The Effects on Secondary School Students of Applying Experiential Learning to the Conversational AI Learning Curriculum

Authors

  • Ting-Chia Hsu Department of Technology Application and Human Resource Development, National Taiwan Normal University https://orcid.org/0000-0001-6504-9540
  • Hal Abelson Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology
  • Jessica Van Brummelen Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology

DOI:

https://doi.org/10.19173/irrodl.v22i4.5474

Keywords:

gender studies, conversational AI application, experiential learning, block-based programming

Abstract

The purpose of this study was to design a curriculum of artificial intelligence (AI) application for secondary schools. The learning objective of the curriculum was to allow students to learn the application of conversational AI on a block-based programming platform. Moreover, the empirical study actually implemented the curriculum in the formal learning of a secondary school for a period of six weeks. The study evaluated the learning performance of students who were taught with the cycle of experiential learning in one class, while also evaluating the learning performance of students who were taught with the conventional instruction, which was called the cycle of doing projects. Two factors, learning approach and gender, were taken into account. The results showed that females’ learning effectiveness was significantly better than that of males regardless of whether they used experiential learning or the conventional projects approach. Most of the males tended to be distracted from the conversational AI curriculum because they misbehaved during the conversational AI process. In particular, in their performance using the Voice User Interface with the conventional learning approach, the females outperformed the males significantly. The results of two-way ANCOVA revealed a significant interaction between gender and learning approach on computational thinking concepts. Females with the conventional learning approach of doing projects had the best computational thinking concepts in comparison with the other groups.

References

Adya, M., & Kaiser, K. M. (2005). Early determinants of women in the IT workforce: A model of girls’ career choices. Information Technology and People, 18(3), 230–259. https://doi.org/10.1108/09593840510615860

Amazon. (2021). Alexa Developer Console. Retrieved October 20, 2021, from https://developer.amazon.com/alexa/console/ask

Brennan, K., & Resnick, M. (2012). New frameworks for studying and assessing the development of computational thinking. Proceedings of the 2012 Annual Meeting of the American Educational Research Association (pp. 1–25). Vancouver, Canada.

Cetin, I. (2016). Preservice teachers’ introduction to computing: Exploring utilization of Scratch. Journal of Educational Computing Research, 54(7), 997–1021. https://doi.org/10.1177/0735633116642774

Cheng, G. (2019). Exploring factors influencing the acceptance of visual programming environment among boys and girls in primary schools. Computers in Human Behavior, 92, 361–372. https://doi.org/10.1016/j.chb.2018.11.043

Cheryan, S., Ziegler, S. A., Montoya, A. K., & Jiang, L. (2017). Why are some STEM fields more gender balanced than others? Psychological Bulletin, 143(1), 1–35.

Chiu, C. F. (2020). Facilitating K–12 teachers in creating apps by visual programming and project-based learning. International Journal of Emerging Technologies in Learning, 15(1), 103–118.

Efstratia, D. (2014). Experiential education through project based learning. Procedia - Social and Behavioral Sciences, 152, 1256–1260. https://doi.org/10.1016/j.sbspro.2014.09.362

Grover, S., & Pea, R. (2013). Computational thinking in K–12: A review of the state of the field. Educational Researcher, 42(1), 38–43. https://doi.org/10.3102/0013189X12463051

Hsu, T.-C., Abelson, H., Lao, N., Tseng, Y.-H. & Lin, Y.-T. (2021). Behavioral-pattern exploration and development of an instructional tool for young children to learn AI. Computers and Education: Artificial Intelligence, 2, 100012. https://doi.org/10.1016/j.caeai.2021.100012

Hsu, T.-C., Chang, S.-C., & Hung, Y.-T. (2018). How to learn and how to teach computational thinking: Suggestions based on a review of the literature. Computers & Education, 126, 296-310. https://doi.org/10.1016/j.compedu.2018.07.004

Kalelioğlu, F. (2015). A new way of teaching programming skills to K–12 students: Code. org. Computers in Human Behavior, 52, 200–210. https://doi.org/10.1016/j.chb.2015.05.047

Kelleher, C., & Pausch, R. (2005). Lowering the barriers to programming: A taxonomy of programming environments and languages for novice programmers. ACM Computing Surveys, 37(2), 83–137. https://doi.org/10.1145/1089733.1089734

Korkmaz, Ö., & Altun, H. (2013). Engineering and CEIT student’s attitude towards learning computer programming. International Journal of Social Science Studies, 6(2), 1169–1185.

