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Personalisation in STE(A)M Education: A Review of Literature from 2011 to 2020

Authors :
Li, Kam Cheong
Wong, Billy Tak-ming
Source :
Journal of Computing in Higher Education. Apr 2023 35(1):186-201.
Publication Year :
2023

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.

Details

Language :
English
ISSN :
1042-1726 and 1867-1233
Volume :
35
Issue :
1
Database :
ERIC
Journal :
Journal of Computing in Higher Education
Publication Type :
Academic Journal
Accession number :
EJ1367903
Document Type :
Journal Articles<br />Information Analyses
Full Text :
https://doi.org/10.1007/s12528-022-09341-2