Research consistently emphasizes the critical need to “start early” in informatics education. Young children possess a natural curiosity, and early exposure to computational thinking (CT) and computer science (CS) can effectively challenge stereotypes about who can excel in these fields (Ching, 2018; Scherer, 2019). An OECD working paper, “The State of the Field of Computational Thinking in Early Childhood Education” (Bers et al., 2022), underscores how CT fosters connections across traditional subjects and significantly supports cognitive and social development. It enhances crucial high-level skills such as abstraction, problem-solving, and critical thinking. However, it’s essential that educational initiatives designed for young learners are carefully aligned with their developmental stage. In the early primary years, children are developing foundational literacy and numeracy, characterized by shorter attention spans and working memory limitations. They are also beginning to engage in collaborative activities. Simultaneously, their innate curiosity drives them to explore the world through tactile experiences, experimentation, and creative expression (Bers, 2020, 2022; Su & Yang, 2023).
Despite the recognized importance, the “Informatics Education at School in Europe” Eurydice report from 2022 reveals that informatics is not yet widely established as a distinct subject in primary education across Europe (European Commission / EACEA / Eurydice, 2022). Many education systems postpone its introduction until later primary or lower-secondary levels. This observation is consistent with the “Digital Competence at School” Eurydice 2023 report (European Commission / EACEA / Eurydice, 2023), which advocates for earlier integration to effectively bolster digital competence development.
The academic consensus is strong: informatics teaching in primary education is vital. To create High-quality K-12 Learning Environments for this crucial subject, educators and policymakers must consider several key aspects.
In primary school settings, pedagogical approaches like pair programming (Wei et al., 2021) and game-based learning (Asbell-Clarke et al., 2021; Ching et al., 2018; Hooshyar et al., 2020; Israel-Fishelson & Hershkovitz, 2020) stand out as particularly effective strategies for engaging young students and fostering computational thinking skills within high-quality k-12 learning environments.
Advancing Informatics Education in Secondary Levels
As students progress to lower and upper secondary education, informatics education needs to evolve. It transitions from foundational concepts in lower-secondary to more specialized and adaptable methodologies in upper-secondary, reflecting the distinct goals of each educational stage. The 2022 Eurydice report, “Informatics Education at School in Europe,” provides a clear picture of this progression (European Commission / EACEA / Eurydice, 2022):
- Lower-Secondary Education: Informatics is recognized as a separate discipline in 35 education systems. Roughly half of these offer it as a standalone, compulsory subject, usually across all grades. About a quarter integrate informatics into other compulsory subjects rather than teaching it separately.
- Upper-Secondary Education: Informatics is almost universally taught as a distinct discipline. The majority of countries offer one or more informatics subjects—compulsory, optional, or both—in at least one grade. Integration of informatics into other subjects is less common at this level, indicating a move towards greater specialization and flexibility.
The 2023 “State of Computer Science Education in the U.S.A.” report (Code.org et al., 2023) emphasizes the critical nature of lower-secondary school. During this period, students develop greater independence and explore their interests and intellectual curiosities through a broader curriculum. Positive learning experiences in informatics at this stage are crucial to encourage continued engagement in upper-secondary education. Upper-secondary school provides opportunities for students to delve into computing projects with greater depth and creativity. Furthermore, offering course credits for graduation and college can significantly increase participation in CS courses.
Research highlights key strategies for high-quality k-12 learning environments in secondary informatics education (Kampylis et al., 2023):
The Growing Interplay of Informatics Education and Artificial Intelligence
Artificial Intelligence (AI) is a significant and expanding field within Computer Science, encompassing logic, reasoning, learning, perception, and ethics (Mustafa et al., 2024).
AI’s transformative impact on our world, including education, has spurred substantial policy responses. The 2019 Beijing Consensus on AI and Education (UNESCO, 2019), endorsed by numerous Education Ministers and UN representatives, set guidelines to ensure AI enhances teaching while protecting educators’ rights. Similarly, a Council of Europe analysis in 2019 explored AI’s educational implications, emphasizing human rights, democracy, and law, and advocating for universal AI literacy covering both technical and humanistic aspects (Holmes et al., 2022).
