framework (54.9790; 19.02%), Faculty Development (31.5294; 10.91%), and Mentorship and Industry Linkages (30.0270; 10.39%). In Phase 2, pilot results showed positive mean gains across five domains, with the overall mean increasing from 3.02 to 3.44. The study produced a theory-informed, learner-centered, and data-driven advocacy framework, with pilot findings providing preliminary support for its practical relevance.
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References
Republic of the Philippines, Electronics Engineering Law of 2004. 2024. [Online]. Available: https://lawphil.net/statutes/repacts/ra2004/ra_9292_2004.html
Commission on Higher Education, Policies, Standards and Guidelines for the Bachelor of Science in Electronics Engineering (BSECE) Program. 2017. [Online]. Available: https://www.ustp.edu.ph/wp-content/uploads/2023/07/BS_ECE_CMO-101-s.-2017-BS-Electronics-Engineering.pdf
“DTI-BOI, NGAs eye 128K workers in PH’s semiconductor industry by 2028; vow to upskill Filipino workforce | Board of Investments.” [Online]. Available: https://boi.gov.ph/dti-boi-ngas-eye-128k-workers-in-phs-semiconductor-industry-by-2028-vow-to-upskill-filipino-workforce/
“April 2025 Electronics Engineers and Electronics Technicians Licensure Examinations Results Released in Three (3) Working Days | Professional Regulation Commission.” [Online]. Available: https://www.prc.gov.ph/article/april-2025-electronics-engineers-and-electronics-technicians-licensure-examinations-results
S. Bano, K. Atif, and S. A. Mehdi, “Systematic review: Potential effectiveness of educational robotics for 21st century skills development in young learners,” Educ. Inf. Technol. (Dordr)., vol. 29, no. 9, pp. 11135–11153, Jun. 2024, https://doi.org/10.1007/s10639-023-12233-2
F. Ouyang and W. Xu, “The effects of educational robotics in STEM education: a multilevel meta-analysis,” International Journal of STEM Education, vol. 11, no. 1. 2024. https://doi.org/10.1186/s40594-024-00469-4
I. Trapero-González, F. J. Hinojo-Lucena, J.-M. Romero-Rodríguez, and A. Martínez-Menéndez, “Didactic impact of educational robotics on the development of STEM competence in primary education: a systematic review and meta-analysis,” Front. Educ. (Lausanne)., vol. 9, Dec. 2024, https://doi.org/10.3389/feduc.2024.1480908
I. U. Cayetano-Jiménez, E. A. Martinez-Ríos, R. Bustamante-Bello, R. A. Ramírez-Mendoza, and M. S. Ramírez-Montoya, “Experimenting With Soft Robotics in Education: A Systematic Literature Review From 2006 to 2022,” IEEE Transactions on Learning Technologies, vol. 17, pp. 1249–1266, 2024, https://doi.org/10.1109/TLT.2024.3372894
N. A. Selcuk, S. Kucuk, and B. Sisman, “Does really educational robotics improve secondary school students’ course motivation, achievement and attitude?,” Educ. Inf. Technol. (Dordr)., vol. 29, no. 17, pp. 23753–23780, Dec. 2024, https://doi.org/10.1007/s10639-024-12773-1
A. Alam and A. Mohanty, “Integrated constructive robotics in education (ICRE) model: a paradigmatic framework for transformative learning in educational ecosystem,” Cogent Education, vol. 11, no. 1, Dec. 2024, https://doi.org/10.1080/2331186X.2024.2324487
S. Rapti, S. Tselegkaridis, T. Sapounidis, and S. A. Triantafyllou, “A bibliometric and content analysis of educational robotics’ impact on communication, collaboration, critical thinking, and creativity in kindergarten,” Think. Skills Creat., vol. 57, p. 101849, Sep. 2025, https://doi.org/10.1016/j.tsc.2025.101849
W. Xu and F. Ouyang, “Robotic roles in education: A systematic review based on a proposed framework of the learner-robot relationships,” Educ. Res. Rev., vol. 47, p. 100685, May 2025, https://doi.org/10.1016/j.edurev.2025.100685
A. D. la Hoz, L. Melo, F. Cañada, and J. Cubero, “Educational robotics for science and mathematics teaching: Analysis of pre-service teachers’ perceptions and self-confidence,” Heliyon, vol. 10, no. 21, p. e40032, Nov. 2024, https://doi.org/10.1016/j.heliyon.2024.e40032
F. A. Nannim, N. E. Ibezim, M. Mosia, and B. C. E. Oguguo, “Project-based learning with arduino robots: impact on undergraduate students’ achievement and task persistence in robotics programming,” Front. Robot. AI, vol. 12, Jul. 2025, https://doi.org/10.3389/frobt.2025.1615427
?. Dökme and Z. ?. Hanc?o?lu, “Three-stage robotic STEM program ignites secondary school students’ interest in STEM career and attitudes toward science,” Educ. Inf. Technol. (Dordr)., vol. 30, no. 9, pp. 12079–12100, Jun. 2025, https://doi.org/10.1007/s10639-025-13318-w
L. C. Dat, H. T. Pham, and N. T. Nguyen, “Effects of a block-based Arduino robotics course on computational thinking skills and STEM career interests of Vietnamese students,” Eurasia Journal of Mathematics, Science and Technology Education, vol. 21, no. 6, p. em2642, Jun. 2025, https://doi.org/10.29333/ejmste/16414
