Development of stem skills and national identity through 3D modeling in educational programs

Taras  Lutsiuk; Hanna  Alieksieieva

doi:10.59400/fes1545

Development of stem skills and national identity through 3D modeling in educational programs

Taras Lutsiuk Berdyansk State Pedagogical University, Berdyansk 71100, Ukraine
Hanna Alieksieieva Department of Informatics and Computer Technologies in Management and Learning, Berdyansk State Pedagogical University, Berdyansk 71100, Ukraine

Article ID: 1545

DOI: https://doi.org/10.59400/fes1545

Keywords: secondary education; STEM education;3D modeling; national identity; blender

Abstract

This paper explores the advancement of STEM skills and national identity through the incorporation of 3D modeling into the curriculum of secondary educational institutions using Blender software. It focuses on how 3D modeling can be used to enhance students’ technical prowess and creativity while simultaneously bolstering their motivation and sense of civic duty. The significance of modern educational technologies in nurturing critical thinking, investigative skills, and effective communication, which together foster the holistic development of the individual, is emphasized. The main objective of this article is to showcase practical experiences in embedding 3D modeling within middle school curricula to cultivate STEM skills and promote a sense of patriotism. It examines how hands-on activities and projects can motivate students to actively engage in rebuilding and progressing their nation, underscoring the critical role of patriotic education in today’s learning environments. Illustrating this, the article describes a lesson titled “Creating 3D Objects,” where students utilize Blender to work on a mini-project called “Street of Peace and Freedom,” involving the design of 3D building models. This project saw enthusiastic participation from students, who not only developed new skills but also acknowledged their contributions towards the future of their country. The role of educators in selecting suitable educational programs and materials, especially the integration of Blender for teaching 3D computer graphics in ninth-grade informatics courses, is discussed. The insights from Taras Liutsiuk on the integration of textbooks and effective utilization of Blender’s functionalities highlight the need for skilled pedagogical approaches. Particular attention is paid to the impact of this teaching methodology on ninth graders, a critical period for intense development of both academic and social competencies. Furthermore, the article addresses the future prospects of STEM education in Ukraine, advocating for equitable education access for all students, enhancements in pedagogical strategies, and the adoption of cutting-edge technologies in educational settings.

References

[1]Methodical recommendations for the implementation of STEM education in general and extracurricular educational institutions of Ukraine for the 2017/2018 academic year (Ukrainian). Available online: https://drive.google.com/file/d/0B3m2TqBM0APKekwtZFdhWXJuODg/view (accessed on 26 June 2024).

[2]Concept project of STEM education in Ukraine (Ukrainian). Available online: http://mk-kor.at.ua/STEM/STEM_2017.pdf (accessed on 26 June 2024).

[3]Havrylenko NV. The role of digitization in legal regulation of certain functions of state authorities and administration (Ukrainian). Scientific Notes of the Taurida National University Named After V.I. Vernadsky. Series: Legal Sciences. 2023; 34(73)(5): 1–6. doi: 10.32782/TNU-2707-0581/2023.5/01

[4]Hura V, Nestorenko T, Makarenko T. Research of the ‘educational service’ category: an interdisciplinary approach (Ukrainian). Scientific Notes of Berdyansk State Pedagogical University. Series: Pedagogical Sciences. 2022; 1(2): 91–104. doi: 10.31494/2412-9208-2022

[5]Ostopolets I, Varina G. Features of using “LEARNINGAPPS” in working with students of the pedagogical university (Ukrainian). In: Professionalism of the Teacher: Theoretical and Methodical Aspects, Collection of Scientific Works “Methodological Materials for the Project ‘Digital Educational Space: Ukrainian-Polish Experience’”. 2021; No. 15, pp. 141-149. doi: 10.31865/2414-9292.15.2021.242970.

[6]EU4Digital Initiative (English). Available online: https://eufordigital.eu/discover-eu/the-eu4digital-initiative (accessed on 26 June 2024).

[7]Martynovych N. Distribution of regions by their functional type, as a stage for assessing the socio-economic efficiency of the country’s post-war development (Ukrainian). Economic Bulletin of the State Higher Educational Institution “Ukrainian State University of Chemical Technology”. 2023; 1: 70–78. doi: 10.32434/2415-3974-2022-17-1-70-78

[8]Grynevych L, Morze N, Vember V, et al. The role of digital technologies in the development of the STEM educational ecosystem (Ukrainian). Journal of Information Technologies and Learning Tools. 2021; 83(3). doi: 10.33407/itlt.v83i3.4461

[9]Alekseeva GM. Formation of future social educators’ readiness to use computer technologies in professional activities (Ukrainian). Berdyansk: Berdyansk State Pedagogical University; 2014.