The European Commission’s Digital Competence Framework for Citizens (DigComp 2.2) updated in 2022, now includes the knowledge, skills, and attitudes needed for safe and critical interaction with AI-driven systems (Vuorikari et al., 2022). A European Schoolnet analysis (Cukurova et al., 2024) identified key domains for AI in education: developing AI for educational support, promoting ethical AI literacy, and adapting education for an AI-driven future. The European Digital Education Hub’s Squad on Artificial Intelligence in Education (2023a) categorizes teacher AI competencies into Teaching for AI, Teaching with AI, and Teaching about AI, each crucial for preparing students for an AI-integrated world.
For “teaching about AI,” the AI4K12 initiative’s “5 Big Ideas in AI” framework (Touretzky & Gardner-McCune, 2022) is widely adopted. Spain’s School of Computational Thinking and Artificial Intelligence initiative, based on this framework, implemented a large-scale AI literacy program (Moreno León et al., 2021), reaching 7,000 students and 300 teachers. This extensive study (Rizvi et al., 2023) showed improved AI understanding across pre-university levels, demonstrating the effectiveness of teacher training in AI education (Ayanwale et al., 2022).
A systematic review of K-12 AI curriculum implementations (Su et al., 2022) revealed improved student outcomes in AI concepts, including machine learning and deep learning, alongside increased interest and positive learning attitudes. Rizvi et al.’s (2023) analysis of 28 K-12 AI education studies supports these findings, noting the field’s ongoing development. UNESCO’s analysis of government AI curricula (Miao & Shiohira, 2022) highlights the need for creative AI development and ethical considerations, emphasizing teacher preparation for successful implementation in high-quality k-12 learning environments.
The TeachAI initiative (TeachAI, 2024), supported by global organizations like the World Economic Forum and Code.org, promotes a systemic approach to integrate AI in education, offering policy ideas and guidance for curriculum, pedagogy, and assessment adaptation to create high-quality k-12 learning environments that are future-ready.
Addressing Open Issues in Informatics Education
Despite policy prioritization and increasing adoption, several challenges hinder the successful implementation of informatics education in EU education systems (Bocconi et al., 2022; European Commission / EACEA / Eurydice, 2022; European Commission, 2020a). These include:
- Equity and Gender Balance: Promoting inclusive practices to address disparities and ensure equal opportunities.
- Teacher Education and Training: Educating, recruiting, training, and upskilling teachers to effectively deliver informatics education.
- Inclusive School Ecosystems: Creating innovative and open school environments with strong leadership support.
- Curriculum Design and Resources: Developing quality, age-appropriate resources and tools for comprehensive informatics education.
- Effective Assessment Practices: Implementing robust methods for assessing informatics skills.
These issues underscore the urgent need for coordinated efforts and policy interventions to strengthen informatics education and build high-quality k-12 learning environments.
Addressing Student-Related Issues
Informatics education offers significant benefits for 21st-century students, enhancing both academic and professional opportunities. Research indicates that students engaged in informatics are more likely to pursue college education and develop strong problem-solving abilities (Brown & Brown, 2020; Salehi et al., 2020).
Understanding informatics cultivates essential digital competencies, crucial in today’s technology-driven world. High-quality informatics education in K-12 plays a vital role in nurturing talent by providing foundational skills in CT, problem-solving, and digital literacy (Román-González et al., 2018). This early exposure demystifies technology and inspires students to innovate, laying a solid foundation for future expertise in the field and contributing to high-quality k-12 learning environments.
Promoting Equity and Inclusion
While CS curricula are being adopted globally (Vegas et al., 2021), achieving universal digital competence requires addressing equity, inclusion, and gender balance (Bocconi et al., 2022).
Effective solutions require the active involvement of policymakers, educators, families, researchers, and industry partners. By fostering inclusivity and equitable resource access, informatics education ensures all learners can develop their talents and pursue tech careers. Hsu et al. (2019) highlight a global trend toward expanding access to CS and STEM, especially for girls and underrepresented minorities, through compulsory informatics education in high-quality k-12 learning environments.
Current data shows uneven access, with only 12 of 37 Eurydice network countries mandating informatics as a compulsory, separate subject throughout K-12 (Eurydice, 2022). Even with compulsory informatics, language, culture, gender, or other characteristics can hinder full participation. Studies on ethnicity, disabilities, and special needs in informatics education (Das et al., 2020; Ray et al., 2018; STEM learning, 2022) offer insights into developing core CS skills for all students in high-quality k-12 learning environments.
