Computer Adaptive-Based Learning and Assessment for Enhancing STEM Education in Africa: A Fourth Industrial Revolution Possibility
Jumoke I. Oladele (),
Mdutshekelwa Ndlovu and
Musa A. Ayanwale
Additional contact information
Jumoke I. Oladele: University of Johannesburg
Mdutshekelwa Ndlovu: University of Johannesburg
Musa A. Ayanwale: University of Johannesburg
Chapter Chapter 8 in Mathematics Education in Africa, 2022, pp 131-144 from Springer
Abstract:
Abstract With the numerous advantages that characterised the fourth industrial revolution (4IR), Africa needs to intensify improvements in teaching Mathematics as a key Science, Technology, Engineering and Mathematics (STEM) subject at various levels of education. Mathematics is a fundamental and cross-cutting subject in post-basic education. Mathematics occupies a central position because of its roles in this 4IR and the demands of its fast-evolving workplace. This chapter aims to channel a pathway for enhancing teaching and learning using a focal theory departure premised on the existing knowledge of 4IR, focusing on Computer Adaptive Learning (CAL) for Mathematics education. Employing the research design of theory adaptation being a conceptual chapter, this study adopts the learning theory of connectivism, which suggests learners should combine thoughts, theories, and general information meaningfully to enhance their classroom experiences. Mathematics education has been perceived to enhance the learning experience. The extent to which achieved learning objectives are measured through CAL at the higher levels of learning remains essential. Premised that digital learning has been well explored in South Africa compared to other African countries, it was recommended that South Africa should also champion implementing CAL for Mathematics in higher institutions in furtherance of 4IR compliance in education.
Keywords: Assessment; Computer adaptive learning; Mathematics education; 4IR; Digital solutions (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-13927-7_8
Ordering information: This item can be ordered from
http://www.springer.com/9783031139277
DOI: 10.1007/978-3-031-13927-7_8
Access Statistics for this chapter
More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().