Skip to main content

Advertisement

SpringerLink
Log in

Personalisation in STE(A)M education: a review of literature from 2011 to 2020

  • Published:
Journal of Computing in Higher Education Aims and scope Submit manuscript

Abstract

This paper reports a comprehensive review of literature on personalised learning in STEM and STEAM (or STE(A)M) education, which involves the disciplinary integration of Science, Technology, Engineering, and Mathematics, as well as Arts. The review covered the contexts of STE(A)M education where personalised learning was adopted, the objectives of personalised learning and research issues, and various aspects of practising personalisation. A total of 72 publications from 2011 to 2020 were collected from Scopus for review. The results reveal the widespread studies in this area across various countries/territories, levels of education, subject disciplines, and modes of education. The most common objective of personalised learning lied in catering for learning styles. The issue most frequently addressed focused on evaluating the effectiveness of technologies for personalised learning. Blended learning and learning analytics are the most popular means to achieve personalised learning. Among various aspects of learning, learning material is the one most frequently addressed. Also, the factors and criteria for personalising learning were summarised, which reveal the heterogeneous nature of learners who have their own learning ability, interest, style and progress. The results suggest more research on interdisciplinary and integrative approaches for STE(A)M learning to examine how personalisation can be applied effectively, as well as more investigation on integrating personalisation with the pedagogies and elements commonly introduced to STE(A)M education.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Alamri, H., Lowell, V., Watson, W., & Watson, S. L. (2020). Using personalized learning as an instructional approach to motivate learners in online higher education: Learner self-determination and intrinsic motivation. Journal of Research on Technology in Education, 52(3), 322–352

    Article  Google Scholar 

  • Balakrishnan, B. (2018). Motivating engineering students learning via monitoring in personalized learning environment with tagging system. Computer Applications in Engineering Education, 26(3), 700–710

    Article  Google Scholar 

  • Bozkurt, A., Ucar, H., Durak, G., & Idin, S. (2019). The current state of the art in STEM research: A systematic review study. Cypriot Journal of Educational Science, 14(3), 374–383

    Article  Google Scholar 

  • Charlton, P., Preston, A., Karagiannidis, C., Kouroupetroglou, C., Grenon, M., Handschuh, S., & Koumpis, A. (2016). Use of the curiosity cabinet metaphor to support inclusive learning. Proceedings of the 7th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion (pp. 309–314), Vila Real, Portugal

  • Clark, R. M., & Kaw, A. (2020). Adaptive learning in a numerical methods course for engineers: Evaluation in blended and flipped classrooms. Computer Applications in Engineering Education, 28(1), 62–79

    Article  Google Scholar 

  • DeMink-Carthew, J., Olofson, M. W., LeGeros, L., Netcoh, S., & Hennessey, S. (2017). An analysis of approaches to goal setting in middle grades personalized learning environments. RMLE Online, 40(10), 1–11

    Article  Google Scholar 

  • Deng, Y., Lu, D., Chung, C. J., Huang, D., & Zeng, Z. (2018). Personalized learning in a virtual hands-on lab platform for computer science education. In 2018 IEEE Frontiers in Education Conference (FIE) (pp. 1–8). IEEE

  • Education Elements. (2017). 2016–2017 impact report: Building capacity for personalized learning and more. https://www.edelements.com/resources-old

  • Gao, X., Li, P., Shen, J., & Sun, H. (2020). Reviewing assessment of student learning in interdisciplinary STEM education. International Journal of STEM Education, 7, article 24. https://doi.org/10.1186/s40594-020-00225-4

  • Ghergulescu, I., Moldovan, A. N., Muntean, C. H., & Muntean, G. M. (2020). Evaluation of an interactive personalised virtual lab in secondary schools. In: Lane, H.C., Zvacek, S., & Uhomoibhi, J. (eds) Computer Supported Education, Communications in Computer and Information Science, 1220. Springer, Cham. https://doi.org/10.1007/978-3-030-58459-7_26

  • Gillet, D., de Jong, T., Sotirou, S., & Salzmann, C. (2013). Personalised learning spaces and federated online labs for STEM Education at School. Proceedings of 2013 IEEE Global Engineering Education Conference (pp. 769–773), Berlin

  • Grant, M. M. (2015). Using mobile devices to support formal, informal and semi-formal learning. In X. Ge, D. Ifenthaler, & J. M. Spector (Eds.), Emerging Technologies for STEAM Education (pp. 157–177). Springer

  • Groff, J. (2017). Personalized learning: The state of the field & future directions. Center for Curriculum Redesign. https://www.media.mit.edu/publications/personalized-learning/

  • Hogan, J., & Down, B. (2015). A STEAM school using the Big Picture Education (BPE) design for learning and school–what an innovative STEM Education might look like. International Journal of Innovation in Science and Mathematics Education, 23(3), 47–60