The CAPE Framework (Capacity, Access, Participation, and Experience) provides a comprehensive approach to assessing and improving equity in CS education (Fletcher & Warner, 2021).
Culturally Relevant Informatics Education
European classrooms are increasingly diverse (Spinthourakis et al., 2011). Culturally Relevant Pedagogy (Ladson-Billings, 1995) emphasizes academic achievement, cultural competence, and sociopolitical consciousness. Teachers can leverage students’ cultural backgrounds to create more inclusive high-quality k-12 learning environments. This boosts motivation, agency, critical thinking, and problem-solving by connecting informatics to real-world applications.
Professional development focusing on equality, diversity, and inclusion is crucial (Kemp et al., 2024). Culturally relevant pedagogy and resources (Hello World, 2021; Hoffman et al., 2022; Yuen et al., 2016) ensure educational materials resonate with diverse student backgrounds, making learning more engaging and relevant in high-quality k-12 learning environments. These strategies create equitable informatics education and prepare diverse learners for a globalized, technologically advanced, and multicultural society.
Addressing Gender Differences
Women remain underrepresented in technology, comprising only 19.4% of ICT professionals in Europe (Eurostat, 2023) and 22% of AI specialists globally (World Economic Forum). This disparity is a growing concern (European Commission, 2021), especially as IT systems increasingly reflect the biases of their creators. Diversifying the IT workforce requires addressing the low uptake of computing education among girls (Kemp et al., 2024). The DEAP 2021–2027 promotes early informatics education to tackle gender stereotypes and increase women’s participation in ICT (Aivaloglou & Hermans, 2019; European Education and Culture Executive Agency, 2022) to foster high-quality k-12 learning environments for all genders.
Research on gender differences in computing education is mixed (Kampylis et al., 2023). Some studies find no significant gender differences in CT skills (Atmatzidou & Demetriadis, 2016; del Olmo-Muñoz et al., 2020; Tsai et al., 2020; Wu & Su, 2021), while others report differences (Fraillon et al., 2020; Labusch & Eickelmann, 2020; Román-González et al., 2017; Wei et al., 2021). Task type and assessment methods can influence these findings (Atmatzidou & Demetriadis, 2016; del Olmo-Muñoz et al., 2020; Israel-Fishelson et al., 2021; Román-González et al., 2017). For instance, boys may show higher decomposition thinking, while girls may excel in abstraction tasks after age 9.5 (Rijke et al., 2018; Tsai et al., 2020). Self-efficacy, motivation, and gender stereotypes also play a role (Aivaloglou & Hermans, 2019; Durak & Saritepeci, 2018).
Developmental trajectories and educational approaches can contribute to gender differences (Kong et al., 2018; Sun et al., 2021). Girls show higher STEM learning attitudes, while boys are more interested in programming, indicating a need for targeted engagement strategies (Kong et al., 2018). Compulsory informatics education and targeted recruitment in elective subjects are recommended (Balanskat et al., 2018). Innovative approaches, like blending dance choreography with programming (Leonard et al., 2021), and enrichment programs like Hour of Code and Tech Future for Girls (Hsu et al., 2019), can boost girls’ participation and create high-quality k-12 learning environments. Teacher training is crucial for fostering gender balance and equity (Cateté et al., 2020).
EU policy initiatives aim to balance gender participation in informatics education (European Commission / EACEA / Eurydice, 2022). The consensus is that all children can develop informatics skills regardless of gender (del Olmo-Muñoz et al., 2020) with inclusive resources, activities, and strategies to support all students’ self-belief (Kemp et al., 2024) in high-quality k-12 learning environments.
Fostering Digital Talent
The digital talent gap in most EU Member States threatens Europe’s competitiveness. Informatics education in K-12 serves to equip all students for the digital world and to cultivate talent for Europe’s economic future (European Commission, 2020b).
Research suggests that ‘computationally talented’ learners can be identified in lower-secondary education (Román-González et al., 2018), progressing faster in informatics. However, formal education systems often lack provisions for identifying and nurturing this talent within high-quality k-12 learning environments.
