What Can AI Learn from Teachers and Students? A Contribution to Build the Research Gap Between AI Technologies and Pedagogical Knowledge



Abstract Views
831

Downloads
493

Citations

Share

##plugins.themes.bootstrap3.article.sidebar##

Submitted Nov 4, 2022
Published Dec 9, 2022
10.24018/ejedu.2022.3.6.509

Abstract viewed = 831 times

Table of Contents

##plugins.themes.bootstrap3.article.main##

Artificial Intelligence and related technologies represent a major advance in the human capacity to produce knowledge from different areas of knowledge. The application of these technologies in repetitive human activities that can be learned by a machine is already a constant in society, but their use in education still needs research, especially pedagogical research, which can make it clear how AI can contribute effectively to teaching and learning processes, since these processes are marked not only by cognitive characteristics, but also by cultural and emotional aspects. Having identified this gap, we conducted a qualitative study with students and teachers from four EU countries in order to find out what they know about the use of technologies and AI in education, what are their concrete needs and the recommendations of teachers on the pedagogical use of AI in education. This is a contribution to the gap identified by other authors in research on AI and education. This study gives voice to the participants and addresses the issue from the perspective of education. The results point to (1) A knowledge of the topic only from the perspective of users, (2) High expectations of the impact of AI on education (3) Recommendations of adapting AI to learning purposes, (4) Attention to guarantees of inclusion, citizenship, and democracy.

Keywords: artificial intelligence, education, pedagogy, 21st century skills.

