Introduction to ‘Machine Learning and Human Learning’

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Learning with Technologies and Technologies in Learning

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 456))

Abstract

This introduction to the section on ‘machine learning and human learning’ presents a short overview of machine learning in higher education, including recent developments such as deep learning approaches in the field. It also highlights a need for the further development of human-centered approach to the design of machine learning support tools. Further, it introduces the reader to the three chapters included in this section.

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References

  1. Webb M, Fluck A, Magenheim J, Malyn-Smith J, Waters J, Deschenes M, Zagami J (2021) Machine learning for human learners: opportunities, issues, tensions and threats. Educ Technol Res Dev 69:2109–2130. https://doi.org/10.1007/s11423-020-09858-2

  2. Black P, Wiliam D (2018) Classroom assessment and pedagogy. Assess Educ: Principles Policy Pract 25(6):551–575. https://doi.org/10.1080/0969594X.2018.1441807

    Article  Google Scholar 

  3. Buajang S et al (2021) Multiclass prediction model for student grade prediction using machine learning. IEEE Access 9:95608–95621. https://doi.org/10.1109/ACCESS.2021.3093563

  4. Yang C, Chiang F-K, Cheng Q, Ji J (2021) Machine learning-based student modelling methodology for intelligent tutoring systems. J Educ Comput Res 59(6). https://doi.org/10.1177/0735633120986256

  5. Ball R, Duhadway L, Feuz K, Jensen J, Rague B, Weidman D (2019) Applying machine learning to improve curriculum design. In: SIGCSE’19: proceeding of the 50th ACM technical symposium on computer science education, pp 787–793. https://doi.org/10.1145/3287324.3287430

  6. Hwang G-J, **e H, Wah B, Gasevic D (2020) Vision, challenges, roles and research issues of artificial intelligence in education. Comput Educ: Artif Intel 1:100001. https://doi.org/10.1016/j.caeai.2020.100001

    Article  Google Scholar 

  7. Yin MM, Vaughan J, Wallach H (2019) Understanding the effect of accuracy on trust in machine learning models. In: Proceeding of the 2019 CHI conference on human factors in computing systems, pp 1–12. https://doi.org/10.1145/3290605.3300509

  8. Sarker IH (2021) Machine learning: algorithms, real-world applications and research directions. SN Comput Sci 2:160. https://doi.org/10.1007/s42979-021-00592-x

    Article  Google Scholar 

  9. Cerratto-Pargman T, McGrath C, Viberg O, Kiito K, Knight S, Ferguson R (2021) Responsible learning analytics: creating just, ethical, and caring LA systems. In: LAK 21 companion proceedings. http://su.diva-portal.org/smash/record.jsf?pid=diva2%3A1547574&dswid=6188, https://www.solaresearch.org/wp-content/uploads/2021/04/LAK21_CompanionProceedings.pdf

  10. Rahwan I, Cebrian M, Obradovich N, Bongard J, Bonnefon J-F, Breazeal C et al (2019) Machine behaviour. Nature 568(7753):477–486. https://doi.org/10.1038/s41586-019-1138-y

    Article  Google Scholar 

  11. Viberg O, Grönlund Å (2021) Desperately seeking the impact of learning analytics in education at scale. https://doi.org/10.1201/9781003194620-2. https://www.taylorfrancis.com/chapters/edit/10.1201/9781003194620-2/desperately-seeking-impact-learning-analytics-education-scale-olga-viberg-åke-grönlund

  12. Ochoa X, Wise AF (2021) Supporting the shift to digital with student-centered learning analytics. Educ Tech Res Dev 69(1):357–361. https://doi.org/10.1007/s11423-020-09882-2

    Article  Google Scholar 

  13. Gregory J (2003) Scandinavian approaches to participatory design. Int J Eng Educ 19(1):62–74

    Google Scholar 

  14. Dignum V (2021) The role and challenges of education for responsible AI. Lond Rev Educ 19(1). https://doi.org/10.14324/LRE.19.1.01

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Correspondence to Olga Viberg .

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Viberg, O. (2022). Introduction to ‘Machine Learning and Human Learning’. In: Auer, M.E., Pester, A., May, D. (eds) Learning with Technologies and Technologies in Learning. Lecture Notes in Networks and Systems, vol 456. Springer, Cham. https://doi.org/10.1007/978-3-031-04286-7_23

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