Lindh, J., & Holgersson, T. (2007). Does LEGO training stimulate pupils’ ability to solve logical problems? Computers & Education, 49(4), 1097–1111. https://doi.org/10.1016/j.compedu.2005.12.008

Long, D., & Magerko, B. (2020, April). What is AI literacy? Competencies and design considerations. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–16). Association for Computing Machinery, New York, NY, USA.

Lye, S. Y., & Koh, J. H. L. (2014). Review on teaching and learning of computational thinking through programming: What is next for K–12? Computers in Human Behavior, 41, 51–61. https://doi.org/10.1016/j.chb.2014.09.012

Martin, C. L., Ruble, D. N., & Szkrybalo, J. (2002). Cognitive theories of early gender development. Psychological Bulletin, 128(6), 903–933.

Meyers-Levy, J. (1986). Gender differences in information processing: A selectivity interpretation. Northwestern University.

Meyers-Levy, J. (1989). The influence of a brand name’s association set size and word frequency on brand memory. Journal of Consumer Research, 16(2), 197–207. https://doi.org/10.1086/209208

Özyurt, Ö., & Özyurt, H. (2015). A study for determining computer programming students’ attitudes towards programming and their programming self-efficacy. Journal of Theory and Practice in Education, 11(1), 51–67.

Piwek, P. & Savage, S. (2020). Challenges with learning to program and problem solve: An analysis of student online discussions. The 51st ACM Technical Symposium on Computer Science Education, SIGCSE ’20 (pp. 494–499). Association for Computing Machinery, Portland, OR, USA. https://doi.org/10.1145/3328778.3366838

Pucher, R. & Lehner M. (2011). Project based learning in computer science – A review of more than 500 projects. Procedia - Social and Behavioral Sciences, 29, 1561–1566. https://doi.org/10.1016/j.sbspro.2011.11.398

Putrevu, S. (2001). Exploring the origins and information processing differences between men and women: Implications for advertisers. Academy of Marketing Science Review, 10(1), 1–14.

Sáez-López, J. M., Román-González, M., & Vázquez-Cano, E. (2016). Visual programming languages integrated across the curriculum in elementary school: A two year case study using “Scratch” in five schools. Computers & Education, 97, 129-141. https://doi.org/10.1016/j.compedu.2016.03.003

Sendall, P., Stuetzle, C. S., Kissel, Z. A., Hameed, T. (2019). Experiential learning in the technology disciplines. Proceedings of the 2019 EDSIG Conference (Vol.5, n4968). Information Systems and Computing Academic Professionals.

Touretzky, D., Gardner-McCune, C., Martin, F., & Seehorn, D. (2019, July). Envisioning AI for K-12: What should every child know about AI? Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, pp. 9795–9799). https://doi.org/10.1609/aaai.v33i01.33019795

Van Brummelen, J. (2019). Tools to create and democratize conversational artificial intelligence [Master’s thesis, Massachusetts Institute of Technology]. https://hdl.handle.net/1721.1/122704

Weston, T. J., Dubow, W. M., Kaminsky, A. (2019). Predicting women’s persistence in computer science- and technology-related majors from high school to college. ACM Transactions on Computing Education, 20(1). https://doi.org/10.1145/3343195

Yukselturk, E., & Altiok, S. (2017). An investigation of the effects of programming with Scratch on the preservice IT teachers’ self-efficacy perceptions and attitudes towards computer programming. British Journal of Educational Technology, 48(3), 789–801. https://doi.org/10.1111/bjet.12453

Published

2022-02-01

How to Cite

Hsu, T.-C., Abelson, H., & Van Brummelen, J. (2022). The Effects on Secondary School Students of Applying Experiential Learning to the Conversational AI Learning Curriculum. The International Review of Research in Open and Distributed Learning, 23(1), 82–103. https://doi.org/10.19173/irrodl.v22i4.5474