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Research Article

Learning machine learning with young children: exploring informal settings in an African context

ORCID Icon, , , , &
Pages 161-192 | Received 15 Mar 2022, Accepted 30 Jan 2023, Published online: 07 Feb 2023

References

  • Ackermann, E. K. (2004). Constructing knowledge and transforming the world. In M. Tokoro & L. Steels Eds., A learning zone of one’s own: sharing representations and flow in collaborative learning environments. Part 1. Chapt 2. (pp. 15–37). IOS Press.
  • Actua. (n.d). Bringing AI into the classroom. Retrieved August 8, 2022, fromhttps://www.actua.ca/en/bringing-ai-into-theclassroom/, Dec. 2019.
  • Ainsworth, H. L., & Eaton, S. E. (2010). Formal, non-formal and informal learning in the sciences. Onate Press.
  • Al-Zubidy, A., Carver, J. C., Heckman, S., & Sherriff, M. (2016). A (updated) review of empiricism at the sigcse technical symposium. In Proceedings of the 47th ACM Technical Symposium on Computing Science Education, SIGCSE ’16, (pp. 120–125), New York, NY, USA. ACM. https://doi.org/10.1145/2839509.2844601
  • Aspers, P., & Corte, U. (2019). What is qualitative in qualitative research. Qualitative sociology, 42(2), 139–160. https://doi.org/10.1007/s11133-019-9413-7
  • Aworanti, O. A. (2016). Information and Communications Technology (ICT) in Nigeria educational assessment system–emerging challenges. Universal Journal of Educational Research, 4(6), 1351–1356. https://doi.org/10.13189/ujer.2016.040612
  • Baker, S. T., Le Courtois, S., & Eberhart, J. (2021). Making space for children’s agency with playful learning. International Journal of Early Years Education, 1–13. https://doi.org/10.1080/09669760.2021.1997726
  • Bird, J., & Edwards, S. (2015). Children learning to use technologies through play: A digital play framework. British Journal of Educational Technology, 46(6), 1149–1160. https://doi.org/10.1111/bjet.12191
  • Bulunuz, M. (2013). Teaching science through play in kindergarten: Does integrated play and science instruction build understanding? European Early Childhood Education Research Journal, 21(2), 226–249. https://doi.org/10.1080/1350293X.2013.789195
  • Burgsteiner, H., Kandlhofer, M., & Steinbauer, G. (2016). Irobot: Teaching the Basics of Artificial Intelligence in High Schools. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1). https://doi.org/10.1609/aaai.v30i1.9864
  • Carney, M., Webster, B., Alvarado, I., Phillips, K., Howell, N., Griffith, J., Jongejan, J., Pitaru, A., & Chen, A. (2020). Teachable machine: approachable web-based tool for exploring machine learning classification”. Chi 2020, April 25–30, https://doi.org/10.1145/3334480.3382839
  • Chaudron, S., DiGioia, R., & Gemo, M. (2018). Young children (0-8) and digital technology, a qualitative study across Europe. JRC Science for Policy Report.
  • Danner, R. B., & Pessu, C. O. (2013). A survey of ICT competencies among students in teacher preparation programmes at the University of Benin, Benin City, Nigeria. Journal of Information Technology Education: Research, 12(1), 33–49. https://doi.org/10.28945/1762
  • Dierking, L. D., Falk, J. H., Rennie, L., Anderson, D., & Ellenbogen, K. (2003). Policy statement of the “informal science education” ad hoc committee. Journal of Research in Science Teaching, 40(2), 108–111. https://doi.org/10.1002/tea.10066
  • Druga, S., Vu, S., Likhith, E., & Qiu, T. (2019). Inclusive AI literacy for kids around the world. In: Proceedings of FabLearn 2019. New York, NY, USA, 2019.: ACM (pp. 104–111). https://doi.org/10.1145/3311890.3311904.
  • Dubovi, I., & Tabak, I. (2020). An empirical analysis of knowledge co-construction in YouTube comments. Computers & Education, 156, 103939. https://doi.org/10.1016/j.compedu.2020.103939
  • Durlak, J., & Weissberg, R. (2007). The impact of after-school programs that promote personal and social skills. Collaborative for Academic, Social, and Emotional Learning (NJ1).
  • Dwivedi, U., Gandhi, J., Parikh, R., Coenraad, M., Bonsignore, E., & Kacorri, H. (2021). Exploring machine teaching with children. In 2021 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), St Louis, MO, USA (pp. 1–11). IEEE.
