Mobile learning for science and mathematics school education: A systematic review of empirical evidence. (June 2018)

• Record Type: Journal Article
• Authors: Bano, Muneera; Zowghi, Didar; Kearney, Matthew; Schuck, Sandra; Aubusson, Peter
• Is Part Of: Computers & education. Volume 121(2018)
• Page Start: 30
• Page End: 58
• Publication Date: 2018-06
• Subjects: Mobile learning -- Mathematics education -- Science education -- School education -- Pedagogy; Education -- Data processing -- Periodicals; Education -- Periodicals; Computers -- Periodicals; Computer-Assisted Instruction -- Periodicals; Éducation -- Informatique -- Périodiques; Electronic journals; 370.285
• DOI: 10.1016/j.compedu.2018.02.006
• Languages: English
• ISSNs: 0360-1315

Abstract

Abstract: The ubiquity, flexibility, ease of access and diverse capabilities of mobile technologies make them valuable and a necessity in current times. However, they are under-utilized assets in mathematics and science school education. This article analyses the high quality empirical evidence on mobile learning in secondary school science and mathematics education. Our study employed a Systematic Literature Review (SLR) using well-accepted and robust guidelines. The SLR resulted in the detailed analysis of 49 studies (60 papers) published during 2003–2016. Content and thematic analyses were used to ascertain pedagogical approaches, methodological designs, foci, and intended and achieved outcomes of the studies. The apps and technologies used in these studies were further classified for domain, type and context of use. The review has highlighted gaps in existing literature on the topic and has provided insights that have implications for future research. Highlights: This article presents analysis of m-Learning apps for science and math education. We have followed standard guidelines of Systematic Literature Review. The review has resulted in 49 studies (60 papers) published during 2003–2016. The review has highlighted gaps in existing literature on the topic. Some noteworthy observations in the analysis are found for future research.