References

  1. Ahmad, K., Qadir, J., Al-Fuqaha, A., Iqbal, W., El-Hassan, A., Benhaddou, D., & Ayyash, M. (2020). Artificial intelligence in education: a panoramic review. Preprint. https://doi.org/10.35542/osf.io/zvu2n.
     Google Scholar
  2. Alraimi, K. M., Zo, H., & Ciganek, A. P. (2015). Understanding the MOOCs continuance: the role of openness and reputation. Computers and Education, 80, 28–38.
     Google Scholar
  3. Baker, R. S. (2016). Stupid tutoring systems, intelligent humans. International Journal of Artificial Intelligence in Education, 26(2), 600–614.
     Google Scholar
  4. Barak, M. (2017). Science teacher education in the twenty-first century: A pedagogical framework for technology-integrated social constructivism. Research in Science Education, 47(2), 283–303.
     Google Scholar
  5. Brinkmann, S., & Kvale, S. (2015). InterViews: Learning the craft of qualitative research interviewing (3rd ed.). Thousand Oaks, CA: Sage.
     Google Scholar
  6. Buchanan, E. A. and Zimmer, M. (2012) Internet research ethics. Stanford Encyclopedia of Philosophy. Available from: http://plato.stanford.edu/entries/ethics-internet-research. [Accessed 4 April 2016].
     Google Scholar
  7. Cambria, E., & White, B. (2014). Jumping NLP curves: A review of natural language processing research. IEEE Computational intelligence magazine, 9(2), 48–57.
     Google Scholar
  8. Canário, R. (1992). Escolas e Mudança: da Lógica da Reforma à Lógica da Inovação. In A. Estrela & M. Falcão [II AIPELF/AFIRSE National Colloquium - Curriculum Reform in Portugal and in the European Community Countries]. (Org.). II Colóquio Nacional da AIPELF/AFIRSE-A Reforma Curricular em Portugal e nos Países da Comunidade Europeia (pp. 195-220). Lisboa: FPCE.
     Google Scholar
  9. Cetintas, S., Si, L., Xin, Y. P. P., & Hord, C. (2009). Automatic detection of off-task behaviors in intelligent tutoring systems with machine learning techniques. IEEE Transactions on Learning Technologies, 3(3), 228–236.
     Google Scholar
  10. Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: a review. Ieee Access, 8, 75264–75278.
     Google Scholar
  11. Chen, X., Xie, H., Zou, D., & Hwang, G. J. (2020). Application and theory gaps during the rise of Artificial Intelligence in Education. Computers and Education: Artificial Intelligence, 1, 100002.
     Google Scholar
  12. Cohen, L., Manion, L., & Morrison, K. (2018). Research Methods in Education. (8th edition). London, Routledge.
     Google Scholar
  13. Davies, R. S., Dean, D. L., & Ball, N. (2013). Flipping the classroom and instructional technology integration in a college-level information systems spreadsheet course. Educational Technology Research and Development, 61(4), 563–580.
     Google Scholar
  14. Delen, D. (2010). A comparative analysis of machine learning techniques for student retention management. Decision Support Systems, 49(4), 498–506.
     Google Scholar
  15. Ding, R. X., Palomares, I., Wang, X., Yang, G. R., Liu, B., Dong, Y., ... & Herrera, F. (2020). Large-Scale decision-making: Characterization, taxonomy, challenges and future directions from an Artificial Intelligence and applications perspective. Information Fusion, 59, 84–102.
     Google Scholar
  16. Earle, R. S. (2002). The integration of instructional technology into public education: Promises and challenges. Educational technology, 42(1), 5–13.
     Google Scholar
  17. Eriksson, T., Adawi, T., & Stöhr, C. (2017). “Time is the bottleneck”: a qualitative study exploring why learners drop out of MOOCs. Journal of Computing in Higher Education, 29(1), 133–146.
     Google Scholar
  18. Escudero Muñoz, J. (1988). La Inovación y la Organización Escolar. In R. Pascual [Educational Management, in the face of Innovation and Change]. (Org.). La Gestión Educativa, ante la Innovación y el Cambio (pp. 84-99). Madrid: Ed. Narcea.
     Google Scholar
  19. Farrimond, H. (2013). Doing Ethical Research. Palgrave Macmillan.
     Google Scholar
  20. Félix Angulo, J. (1994). Innovación, Cambio y Reforma: Algunas Ideas para Analizar lo que está Ocurriendo [Innovation, Change and Reform: Some Ideas to Analyze What is Happening]. In F. Angulo & N. Blanco (Coord.). Teoría y Desarrollo del Currículo (pp. 357-368). Málaga: Ed. Aljibe.
     Google Scholar
  21. Flick, U. (2009). An Introduction to Qualitative Research (4th edition). Sage.
     Google Scholar
  22. Forbus, K. D., & Feltovich, P. J. (2001). Smart machines in education: The coming revolution in educational technology. The MIT Press.
     Google Scholar
  23. Gonzales, L., Brown, M. S. and Slate, J. R. (2008) Teachers who left the teaching professions: a qualitative understanding. The Qualitative Report, 13 (1),1–11.
     Google Scholar
  24. González González, M. & Escudero Muñoz, J. (1987). Innovación educativa: teorías y procesos de Desarrollo [Educational innovation: development theories and processes]. Barcelona: Editorial Humanitas.
     Google Scholar
  25. Greene, J. A., Oswald, C. A., & Pomerantz, J. (2015). Predictors of Retention and Achievement in A Massive Open Online Course. American Educational Research Journal, 52, 925–955. https://doi.org/10.3102/0002831215584621.
     Google Scholar
  26. Hew, K. F. (2016). Promoting engagement in online courses: What strategies can we learn from three highly rated MOOCS. British Journal of Educational Technology, 47(2), 320–341.
     Google Scholar
  27. Hone, K. S., & El Said, G. R. (2016). Exploring the factors affecting MOOC retention: A survey study. Computers & Education, 98, 157–168.
     Google Scholar