Initiatives like the Bebras Computing Challenge and the International Olympiad in Informatics, alongside extracurricular programs, can help bridge the digital talent gap. They introduce CT through engaging tasks and make informatics accessible to a wider audience, while also identifying and fostering talent early. The International Olympiad in Informatics, running since 1989, assesses core informatics competences (Dagienė & Stupurienė, 2016). National initiatives like Poland’s Informatics Talent Development Programme (2019–2029) also support talented students in upper-secondary schools.
These programs and competitions identify and nurture exceptional talent, providing pathways to advanced IT studies and careers. They build collaborative networks among aspiring computer scientists, fostering individual growth and career advancement. Encouraging participation in these initiatives is vital for building a robust pipeline of skilled IT professionals and ensuring Europe’s technological innovation and competitiveness, all starting with high-quality k-12 learning environments.
References
Aivaloglou, E., & Hermans, F. (2019). Early Programming Education and Career Orientation: The Effects of Gender, Self-Efficacy, Motivation and Stereotypes. In Proceedings of the 50th ACM Technical Symposium on Computer Science Education, 679–685. https://doi.org/10.1145/3287324.3287358
Asbell-Clarke, J., Rowe, E., Almeda, V., Edwards, T., Bardar, E., Gasca, S., Baker, R. S., & Scruggs, R. (2021). The Development of students’ computational thinking practices in elementary- and middle-school classes using the learning game Zoombinis. Computers in Human Behavior, 115, 1–14. https://doi.org/10.1016/j.chb.2020.106587
Atmatzidou, S., & Demetriadis, S. (2016). Advancing students’ computational thinking skills through educational robotics: A study on age and gender relevant differences. Robotics and Autonomous Systems, 75, 661–670. https://doi.org/10.1016/j.robot.2015.10.008
Ayanwale, M. A., Sanusi, I. T., Adelana, O. P., Aruleba, K. D., & Oyelere, S. S. (2022). Teachers’ readiness and intention to teach artificial intelligence in schools. Computers and Education: Artificial Intelligence, 3, 100099. https://doi.org/10.1016/j.caeai.2022.100099
Balanskat, A., Engelhardt, K., & Licht, A. H. (2018). Strategies to include computational thinking in school curricula in Norway and Sweden—European Schoolnet’s 2018 Study Visit. European Schoolnet.
Bers, M. U. (2020). Coding as a Playground. Routledge. https://doi.org/10.4324/9781003022602
Bers, M. U. (2022). Beyond Coding. MIT Press.
Bers, M. U., Strawhacker, A., & Sullivan, A. (2022). The state of the field of computational thinking in early childhood education. In: OECD education working papers, no. 274. OECD Publishing. https://doi.org/10.1787/3354387a-en
Bocconi, S., Chioccariello, A., Kampylis, P., Dagienė, V., Wastiau, P., Engelhardt, K., Earp, J., Horvath, M., Jasutė, E., Malagoli, C., Masiulionytė-Dagienė, V., & Stupurienė, G. (2022). Reviewing computational thinking in compulsory education: State of play and practices from computing education. Publications Office of the European Union. https://doi.org/10.2760/126955
Brown, E. A., & Brown, R. S. (2020). The Effect of Advanced Placement Computer Science Course Taking on College Enrollment. West Coast Analytics. https://www.westcoastanalytics.com/uploads/6/9/6/7/69675515/longitudinal_study_-_combined_report_final_3_10_20__jgq_.pdf
Cateté, V., Alvarez, L., Isvik, A., Milliken, A., Hill, M., & Barnes, T. (2020). Aligning Theory and Practice in Teacher Professional Development for Computer Science. ACM Int. Conf. Proc. Ser. 20th Koli Calling Conference on Computing Education Research, Koli Calling 2020. https://doi.org/10.1145/3428029.3428560
Ching, Y.-H., Hsu, Y. C., & Baldwin, S. (2018). Developing Computational thinking with educational technologies for young learners. TechTrends, 62(6), 563–573. https://doi.org/10.1007/s11528-018-0292-7
Code.org / CSTA / ECEP Alliance. (2023). 2023 State of Computer Science Education. Retrieved from https://advocacy.code.org/stateofcs
Cukurova, M., Kralj, L., Hertz, B. & Saltidou, E. (2024). Professional Development for Teachers in the Age of AI. European Schoolnet.