  • Edwards, A. (2005). Let’s get beyond community and practice: The many meanings of learning by participating. The Curriculum Journal, 16(1), 49–65. https://doi.org/10.1080/0958517042000336809
  • Efstathiou, I., Kyza, E. A., & Georgiou, Y. (2018). An inquiry-based augmented reality mobile learning approach to fostering primary school students’ historical reasoning in non-formal settings. Interactive Learning Environments, 26(1), 22–41. https://doi.org/10.1080/10494820.2016.1276076
  • Egede, B. A. J., & Asabor, B. M. (2019). Integration of technology in the training of basic education teachers in Nigeria: Issues, challenges and prospects. ADECT 2019 Proceedings, Abuja FCT, Nigeria.
  • Elo, S., & Kyngäs, H. (2008). The qualitative content analysis process. Journal of Advanced Nursing, 62(1), 107–115. https://doi.org/10.1111/j.1365-2648.2007.04569.x
  • Federal Ministry of Education. (2019). National Policy on ICT in Education. Retrieved form https://education.gov.ng/wp-content/uploads/2019/08/national-policy-on-ict-in-education-2019.pdf
  • Federal Ministry of Education, FME (2019). National Policy on ICT in Education. Retrieved form https://education.gov.ng/wp-content/uploads/2019/08/national-policy-on-ict-in-education-2019.pdf
  • Gallivan, M. J., & Srite, M. (2005). Information technology and culture: Merging fragmented and holistic perspectives of culture. Information and Organizations, 15(2005), 295–338. https://doi.org/10.1016/j.infoandorg.2005.02.005
  • Google. (2020). Google teachable machine. https://teachablemachine.withgoogle.com/
  • Gwagwa, A., Kachidza, P., Siminyu, K., & Smith, M. (2021). Responsible artificial intelligence in sub-saharan Africa: Landscape and general state of play. AI4D Africa.
  • Han, X., Hu, F., Xiong, G., Liu, X., Gong, X., Niu, X., Shi, W., & Wang, X., 2018. Design of AI + curriculum for primary and secondary schools in Qingdao. In 2018 Chinese Automation Congress (CAC), (pp. 4135–4140). https://doi.org/10.1109/CAC.2018.8623310
  • Hedges, H., & Cullen, J. (2012). Participatory learning theories: A framework for early childhood pedagogy. Early Child Development and Care, 182(7), 921–940. https://doi.org/10.1080/03004430.2011.597504
  • Hitron, T., Orlev, Y., Wald, I., Shamir, A., Erel, H., & Zuckerman, O. (2019). Can children understand machine learning concepts? the effect of uncovering black boxes. CHI 2019, May 4–9, 2019, Glasgow, Scotland, UK. https://doi.org/10.1145/3290605.3300645
  • Hitron, T., Wald, I., Erel, H., & Zuckerman, O. (2018). Introducing children to machine learning concepts through hands-on experience. In Proceedings of the 17th ACM Conference on Interaction Design and Children. ACM, (pp. 563–568). https://doi.org/10.1145/3202185.3210776
  • Ho, J. W., & Scadding, M. (2019). Classroom Activities for Teaching Artificial Intelligence to Primary School Students. CoolThink@ JC, 157. In S. C. Kong & D. Andone, G. Biswas, H. U. Hoppe, T. C. Hsu, R. H. Huang, B. C. Kuo, K. Y. Li, C. K. Looi, M. Milrad, J. Sheldon, J. L. Shih, K. F. Sin, K. S. Song, and J. Vahrenhold (Eds.). Proceedings of International Conference on Computational Thinking Education 2019. Hong Kong: The Education University of Hong Kong.
  • Hubber, P. J., Outhwaite, L. A., Chigeda, A., McGrath, S., Hodgen, J., & Pitchford, N. J. (2016). Should touch screen tablets be used to improve educational outcomes in primary school children in developing countries? Frontiers in Psychology, 7, 839. https://doi.org/10.3389/fpsyg.2016.00839
  • Isaac, O. A., Amana, Y. H., Christian, N. C., & Adewale, O. A. (2018). Computer science education in Nigeria secondary schools–gap between policy pronouncement and implementation. International Journal of Engineering Research and Technology, 7(4), 463–466. https://doi.org/10.17577/IJERTV7IS040350
  • Jackson, L. A., & Wang, J. L. (2013). Cultural differences in social networking site use: A comparative study of China and the United States. Computers in Human Behavior, 29(3), 910–921. https://doi.org/10.1016/j.chb.2012.11.024
  • Kafai, Y. B., & Harel, I. (1991). Learning through design and teaching: Exploring social and collaborative aspects of constructionism. In Constructionism (pp. 111–140). Ablex. https://doi.org/10.1145/182987.383882
  • Kafai, Y., Proctor, C., & Lui, D. (2020). From theory bias to theory dialogue: Embracing cognitive, situated, and critical framings of computational thinking in K-12 CS education. ACM Inroads, 11(1), 44–53.