[10]Alekseeva GM. Use of information and communication technologies in the process of professional training of pedagogical university students (Ukrainian). Current Issues in Natural-Mathematical Education. 2014; 184–191.

[11]Ovsyannikov O, Alekseeva G. The computer environment of the scientific research work of students in engineering-pedagogical specialties of computer profile as an object of design (Ukrainian). Youth and Market. 2019; 9(176). doi: 10.32843/2663-6085-2019-14-2-39

[12]Havrylenko NV. Legal principles of digital identification and representation of personality (Ukrainian). Current Problems of Politics. 2024; 73: 162–167. doi: 10.32782/app.v73.2024.23

[13]Iglesias-Pradas S, Hernandez-Garcia A, Chaparro-Pelaez J, et al. Extraordinary remote learning and academic performance of students in higher education during the COVID-19 pandemic: a practical study (Spanish). Computers in Human Behavior. 2021; 119: 106713. doi: 10.1016/j.chb.2021.106713

[14]Kravchenko NV, Alyeksyeyeva HM, Gorbatyuk LV. Curriculum optimization by the criteria of maximizing professional value and the connection coefficient of educational elements, using software tools (English). In: Proceedings of the 14th International Conference on ICT in Education, Research, and Industrial Applications (ICTERI 2018); May 14-17, 2018; Kyiv, Ukraine.

[15]Hrynevych LM, Morze NV. The role of digital technologies in the development of the STEM educational ecosystem (Ukrainian). Information Technologies and Learning Tools. 2021; 83(3): 1-25.

[16]Osadchyi VV, Valko NV, Kuzmich LV. Using augmented reality technologies for STEM education organization. Journal of Physics: Conference Series. 2021; 1840(1): 012027.

[17]Balyk N, Shmyger G, Oleksiuk V, et al. Model of professional retraining of teachers based on the development of STEM competencies. In: Proceedings of ICTERI 2018: ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer; 2018.

[18]Semerikov SO. At the intersection of knowledge: XVI International Conference on Mathematical, Natural Science, and Technological Education. Bulletin of the National Academy of Pedagogical Sciences of Ukraine. 2024; 6(1): 1-5.

[19]Shapovalov V, Strizhak O. Transdisciplinary integration of knowledge in the environment of a virtual STEM center. Current State of Scientific Research and Technologies in Industry. 2023; 4(26): 95-107.

[20]Strutynska O, Umryk M. Learning startups as a project-based approach in STEM education. E-learning. 2019; 529.

[21]Anderson N. Rethinking STEM Education: Pathways to an Inclusive and Equitable System (English). 2020.

[22]Resnick M. Lifelong Kindergarten: Cultivating Creativity through Projects, Passions, Peers, and Play. Cambridge, Massachusetts: MIT Press; 2017.

[23]Sen C, Ay Z, Kıray S. STEM Skills in the 21st Century Education. 2018.

[24]Corbett T, Dumares CC, Barnaby T, et al. The Framework for integrative Science, Technology, Engineering and Mathematics (STEM) Education Endorsement guidelines. Pennsylvania; 2014.

[25]Bureau of Education U, Ng S. Exploring STEM competences for the 21st century (English). UNESCO: United Nations Educational, Scientific and Cultural Organisation; 2019. Switzerland.

[26]Calvani A. Models and Instruments for assessing Digital Competence at School. Journal of E-Learning and Knowledge Society. 2008; 4(3): 183–193.

[27]Nugroho OF, Permanasari A, Firman H. STEM Learning for Science Education Program: Reference to Indonesia. Jurnal Inspirasi Pendidikan. 2021; 11(2): 90-100.

[28]Woo H, Heo N, Jang H, et al. Parental and school factors on American high school students’ academic and career intentions in STEM fields. International Journal for Educational and Vocational Guidance. 2021; 1-24.

[29]Habibaturrohmah Z, Kaniawati I, Solihat R, et al. How to Integrate ESD (Education for Sustainable Development) into STEM Education?: A Systematic Literature Review. Journal of Natural Science and Integration. 2023; 6(2): 270-282.

[30]Korbel P. Measuring STEM in Vocational Education and Training. National Centre for Vocational Education Research (NCVER). 2016.

[31]Sejati BK. Developing science-technology-engineering-mathematics (stem)-based workbook to enhance secondary students’ stem competencies on lever system in human body [PHD thesis]. Universitas Pendidikan Indonesia; 2016.