Dagienė, V., & Stupurienė, G. (2016). Bebras—A sustainable community building model for the concept based learning of informatics and computational thinking. Informatics in Education, 15(1), 25–44. https://doi.org/10.15388/infedu.2016.02
Das, M., Marghitu, D., Jamshidi, F., Mandala, M., & Howard, A. (2020). Accessible Computer Science for K-12 Students with Hearing Impairments (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2007.08476
del Olmo-Muñoz, J., Cózar-Gutiérrez, R., & González-Calero, J. A. (2020). Computational thinking through unplugged activities in early years of Primary Education. Computers & Education, 150, 103832. https://doi.org/10.1016/j.compedu.2020.103832
Durak, H. Y., & Saritepeci, M. (2018). Analysis of the relation between computational thinking skills and various variables with the structural equation model. Computers & Education, 116, 191–202. https://doi.org/10.1016/j.compedu.2017.09.004
European Commission. (2020a). Communication from the Commission accompanying the Digital Education Action Plan 2021–2027 – Resetting education and training for the digital age. COM/2020/624 final. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52020DC0624
European Commission. (2020b). Commission Staff Working Document accompanying the Digital Education Action Plan 2021–2027- SWD(2020) 209 final. https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:52020SC0209
European Commission. (2021). Commission’s 2021 Women in Digital Scoreboard. Shaping Europe’s Digital Future. https://digital-strategy.ec.europa.eu/en/news/women-digital-scoreboard-2021
European Commission / EACEA / Eurydice. (2022). Informatics education at school in Europe. Eurydice report. Publications Office of the European Union. https://doi.org/10.2797/268406
European Commission / EACEA / Eurydice (2023). Structural indicators for monitoring education and training systems in Europe 2023 – Digital competence at school. Publications Office of the European Union. https://doi.org/10.2797/886074
European Education and Culture Executive Agency. (2022).
Fletcher, C. L., & Warner, J. R. (2021). CAPE: A framework for assessing equity throughout the computer science education ecosystem. Communications of the ACM, 64(2), 23–25. https://doi.org/10.1145/3442373
Fraillon, J., Ainley, J., Schulz, W., Friedman, T., & Duckworth, D. (2020). Preparing for life in a digital world: IEA international computer and information literacy study 2018 international report. Springer International Publishing. https://doi.org/10.1007/978-3-030-38781-5
Hello World. (2021). October). Computing for all: Designing a culturally relevant curriculum. Hello World, 17, 54–55.
Hoffman, D. L., Leong, P., Ka’aloa, R. P. H., & Paek, S. (2022). Teachers’ perspectives on culturally-relevant computing: Principles and processes. TechTrends, 66(3), 423–435. https://doi.org/10.1007/s11528-022-00733-w
Holmes, W., Persson, J., Chounta, I. A., Wasson, B., & Dimitrova, V. (2022). Artificial intelligence and education: A critical view through the lens of human rights, democracy and the rule of law. Council of Europe.
Hooshyar, D., Pedaste, M., Yang, Y., Malva, L., Hwang, G.-J., Wang, M., Lim, H., & Delev, D. (2020). From gaming to computational thinking: An Adaptive educational computer game-based learning approach. Journal of Educational Computing Research, 59(3), 383–409. https://doi.org/10.1177/0735633120965919
Hsu, Y.-C., Irie, N. R., & Ching, Y.-H. (2019). Computational thinking educational policy initiatives (CTEPI) across the globe. TechTrends, 63(3), 260–270. https://doi.org/10.1007/s11528-019-00384-4
Israel-Fishelson, R., & Hershkovitz, A. (2020). Persistence in a game-based learning environment: The case of elementary school students learning computational thinking. Journal of Educational Computing Research, 58(5), 891–918. https://doi.org/10.1177/0735633119887187
Israel-Fishelson, R., Hershkovitz, A., Eguíluz, A., Garaizar, P., & Guenaga, M. (2021). The associations between computational thinking and creativity: The role of personal characteristics. Journal of Educational Computing Research, 58(8), 1415–1447. https://doi.org/10.1177/0735633120940954
Kampylis, P., Dagienė, V., Bocconi, S., Chioccariello, A., Engelhardt, K., Stupurienė, G., Masiulionytė-Dagienė, V., Jasutė, E., Malagoli, C., Horvath, M., & Earp, J. (2023). Integrating computational thinking into primary and lower secondary education: A systematic review. Educational Technology & Society, 26(2), 99–117.