  • Kangas, M. (2010). Creative and playful learning: Learning through game co-creation and games in a playful learning environment. Thinking Skills and Creativity, 5(1), 1e15. https://doi.org/10.1016/j.tsc.2009.11.001
  • Kangas, M., Siklander, P., Randolph, J., & Ruokamo, H. (2017). Teachers’ engagement and students’ satisfaction with a playful learning environment. Teaching and Teacher Education, 63, 274–284. https://doi.org/10.1016/j.tate.2016.12.018
  • Kumpulainen, K., & Lipponen, L. (2012). Crossing boundaries: Harnessing funds of knowledge in dialogic inquiry across formal and informal learning environments. In P. Jarvis & M. Watts (Eds.), The Routledge international handbook of learning (pp. 112–125). Routledge.
  • Lee, I., Ali, S., Zhang, H., DiPaola, D., & Breazeal, C. (2021). Developing middle school students’ AI literacy. In Proceedings of the 52nd ACM technical symposium on computer science education, Virtual Event, USA (pp. 191–197).
  • Lee, S. M., & Chun, S. J. (2021). The effect of AI experience program using teachable machine on AI perception of elementary school students. Journal of the Korean Association of Information Education, 25(4), 611–619. https://doi.org/10.14352/jkaie.2021.25.4.611
  • Lee, S. G., Trimi, S., & Kim, C. (2013). The impact of cultural differences on technology adoption. Journal of World Business, 48(1), 20–29. https://doi.org/10.1016/j.jwb.2012.06.003
  • Lin, P., Van Brummelen, J., Lukin, G., Williams, R., & Breazeal, C. (2020). Zhorai: Designing a conversational agent for children to explore machine learning concepts. Proceedings of the AAAI Conference on Artificial Intelligence, 34(09), 13381–13388. https://doi.org/10.1609/aaai.v34i09.7061
  • Long, D., Blunt, T., & Magerko, B. (2021). Co-Designing AI literacy exhibits for informal learning spaces. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW2), 1–35. https://doi.org/10.1145/3476034
  • Mahipal, V., Ghosh, S., Sanusi, I. T., Ma, R., Gonzales, J. E., & Martin, F. G. (2023, January). DoodleIt: A Novel Tool and Approach for Teaching How CNNs Perform Image Recognition. In Proceedings of the 25th Australasian Computing Education Conference (pp. 31-38). Melbourne, VIC, Australia
  • Ma, R., Sanusi, I. T., Mahipal, V., Gonzales, J., & Martin, F. (2023). Developing machine learning algorithm literacy with novel plugged and unplugged approaches. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1 (Accepted), Toronto, ON, Canada.
  • Mertala, P. (2019). Young children’s conceptions of computers, code, and the Internet. International Journal of Child-Computer Interaction, 19, 56–66. https://doi.org/10.1016/j.ijcci.2018.11.003
  • Michelle, C., Barron, W., Irene, A., Kyle, P., Noura, H., Jordan, G., & Alexander, C. (2020). Teachable Machine: ApproachableWeb-based tool for exploring machine learning classification. ACM. https://doi.org/10.1145//3334480.3382839
  • Morris, D., & Fiebrink, R. (2013). Using machine learning to support pedagogy in the arts. Personal and ubiquitous computing, 17(8), 1631–1635. https://doi.org/10.1007/s00779-012-0526-1
  • Narahara, T., & Kobayashi, Y. (2018). Personalizing homemade bots with plug and play AI for STEAM Education. In Proceedings of ACM In SIGGRAPH Asia 2018 Technical Briefs, 2018, Tokyo, Japan. ACM, New York, NY, USA, 4 . https://doi.org/10.1145/3283254.3283270
  • NHREC. (2016). Policy Statement Regarding Enrollment of Children in Nigeria. Retrieved January 13, 2023, fromhttp://nhrec.net/nhrec/Final%20NHREC%20Policy%20Statement%20on%20Enrollment%20of%20Children%20in%20Research.pdf
  • Ogundile, O. P., Bishop, S. A., Okagbue, H. I., Ogunniyi, P. O., & Olanrewaju, A. M. (2019). Factors Influencing ICT adoption in some selected secondary schools in Ogun State, Nigeria. International Journal of Emerging Technologies in Learning, 14(10), 62. https://doi.org/10.3991/ijet.v14i10.10095
  • Ololube, N. P., Onyekwere, L. A., & Agbor, C. N. (2016). Effectively managing inclusive and equitable quality education to promote lifelong learning opportunities (LLO) for all. Journal of Global Research in Education and Social Science, 8(4), 179–195.