    Google Scholar 

  • Høgheim, S., & Reber, R. (2015). Supporting interest of middle school students in mathematics through context personalization and example choice. Contemporary Educational Psychology, 42, 17–25

    Article  Google Scholar 

  • Hurson, A. R., Sedigh, S., Miller, L., & Shirazi, B. (2011). Enriching STEM education through personalization and teaching collaboration. Proceedings of 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (pp. 543–549), Seattle

  • Jayarajah, K., Saat, R. M., & Rauf, R. A. A. (2014). A review of Science, Technology, Engineering & Mathematics (STEM) education research from 1999–2013: A Malaysian perspective. Eurasia Journal of Mathematics Science & Technology Education, 10(3), 155–163

    Google Scholar 

  • Juskeviciene, A., Jasute, E., Kurilovas, E., & Mamcenko, J. (2016). Application of 1:1 mobile learning scenarios in computer engineering education. International Journal of Engineering Education, 32(3), 1087–1096

    Google Scholar 

  • Kajonmanee, T., Chaipidech, P., Srisawasdi, N., & Chaipah, K. (2020). A personalised mobile learning system for promoting STEM discipline teachers’ TPACK development. International Journal of Mobile Learning and Organisation, 14(2), 215–235

    Article  Google Scholar 

  • Kaw, A., Clark, R., Delgado, E., & Abate, N. (2019). Analyzing the use of adaptive learning in a flipped classroom for preclass learning. Computer Applications in Engineering Education, 27(3), 663–678

    Article  Google Scholar 

  • Kolekar, S. V., Pai, R. M., & MM, M. P. (2019). Rule based adaptive user interface for adaptive E-learning system. Education and Information Technologies, 24(1), 613–641

    Article  Google Scholar 

  • Kwan, R., & Wong, B. T. M. (2021). Latest advances in STEAM education research and practice: A review of the literature. International Journal of Innovation and Learning, 29(3), 323–339

    Article  Google Scholar 

  • Lee, M. H., Chai, C. S., & Hong, H. Y. (2019). STEM education in Asia Pacific: Challenges and development. The Asia-Pacific Education Researcher, 28, 1–4

    Article  Google Scholar 

  • Li, Y., Wang, K., Xiao, Y., & Froyd, J. E. (2020). Research and trends in STEM education: A systematic review of journal publications. International Journal of STEM Education, 7, article 11. https://doi.org/10.1186/s40594-020-00207-6

  • Li, K. C., & Wong, B. T. M. (2019). How learning has been personalised: A review of literature from 2009 to 2018. In Cheung, et al. (Eds.), Blended Learning: Educational Innovation for Personalized Learning (pp. 72–81). Springer

  • Li, K. C., & Wong, B. T. M. (2020a). Personalising learning with learning analytics: A review of the lit-erature. In Cheung, et al. (Eds.), Blended Learning: Education in a Smart Learning Environment (pp. 39–48). Springer

  • Li, K. C., & Wong, B. T. M. (2020b). Trends of learning analytics in STE(A)M education: A review of case studies. Interactive Technology and Smart Education, 17(3), 323–335

    Article  Google Scholar 

  • Li, K. C., & Wong, B. T. M. (2021). Features and trends of personalised learning: A review of journal publications from 2001 to 2018. Interactive Learning Environments, 29(2), 182–195

    Article  Google Scholar 

  • Liao, C. (2016). From interdisciplinary to transdisciplinary: An arts-integrated approach to STEAM education. Art Education, 69(6), 44–49

    Article  Google Scholar 

  • Lim, L. A., Dawson, S., Gašević, D., Joksimović, S., Pardo, A., Fudge, A., & Gentili, S. (2020). Students’ perceptions of, and emotional responses to, personalised learning analytics-based feedback: An exploratory study of four courses. Assessment & Evaluation in Higher Education, 46(3), 339–359

    Article  Google Scholar 

  • Liu, M., McKelroy, E., Corliss, S. B., & Carrigan, J. (2017). Investigating the effect of an adaptive learning intervention on students’ learning. Educational Technology Research and Development, 65(6), 1605–1625

    Article  Google Scholar 

  • Margot, K. C., & Kettler, T. (2019). Teachers’ perception of STEM integration and education: A sys-tematic literature review. International Journal of STEM Education, 6(2), https://doi.org/10.1186/s40594-018-0151-2

  • McCarthy, E. M., Liu, Y., & Schauer, K. L. (2020). Strengths-based blended personalized learning: An impact study using virtual comparison group. Journal of Research on Technology in Education, 52(3), 353–370

    Article  Google Scholar 

  • Mongeon, P., & Paul-Hus, A. (2016). The journal coverage of Web of Science and Scopus: A comparative analysis. Scientometrics, 106(1), 213–228

    Article  Google Scholar 

  • National Association of State Boards of Education. (2002). Most likely to succeed—Policy making in support of a restructured high school. Alexandria, VA: NASBE