I. Torres and E. Inga, “Fostering STEM Skills Through Programming and Robotics for Motivation and Cognitive Development in Secondary Education,” Information (Switzerland), vol. 16, no. 2, 2025, https://doi.org/10.3390/info16020096
R. Zviel-Girshin and N. Rosenberg, “The Impact of Early Robotics on Kindergarten Children’s Self-Efficacy and Problem-Solving Abilities,” Educ. Sci. (Basel)., vol. 15, no. 11, p. 1436, Oct. 2025, https://doi.org/10.3390/educsci15111436
J. S. Eccles and A. Wigfield, “From expectancy-value theory to situated expectancy-value theory: A developmental, social cognitive, and sociocultural perspective on motivation,” Contemp. Educ. Psychol., vol. 61, 2020, https://doi.org/10.1016/j.cedpsych.2020.101859
A. Wigfield and J. S. Eccles, “Expectancy-value theory of achievement motivation,” Contemp. Educ. Psychol., vol. 25, no. 1, 2000, https://doi.org/10.1006/ceps.1999.1015
E. Hur, B. Boyle, K. Ardeleanu, L. Jeon, and I. Bubier, “What Drives Early Childhood Providers to Increase Quality? Using Expectancy-Value Theory to Understand Providers’ Motivations and Challenges in Quality Rating and Improvement Systems,” Early Educ. Dev., vol. 36, no. 3, pp. 542–567, Apr. 2025, https://doi.org/10.1080/10409289.2024.2404824
T. Törmänen et al., “Situational Motivation in Academic Learning: A Systematic Review,” Educ. Psychol. Rev., vol. 37, no. 2, p. 56, Jun. 2025, https://doi.org/10.1007/s10648-025-10036-0
L. Ma, Y. Jiao, L. Xiao, and J. Liu, “Three-way interactions of self-efficacy, intrinsic value, utility value, and gender on foreign language achievement: A moderated moderation model,” System, vol. 132, p. 103693, Aug. 2025, https://doi.org/10.1016/j.system.2025.103693
R. M. Adler, B. Rittle-Johnson, M. Hickendorff, and K. Durkin, “A longitudinal examination of the relations between motivation, math achievement, and STEM career aspirations among Black students,” Contemp. Educ. Psychol., vol. 76, p. 102240, Mar. 2024, https://doi.org/10.1016/j.cedpsych.2023.102240
R. W. Lent, S. D. Brown, and G. Hackett, “Toward a Unifying Social Cognitive Theory of Career and Academic Interest, Choice, and Performance,” Journal of Vocational Behavior, vol. 45, no. 1. 1994. https://doi.org/10.1006/jvbe.1994.1027
P. Damodar, A. Shetty, M. P. Dsouza, A. Prakash, and N. Gudi, “Crafting careers through theory-driven interventions: a scoping review of the utility of social cognitive career theory and career maturity inventory,” Int. J. Adolesc. Youth, vol. 29, no. 1, Dec. 2024, https://doi.org/10.1080/02673843.2024.2308081
N. Yamani and H. Almazroa, “Exploring career interest and STEM self-efficacy: implications for promoting gender equity,” Front. Psychol., vol. 15, Oct. 2024, https://doi.org/10.3389/fpsyg.2024.1402933
T. Ribeirinha, M. Baptista, and M. Correia, “Investigating the Impact of STEM Inquiry-Based Learning Activities on Secondary School Student’s STEM Career Interests: A Gender-Based Analysis Using the Social Cognitive Career Framework,” Educ. Sci. (Basel)., vol. 14, no. 10, p. 1037, Sep. 2024, https://doi.org/10.3390/educsci14101037
S. Papert, “Mindstorms Children, Computers, and Powerful Ideas (Second edition),” BasicBooks, vol. 1, 1980.
T. Papagiannopoulou, J. Vaiopoulou, and D. Stamovlasis, “Teachers’ Readiness to Implement Robotics in Education: Validation and Measurement Invariance of TRi-Robotics Scale via Confirmatory Factor Analysis and Network Psychometrics,” Behavioral Sciences, vol. 15, no. 9, p. 1227, Sep. 2025, https://doi.org/10.3390/bs15091227
N. Kano, N. Seraku, F. Takahashi, and S. Tsuji, “Attractive quality and must-be quality,” Journal of the Japanese Society for Quality Control, vol. 14, no. 2, 1984.
C. Z. Buffon, E. A. Cudney, and A. Elshennawy, “The application of the KANO model in education: A systematic literature review,” Journal of Management and Engineering Integration, vol. 17, no. 1, pp. 34–46, Jun. 2024, https://doi.org/10.62704/10057/28083
J. A. Martilla and J. C. James, “Importance-Performance Analysis,” J. Mark., vol. 41, no. 1, p. 77, Jan. 1977, https://doi.org/10.2307/1250495
L. K. Chan and M. L. Wu, “Quality function deployment: A literature review,” Eur. J. Oper. Res., vol. 143, no. 3, 2002, https://doi.org/10.1016/S0377-2217(02)00178-9
V. Braun and V. Clarke, “Using thematic analysis in psychology,” Qual. Res. Psychol., vol. 3, no. 2, 2006, https://doi.org/10.1191/1478088706qp063oa
Johhn w. Creswell; Vicki L. Plano Clark, “Designing and Conducting Mixed Methods Research.,” Organ. Res. Methods, vol. 12, no. 4, 2018.
L. Gavrilas and K. T. Kotsis, “Investigating perceptions of primary and preschool educators regarding incorporation of educational robotics into STEM education,” Contemporary Mathematics and Science Education, vol. 5, no. 1, p. ep24003, Mar. 2024, https://doi.org/10.30935/conmaths/14384
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