Kemp, P., Wong, B., Hamer, J., & Copsey-Blak, M. (2024). The future of Computing Education—Considerations for Policy, Curriculum and Practice. https://www.kcl.ac.uk/ecs/assets/kcl-scari-computing.pdf
Kong, S.-C., Chiu, M. M., & Lai, M. (2018). A study of primary school students’ interest, collaboration attitude, and programming empowerment in computational thinking education. Computers and Education, 127, 178–189. https://doi.org/10.1016/j.compedu.2018.08.026
Labusch, A., & Eickelmann, B. (2020). Computational Thinking Competences in Countries from Three Different Continents in the Mirror of Students’ Characteristics and School Learning. In S. C. Kong, H. U. Hoppe, T. C. Hsu, R. H. Huang, B. C. Kuo, K. Y. Li, C. K. Looi, M. Milrad, J. L. Shih, K. F. Sin, K. S. Song, M. Specht, F. Sullivan, & J. Vahrenhold (Eds.), Proceedings of International Conference on Computational Thinking Education 2020 (pp. 2–7). The Education University of Hong Kong.
Ladson-Billings, G. (1995). Toward a theory of culturally relevant pedagogy. American Educational Research Journal, 32(3), 465–491. https://doi.org/10.3102/00028312032003465
Leonard, A. E., Daily, S. B., Jörg, S., & Babu, S. V. (2021). Coding moves: Design and research of teaching computational thinking through dance choreography and virtual interactions. Journal of Research on Technology in Education, 53(2), 159–177. https://doi.org/10.1080/15391523.2020.1760754
Miao, F., & Shiohira, K. (2022). K-12 AI curricula. A mapping of government-endorsed AI curricula. UNESCO Publishing, 3, 1144399.
Moreno León, J., Román-González, M., & Robles, G. (2021). Escuela de Pensamiento Computacional e Inteligencia Artificial 20/21: Enfoques y propuestas para su aplicación en el aula. Resultados de la investigación. Available at https://www.libreria.educacion.gob.es/libro/escuela-de-pensamiento-computacional-e-inteligencia-artificial-20-21-enfoques-y-propuestas-para-su-aplicacion-en-el-aula-resultados-de-la-investigacion_176816
Mustafa, M. Y., Tlili, A., Lampropoulos, G., Huang, R., Jandrić, P., Zhao, J., Salha, S., Lin, X., Panda, S., Kinshuk, S.L.-P., & Saqr, M. (2024). A systematic review of literature reviews on artificial intelligence in education (AIED): a roadmap to a future research agenda. Smart Learning Environments, 11(1), 59. https://doi.org/10.1186/s40561-024-00350-5
Ray, M. J., Israel, M., Lee, C. E., & Do, V. (2018). A cross-Case analysis of instructional strategies to support participation of K-8 students with disabilities in CS for all. In SIGCSE – Proceedings of the 49th ACM Technical Symposium on Computer Science Education, 2018-January, 900–905. https://doi.org/10.1145/3159450.3159482
Rijke, W. J., Bollen, L., Eysink, T. H. S., & Tolboom, J. L. J. (2018). Computational thinking in primary school: An examination of abstraction and decomposition in different age groups. Informatics in Education, 17(1), 77–92.