    Google Scholar 

  • Perignat, E., & Katz-Buonincontro, J. (2019). STEAM in practice and research: An integrative literature review. Thinking Skills and Creativity, 31, 31–43

    Article  Google Scholar 

  • Plass, J. L., & Pawar, S. (2020). Toward a taxonomy of adaptivity for learning. Journal of Research on Technology in Education, 52(3), 275–300

    Article  Google Scholar 

  • Pliner, E. M., Dukes, A. A., Beschorner, K. E., & Mahboobin, A. (2020). Effects of student interests on engagement and performance in biomechanics. Journal of Applied Biomechanics, 36(5), 360–367

    Article  Google Scholar 

  • Salahli, M. A., Özdemir, M., & Yasar, C. (2013). Concept based approach for adaptive personalized course learning system. International Education Studies, 6(5), 92–103

    Article  Google Scholar 

  • Shchedrina, E., Galkina, E., Petunina, I., & Lushkov, R. (2020). Integration of mobile learning into complex problem-solving processes during STEM education. International Journal of Interactive Mobile Technologies, 14(21), 19–37

    Article  Google Scholar 

  • Song, Y., Wong, L. H., & Looi, C. K. (2012). Fostering personalized learning in science inquiry supported by mobile technologies. Educational Technology Research and Development, 60(4), 679–701

    Article  Google Scholar 

  • Takeuchi, M. A., Sengupta, P., Shanahan, M. C., Adams, J. D., & Hachem, M. (2020). Transdisciplinarity in STEM education: A critical review. Studies in Science Education, 56(2), 213–253

    Article  Google Scholar 

  • Thibaut, L., Ceuppens, S., De Loof, H., De Meester, J., Goovaerts, L., Struyf, A., & Depaepe, F. (2018). Integrated STEM education: A systematic review of instructional practices in secondary education. European Journal of STEM Education, 3(1), 02. https://doi.org/10.20897/ejsteme/85525

    Article  Google Scholar 

  • U.S. Department of Education, Office of Educational Technology (2017). Reimagining the role of technology in education: 2017 national education technology plan update. Retrieved from https://tech.ed.gov/files/2017/01/NETP17.pdf

  • Xie, H., Chu, H. C., Hwang, G. J., & Wang, C. C. (2019). Trends and development in technology-enhanced adaptive/personalized learning: A systematic review of journal publications from 2007 to 2017. Computers and Education, 140, doi:https://doi.org/10.1016/j.compedu.2019.103599

  • Walkington, C., & Bernacki, M. L. (2019). Personalizing algebra to students’ individual interests in an intelligent tutoring system: Moderators of impact. International Journal of Artificial Intelligence in Education, 29(1), 58–88

    Article  Google Scholar 

  • Walkington, C., & Bernacki, M. L. (2020). Appraising research on personalized learning: Definitions, theoretical alignment, advancements, and future directions. Journal of Research on Technology in Education, 52(3), 235–252

    Article  Google Scholar 

  • Wang, T. H. (2014). Developing an assessment-centered e-Learning system for improving student learning effectiveness. Computers & Education, 73, 189–203

    Article  Google Scholar 

  • Wang, S., Christensen, C., Cui, W., Tong, R., Yarnall, L., Shear, L., & Feng, M. (2020). When adaptive learning is effective learning: Comparison of an adaptive learning system to teacher-led instruction. Interactive Learning Environments. https://doi.org/10.1080/10494820.2020.1808794

    Article  Google Scholar 

  • Wong, B. T. M., & Li, K. C. (2020). Meeting diverse student needs for support services: A comparison between face-to-face and distance-learning students. In K. C. Li, E. Y. M. Tsang, & B. T. M. Wong (Eds.), Innovating Education in Technology-Support Environments (pp. 253–268). Springer

  • You, X., Li, M., & Liu, H. (2019). The feedback of the Chinese learning diagnosis system for personalized learning in classrooms. Frontiers in Psychology, 10, article 1751

  • Zhu, Q., & Wang, M. (2020). Team-based mobile learning supported by an intelligent system: Case study of STEM students. Interactive Learning Environments, 28(5), 543–559

    Article  Google Scholar 

Download references

Acknowledgements

The work described in this paper was partially supported by a grant from Hong Kong Metropolitan University (2021/011).

Author information

Authors and Affiliations

  1. Hong Kong Metropolitan University, Hong Kong, China

    Kam Cheong Li & Billy Tak-ming Wong

Authors
  1. Kam Cheong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Billy Tak-ming Wong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Billy Tak-ming Wong.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, K.C., Wong, B.Tm. Personalisation in STE(A)M education: a review of literature from 2011 to 2020. J Comput High Educ 35, 186–201 (2023). https://doi.org/10.1007/s12528-022-09341-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12528-022-09341-2

Keywords

Search

Navigation