  28. Issroff, K., & Scanlon, E. (2002). Educational technology: The influence of theory. Journal of Interactive Media in Education, 6, 1–13.
     Google Scholar
  29. Istance, D., & Kools, M. (2013). OECD work on technology and education: Innovative learning environments as an integrating framework. European Journal of Education, 48(1), 43–57.
     Google Scholar
  30. Jarnac de Freitas, M., & Mira da Silva, M. (2020). Systematic literature review about gamification in MOOCs. Open Learning: The Journal of Open, Distance and e-Learning, 35(1), 1–23. https://doi.org/10.1080/02680513.2020.1798221.
     Google Scholar
  31. Koehler, M. J., & Mishra, P. (2005). What happens when teachers design educational technology? The development of technological pedagogical content knowledge. Journal of educational computing research, 32(2), 131–152.
     Google Scholar
  32. Kovacs, H. (2017). Learning and Teaching in Innovation: why it is important for education in the 21st century. Neveléstudomány, (2), 45–60. http://doi.org/10.21549/NTNY.18.2017.2.4.
     Google Scholar
  33. Lefever, S., Dal, M., & Matthiasdottir, A. (2007). Online data collection in academic research: advantages and limitations. British Journal of Educational Technology, 38(4), 574–582.
     Google Scholar
  34. Likert, R. (1932) A Technique for the Measurement of Attitudes. Columbia University Pres.
     Google Scholar
  35. Littenberg-Tobias, J., & Reich, J. (2020). Evaluating access, quality, and equity in online learning: A case study of a MOOC-based blended professional degree program. The Internet and Higher Education, 47, 100759.
     Google Scholar
  36. Liu, Z. Y., Lomovtseva, N., & Korobeynikova, E. (2020). Online learning platforms: Reconstructing modern higher education. International Journal of Emerging Technologies in Learning (iJET), 15(13), 4–21.
     Google Scholar
  37. Luckin, R., & Cukurova, M. (2019). Designing educational technologies in the age of AI: A learning sciences‐driven approach. British Journal of Educational Technology, 50(6), 2824–2838.
     Google Scholar
  38. Liyanagunawardena, T., Williams, S., & Adams, A. (2013). The impact andreach of MOOCs: A developing countries’ perspective. eLearningPapers. http://centaur.reading.ac.uk/32452/.
     Google Scholar
  39. McArthur, D., Lewis, M., & Bishary, M. (2005). The roles of artificial intelligence in education: current progress and future prospects. Journal of Educational Technology, 1(4), 42–80.
     Google Scholar
  40. McAuley, A., Stewart, B., Siemens, G., & Cormier, D. (2010). The MOOC model for digital practice.
     Google Scholar
  41. Muldner, K., Burleson, W., Van de Sande, B., & VanLehn, K. (2011). An analysis of students’ gaming behaviors in an intelligent tutoring system: Predictors and impacts. User modeling and user-adapted interaction, 21(1-2), 99–135.
     Google Scholar
  42. Niess, M. L. (2005). Preparing teachers to teach science and mathematics with technology: Developing a technology pedagogical content knowledge. Teaching and teacher education, 21(5), 509–523.
     Google Scholar
  43. Nye, B. D., Graesser, A. C., & Hu, X. (2014). AutoTutor and family: A review of 17 years of natural language tutoring. International Journal of Artificial Intelligence in Education, 24(4), 427–469.
     Google Scholar
  44. O’Sullivan, D., & Dooley, L. (2009). Applying Innovation. Thousand Oaks: Sage. http://doi.org/10.4135/9781452274898.
     Google Scholar
  45. Paviotti, G., Rossi, P. G., & Zarka, D. (2012). Intelligent tutoring systems: an overview. Pensa Multimedia.
     Google Scholar
  46. Phobun, P., & Vicheanpanya, J. (2010). Adaptive intelligent tutoring systems for e-learning systems. Procedia-Social and Behavioral Sciences, 2(2), 4064–4069.
     Google Scholar
  47. Raffaghelli, J. E., Cucchiara, S., & Persico, D. (2015). Methodological approaches in MOOC research: retracing the myth of proteus. British Journal of Educational Technology, 46(3), 488–509.
     Google Scholar
  48. Rodriguez, C. O. (2012). MOOCs and the AI-Stanford Like Courses: Two Successful and Distinct Course Formats for Massive Open Online Courses. European Journal of Open, Distance and E-Learning. https://olj.onlinelearningconsortium.org/index.php/olj/article/view/889.
     Google Scholar
  49. Rogers, E. (2003). Diffusion of Innovations. New York: Free Press.
     Google Scholar
  50. Salmon, G. (2005). Flying not flapping: a strategic framework for e-learning and pedagogical innovation in higher education institutions. ALT-J, 13(3), 201–218.
     Google Scholar
  51. Stone, P., Brooks, R., Brynjolfsson, E., Calo, R., Etzioni, O., Hager, G., ... & Teller, A. (2016). One-hundred-year study on artificial intelligence (AI100). Stanford University. https://ai100. stanford. edu.
     Google Scholar
  52. Thomaz, A. L., & Breazeal, C. (2008). Teachable robots: Understanding human teaching. Transactions on Learning Technologies, 3(3), 228–236.
     Google Scholar
  53. Warren, C. A. B. (2002). Qualitative interviewing. In J. F. Gubrium & J. A. Holstein (Eds.), Handbook of interview research: Context and method (pp. 83–101). Thousand Oaks: Sage.
     Google Scholar
  54. Yang, Z., & Liu, Q. (2007). Research and development of web-based virtual online classroom. Computers & education, 48(2), 171–184.
     Google Scholar
  55. Zheng, S., Rosson, M. B., Shih, P. C., & Carroll, J. M. (2015). Understanding student motivation, behaviors, and perceptions in MOOCs. In Proceedings of the 18th ACM conference on computer supported cooperative work & social computing (pp. 1882-1895).
     Google Scholar