  • Oyelere, S. S., Suhonen, J., & Sutinen, E. (2016). M-learning: A new paradigm of learning ICT in Nigeria. International Journal of Interactive Mobile Technologies, 10(1), 35. https://doi.org/10.3991/ijim.v10i1.4872
  • Papert, S. (1980). Mindstorms—children, computers and powerful ideas. Basic Books, Inc
  • Payne, B. H. 2019. An ethics of artificial intelligence curriculum for middle school students. MIT Media Lab—AI Educ. Accessed December, 29, 2021. https://aieducation.mit.edu/aiethics.html
  • Phamduy, P., DeBellis, M., & Porfiri, M. (2015). Controlling a robotic fish via a natural user interface for informal science education. IEEE Transactions on Multimedia, 17(12), 2328–2337. https://doi.org/10.1109/TMM.2015.2480226
  • Randolph, J. J., & Bednarik, R. (2008). Publication bias in the computer science education research literature. Journal of Universal Computer Science, 14(4), 575–589.
  • Rogoff, B. (1990). Apprenticeship in Thinking: Cognitive Development in Social Context. Oxford University Press, New York, 249(4969). https://doi.org/10.5860/choice.28-0612
  • Sanusi, I. T. (2021a). Intercontinental evidence on learners’ differentials in sense-making of machine learning in schools. In 21st Koli Calling International Conference on Computing Education Research, Joensuu, Finland (pp. 1–2).
  • Sanusi, I. T. (2021b). Teaching machine learning in K-12 Education. In Proceedings of the 17th ACM Conference on International Computing Education Research (ICER 2021), August 16–19, 2021, Virtual Event, USA. ACM, New York, NY, USA, 3 . https://doi.org/10.1145/3446871.3469769
  • Sanusi, I. T., Olaleye, S. A., Oyelere, S. S., & Dixon, R. A. (2022). Investigating learners’ competencies for artificial intelligence education in an African K-12 setting. Computers and Education Open, 3, 100083. https://doi.org/10.1016/j.caeo.2022.100083
  • Sanusi, I. T., & Oyelere, S. S. (2020). Pedagogies of machine learning in K-12 Context. In 2020 IEEE Frontiers in Education Conference (FIE) (pp. 1–8). IEEE. https://doi.org/10.1109/FIE44824.2020.9274129
  • Sanusi, I. T., Oyelere, S. S., Agbo, F. J., & Suhonen, J. (2021). Survey of resources for introducing machine learning in K-12 context. In 2021 IEEE Frontiers in Education Conference (FIE), Lincoln, NE, USA (pp. 1–9). IEEE.
  • Sanusi, I. T., Oyelere, S. S., & Omidiora, J. O. (2022). Exploring teachers’ preconceptions of teaching machine learning in high school: A preliminary insight from Africa. Computers and Education Open, 3, 100072. https://doi.org/10.1016/j.caeo.2021.100072
  • Sanusi, I. T., Oyelere, S. S., Suhonen, J., Olaleye, S. A., & Otunla, A. O. (2017). Exploring students and teachers’ activities, experiences and impact of Opón ìmò mobile learning device on teaching and learning. In 2017 IEEE AFRICON (pp. 788–793). IEEE. https://doi.org/10.1109/AFRCON.2017.8095583
  • Sanusi, I. T., Oyelere, S. S., Vartiainen, H., Suhonen, J., & Tukiainen, M. (2022). A systematic review of teaching and learning machine learning in K-12 education. Education and Information Technologies, 1–31. https://doi.org/10.1007/s10639-022-11416-7
  • Scheidt, A., & Pulver, T. (2019). Any-cubes: A children’s toy for learning AI: Enhanced play with deep learning and MQTT. In Proceedings of Mensch und Computer 2019. (pp. 893–895). https://doi.org/10.1145/3340764.3345375
  • Schoultz, J., Säljö, R., & Wyndhamn, J. (2001). Heavenly talk: Discourse, artifacts, and children’s understanding of elementary astronomy. Human development, 44(2–3), 103–118. https://doi.org/10.1159/000057050
  • Sfard, A. (1998). On two metaphors for learning and the danger of choosing just one. Educational Researcher, 27(2), 4–13. https://doi.org/10.3102/0013189X027002004
  • Sperling, A., & Lickerman, D. (2012). Integrating AI and Machine Learning in Software Engineering Course for High School Students. ITiCSE’12, July 3–5, 2012, Haifa, Israel.