[32]Siekmann G. What Is STEM? The Need for Unpacking Its Definitions and Applications. National Centre for Vocational Education Research (NCVER); 2016.

[33]Song M. Integrated STEM teaching competencies and performances as perceived by secondary teachers in South Korea. International Journal of Comparative Education and Development. 2020; 22(2): 131-146.

[34]Khomenko VG, Pavlenko LV. Problems of preparing future engineering educators for the use of computer technologies for statistical processing of experimental data (Ukrainian). Problems of Engineering-Pedagogical Education. 2010; (26-27): 149-155.

[35]Martynovych N, Boichenko E, Dielini M. Renovation of Ukraine after war: Special restoration regimes and stimulation of sustainable development of territories. International Review for Spatial Planning and Sustainable Development. 2023; 11(4): 54-70.

[36]Nestorenko T, Nestorenko O, Peliova J. Displaced and fake universities—Experience of Ukraine (English). In: Proceedings of the Economic, Political and Legal Issues of International Relations 2017; June 9-10, 2017; Virt, Slovakia.

[37]Lutsyuk T. Creating 3D models. Practical work #11 [Video]. YouTube. 2024. Available online: https://www.youtube.com/watch?v=gbabF32r9zs&ab_channel=ТарасЛуцюк (accessed on 4 June 2024).

[38]Morze NV, Gladun MA, Dzyuba SM. Formation of key and subject competencies of students by robotic means of STEM education (Ukrainian). Information Technologies and Learning Tools. 2018; 65(3): 37–52.

[39]Bosko N. The Role of ICT in the Implementation of the Interdisciplinary STEM Approach to Education in the Context of Sustainable Development. In: Information and Communication Technologies and Sustainable Development: Collected Monographs from the XXI International Scientific and Practical Conference; 14–16 November 2022; Kyiv, Ukraine.

[40]Ostopolets I, Pachadzhi D, Konovalova V. The impact of the birth order of a child in the family on the formation of her lifestyle (Ukrainian). Scientific Bulletin of Sivershchyna. Series: Education. Social and Behavioral Sciences. 2022; 8(1): 141-153. doi: 10.32755/sjeducation.2022.01.141

Published

2025-02-25

How to Cite

Lutsiuk, T., & Alieksieieva, H. (2025). Development of stem skills and national identity through 3D modeling in educational programs. Forum for Education Studies, 3(1), 1545. https://doi.org/10.59400/fes1545

Download Citation

Issue

Vol. 3 No. 1 (2025)

Section

Article

This work is licensed under a Creative Commons Attribution 4.0 International License.

Editor-in-Chief

Dr. Surapati Pramanik

Nandalal Ghosh B.T. College, India

eISSN

3029-2956

Publication Frequency

Quarterly

About the Publisher

Academic Publishing insists on taking academic exchange and publication as the main line, carrying out comprehensive management based on science and technology, and fully exploring excellent international publishing resources. Within 5 years, it will form a strategic framework and scale with science (S), technology (T), medicine (M), education (E), and humanities and arts (H) as the main publishing fields. Academic Publishing is headquartered in Singapore and based in Malaysia, with the United States and China providing the main scientific and academic resources. At the same time, it has established long-term good cooperative relations with other publishing companies, scientific research communities, and academic organizations in more than a dozen countries and regions. Academic Publishing uses English and Chinese as its main publishing languages, mainly publishing books, journals, and conference papers in print and online. The vast majority of publications follow the international open access policy, providing stable and long-term quality and professional publications. With the joint efforts of the expert team and our professional editorial team, our publications will gradually be indexed by international databases in stages to provide convenient and professional retrieval for various scholars. At the same time, manuscripts we accept will be subject to the peer review principle, and cutting-edge and innovative research articles will be preferentially accepted for peer reference and discussion. All kinds of our publications are welcome for peer to contribute, access, and download.

more

Volume Arrangement

2025

2024

2023

Featured Articles

Diachronic analysis of Spanish scientific production on teaching methodologies in Primary Education: A scientometric and conceptual perspective (2000–2023)

The evaluation of science is essential to ensuring the quality, validity, and reliability of scientific results. Science needs to undergo a review process to ensure the rigorousness of scientific output. This evaluation provides a solid basis for political and economic decisions related to the design and execution of research projects, the establishment of new lines of research, or the identification of areas of specialization. This paper analyses diachronically the Spanish scientific production related to the implementation and development of teaching methodologies in primary education and indexed in the Scopus and Web of Science databases during the period 2000–2023. This analysis is carried out on the one hand, from a scientometric perspective, based on the analysis of indicators such as diachronic production, the journals with the highest scientific productivity, and the most productive institutions, and, on the other hand, from a conceptual perspective, trying to define its relationship with other areas of education. The general results of this study reveal two clear stages: the first, up to 2010, with little scientific production; and the second, from 2011 onwards, characterised by a general growth. The relationship between this field and others such as initial teacher training, ICT, and didactics is also evident.