Rizvi, S., Waite, J., & Sentance, S. (2023). Artificial Intelligence teaching and learning in K-12 from 2019 to 2022: A systematic literature review. Computers and Education: Artificial Intelligence, 4, 100145. https://doi.org/10.1016/j.caeai.2023.100145
Román-González, M., Pérez-González, J.-C., & Jiménez-Fernández, C. (2017). Which cognitive abilities underlie computational thinking? Criterion validity of the computational thinking test. Computers in Human Behavior, 72, 678–691. https://doi.org/10.1016/j.chb.2016.08.047
Román-González, M., Pérez-González, J.-C., Moreno-León, J., & Robles, G. (2018). Can computational talent be detected? Predictive validity of the computational thinking test. International Journal of Child-Computer Interaction, 18, 47–58. https://doi.org/10.1016/j.ijcci.2018.06.004
Salehi, S., Wang, K. D., Toorawa, R., & Wieman, C. (2020). Can Majoring in Computer Science Improve General Problem-solving Skills?. In Proceedings of the 51st ACM Technical Symposium on Computer Science Education, 156–161. https://doi.org/10.1145/3328778.3366808
Scherer, R., Siddiq, F., & Sánchez Viveros, B. (2019). The cognitive benefits of learning computer programming: A meta-analysis of transfer effects. Journal of Educational Psychology, 111(5), 764–792. https://doi.org/10.1037/edu0000314
Spinthourakis, J. A., Lalor, J., & Berg, W. (Eds.). (2011). Cultural diversity in the classroom: A European comparison (1st edition). VS Verlag für Sozialwissenschaften.
STEM learning. (2022). Science Education in England: Ethnicity, Gender and Disadvantage at GCSE and A level. https://shorturl.at/plyyT
Su, J., & Yang, W. (2023). A systematic review of integrating computational thinking in early childhood education. Computers and Education Open, 4, 100122. https://doi.org/10.1016/j.caeo.2023.100122
Su, J., Zhong, Y., & Ng, D. T. K. (2022). A meta-review of literature on educational approaches for teaching AI at the K-12 levels in the Asia-Pacific region. Computers and Education: Artificial Intelligence, 3, 100065. https://doi.org/10.1016/j.caeai.2022.100065
Sun, L., Hu, L., Yang, W., Zhou, D., & Wang, X. (2021). STEM learning attitude predicts computational thinking skills among primary school students. Journal of Computer Assisted Learning, 37(2), 346–358. https://doi.org/10.1111/jcal.12493
TeachAI (2024). Foundational Policy Ideas for AI in Education. [Online] Available at: https://www.teachai.org/policy
Touretzky, D. S., & Gardner-McCune, C. (2022). Artificial intelligence thinking in K–12. In Computational Thinking Education in K–12: Artificial Intelligence Literacy and Physical Computing, pages 153–180. MIT Press, 2022. https://doi.org/10.7551/mitpress/13375.001.0001
Tsai, M.-J., Liang, J.-C., & Hsu, C.-Y. (2020). The computational thinking scale for computer literacy education. Journal of Educational Computing Research. https://doi.org/10.1177/0735633120972356
UNESCO. (2019). Beijing consensus on artificial intelligence and education. https://unesdoc.unesco.org/ark:/48223/pf0000368303
Upadhyaya, B., McGill, M. M., & Decker, A. (2020). A Longitudinal analysis of K-12 computing education research in the United States: implications and recommendations for change. In Proceedings of the 51st ACM Technical Symposium on Computer Science Education, 605–611. https://doi.org/10.1145/3328778.3366809
Vegas, E., Hansen, M., & Fowler, B. (2021). Building skills for life—How to expand and improve computer science education around the world. Brookings. https://www.brookings.edu/wp-content/uploads/2021/10/Building_skills_for_life.pdf
Vuorikari, R., Kluzer, S. & Punie, Y. (2022). DigComp 2.2: The digital competence framework for citizens – With new examples of knowledge, skills and attitudes, Publications Office of the European Union. https://doi.org/10.2760/115376
Wei, X., Lin, L., Meng, N., Tan, W., & Kong, S. C. (2021). The Effectiveness of partial pair programming on elementary school students’ computational thinking skills and self-efficacy. Computers & Education, 160, 104023. https://doi.org/10.1016/j.compedu.2020.104023
Wu, S.-Y., & Su, Y.-S. (2021). Visual programming environments and computational thinking performance of fifth- and sixth-grade students. Journal of Educational Computing Research. https://doi.org/10.1177/0735633120988807
Yuen, T., Arreguín-Anderson, M., Carmona, G., & Gibson, M. (2016). A culturally relevant pedagogical approach to computer science education to increase participation of underrepresented populations. International Conference on Learning and Teaching in Computing and Engineering (LaTICE), 2016, 147–153. https://doi.org/10.1109/LaTiCE.2016.44