  • Stromholt, S., & Bell, P. (2018). Designing for expansive science learning and identification across settings. Cultural Studies of Science Education, 13(4), 1015–1047. https://doi.org/10.1007/s11422-017-9813-5
  • Suleiman, Y., Abubakar, Y. A., & Akanbi, I. M. (2019). Addressing the factors responsible for schooling without learning in primary and secondary schools in Nigeria. International Journal of Synergy and Research, 7, 161–178. https://doi.org/10.17951/ijsr.2018.7.0.161-178
  • Tedre, M., Vartiainen, H., Kahila, J., Toivonen, T., Jormanainen, I., & Valtonen, T. (2020). Machine learning introduces new perspectives to data agency in K—12 computing education. In 2020 IEEE Frontiers in Education Conference (FIE) (pp. 1–8). IEEE.
  • Toivonen, T., Jormanainen, I., Kahila, J., Tedre, M., Valtonen, T., & Vartiainen, H. (2020). Co-designing machine learning apps in K–12 with primary school children. In 2020 IEEE 20th International Conference on Advanced Learning Technologies (ICALT) (pp. 308–310). IEEE. https://doi.org/10.1109/ICALT49669.2020.00099
  • UBEC. (2020). Universal Basic Education Commission (UBEC) Who we are. Retrieved April 2, 2021, from https://www.ubec.gov.ng/about/who-we-are/
  • UNICEF. (2021). UNICEF warns of Nigerian education crisis as world celebrates International Day of Education amid COVID-19 concerns. Retrieved September 5 2022, from https://www.unicef.org/nigeria/press-releases/unicef-warns-nigerian-education-crisis-world-celebrates-international-day-education
  • UNICEF Nigeria. (2020). The Challenge - One in every five of the world’s out-of-school children is in Nigeria. Retrieved February 8, 2021, from https://www.unicef.org/nigeria/education#:~:text=One%20in%20every%20five%20of,years%20are%20not%20in%20school.
  • Vance, F. (2010). A comparative analysis of competency frameworks for youth workers in the out-of-school time field. In Child and youth care forum (Vol. 39, No. 6, pp. 421–441). Springer US. https://doi.org/10.1007/s10566-010-9116-4
  • Van Niekerk, K., Dada, S., & Tönsing, K. (2019). Influences on selection of assistive technology for young children in South Africa: Perspectives from rehabilitation professionals. Disability and rehabilitation, 14(8), 912–925. https://doi.org/10.1080/09638288.2017.1416500
  • Vartiainen, H., Tedre, M., & Valtonen, T. (2020). Learning machine learning with very young children: Who is teaching whom? International Journal of Child-Computer Interaction, 25, 100182. https://doi.org/10.1016/j.ijcci.2020.100182
  • Vartiainen, H., Toivonen, T., Jormanainen, I., Kahila, J., Tedre, M., & Valtonen, T. (2020). Machine learning for middle-schoolers: Children as designers of machine-learning apps, IEEE Frontiers in Education Conference (FIE) Uppsala, Sweden (pp. 1–9). https://doi.org/10.1109/FIE44824.2020.9273981
  • Vartiainen, H., Toivonen, T., Jormanainen, I., Kahila, J., Tedre, M., & Valtonen, T. (2021). Machine learning for middle schoolers: Learning through data-driven design. International Journal of Child-Computer Interaction, 29, 100281. https://doi.org/10.1016/j.ijcci.2021.100281
  • Vygotsky, L. (1978). Mind in society. the development of higher psychological processes. Harvard University Press. https://doi.org/10.2307/j.ctvjf9vz4
  • Weisberg, D. S., Kittredge, A. K., Hirsh-Pasek, K., Golinkoff, R. M., & Klahr, D. (2015). Making play work for education. Phi Delta Kappan, 96(8), 8–13. https://doi.org/10.1177/0031721715583955
  • Wertsch, J. V. (1998). Mind as action. Oxford University Press.
  • Xia, Q., Chiu, T. K., Lee, M., Sanusi, I. T., Dai, Y., & Chai, C. S. (2022). A self-determination theory (SDT) design approach for inclusive and diverse artificial intelligence (AI) education. Computers & Education, 189, 104582. https://doi.org/10.1016/j.compedu.2022.104582
  • Yoo, S. J., & Huang, W. H. D. (2011). Comparison of Web 2.0 technology acceptance level based on cultural differences. Educational Technology and Society, 14(4), 241–252.
  • Zhou, X., Van Brummelen, J., & Lin, P. (2020). Designing AI learning experiences for K-12: Emerging works, future opportunities and a design framework. arXiv preprint arXiv:2009.10228.