The relationship between teachers’ self-efficacy and classroom management practices in secondary schools

This study explores the intricate relationship between teacher self-efficacy and classroom management practices in secondary schools in the Mansehra district of Pakistan. Teacher self-efficacy, defined as the belief in one’s ability to manage and influence classroom environments effectively, has been identified as a critical factor influencing both teaching performance and student outcomes. The research employed a mixed-method approach, gathering data from 62 teachers and 310 students using both online surveys (via Google Forms) and physical questionnaires to ensure a diverse and inclusive participant pool. Data analysis was conducted using two complementary tools: SPSS and Smart PLS. SPSS was used for descriptive statistics and inferential analyses, such as t-tests, chi-square tests, and measures of central tendency, to offer an overview of group differences and relationships between variables. Meanwhile, Smart PLS was employed for Partial Least Squares Structural Equation Modeling (PLS-SEM), a technique suited for complex models and smaller sample sizes. This method allowed for the analysis of both direct and indirect relationships between the study variables—teacher self-efficacy, teaching practices, and classroom management. The findings reveal a significant positive correlation between teacher self-efficacy and classroom management practices. Additionally, teaching practices were found to mediate this relationship, indicating that higher levels of self-efficacy not only directly improve classroom management but also enhance teaching performance, which in turn contributes to better-managed classrooms. These results suggest that interventions aimed at enhancing teacher self-efficacy can have far-reaching effects on educational outcomes. The study highlights the need for focused teacher development programs that foster self-efficacy, thereby improving classroom management, student engagement, and overall academic success.

Artificial Intelligence in education: A comprehensive study

This comprehensive study delves into the multifaceted role of AI in education, exploring its applications, benefits, challenges, and future implications. The purpose of the study is to show how AI in education helps educators identify gaps in student knowledge and provide targeted feedback to improve learning outcomes. As a methodology, the library method and the study and review of various documents have been used in this research. The study examines the diverse range of AI technologies employed in educational settings, including intelligent tutoring systems, personalized learning platforms, educational chatbots, and virtual reality simulations. Furthermore, the study delves into the numerous benefits that AI brings to education. It highlights how AI-powered analytics and data-driven insights enable educators to gain deeper insights into student learning patterns, identify areas for improvement, and tailor instructional strategies accordingly. Additionally, AI-driven tools promote inclusivity by providing personalized support to learners with diverse needs and learning styles. Despite its transformative potential, the study also acknowledges the challenges and ethical considerations associated with integrating AI into education. Data privacy, algorithmic bias, and the digital divide are examined in detail, emphasizing the importance of responsible AI deployment and ethical guidelines. Looking ahead, the study explores the future implications of AI in education and the evolving role of educators in AI-enabled classrooms. It discusses how AI technologies will continue to evolve, offering new opportunities for collaborative learning, skill development, and lifelong education. In conclusion, this comprehensive study underscores the profound impact of AI on education and the need for thoughtful implementation strategies that prioritize equity, inclusivity, and ethical considerations. By harnessing the potential of AI, education systems can better prepare learners for the challenges and opportunities of the future.

Enhancing adult learner success in higher education through decision tree models: A machine learning approach

This article explores the use of machine learning, specifically Classification and Regression Trees (CART), to address the unique challenges faced by adult learners in higher education. These learners confront socio-cultural, economic, and institutional hurdles, such as stereotypes, financial constraints, and systemic inefficiencies. The study utilizes decision tree models to evaluate their effectiveness in predicting graduation outcomes, which helps in formulating tailored educational strategies. The research analyzed a comprehensive dataset spanning the academic years 2013–2014 to 2021–2022, evaluating the predictive accuracy of CART models using precision, recall, and F1 score. Findings indicate that attendance, age, and Pell Grant eligibility are key predictors of academic success, demonstrating the strong capability of the model across various educational metrics. This highlights the potential of machine learning (ML) to improve data-driven decision-making in educational settings. The results affirm the effectiveness of Decision Tree (DT) models in meeting the educational needs of adult learners and underscore the need for institutions to adapt their strategies to provide more inclusive and supportive environments. This study advocates for a shift towards nuanced, data-driven approaches in higher education, emphasizing the development of strategies that address the distinct challenges of adult learners, aiming to enhance inclusivity and support within the sector.