Mobile-based learning in science trends: a systematic review (2015–2023)

Abstract

Mobile-based learning has emerged as the best solution for the constraints posed by traditional educational methods. This article makes an important contribution by filling the research gap in current trends in mobile inquiry-based learning in science and providing valuable insights for educational researchers and practitioners in developing more effective teaching approaches in the technological era. This research is bibliometric research with data sources coming from the Scopus database. The keyword used is mobile inquiry-based learning, limited to the range of 2015–2023. There are 308 documents in all countries, as well as 8 documents originating from Indonesia. The data is then analysed using spreadsheets, VOSViewer, and Biblioshiny. This dataset highlights the growing research landscape in mobile inquiry-based learning in Indonesia. Even though there is a quantitative gap when compared with other countries, the continued yearly growth shows increasing enthusiasm for this pedagogical approach in the Indonesian education sector. This comprehensive analysis not only provides an overview of research trends, but also equips researchers and stakeholders with the tools needed to make decisions and contribute to the advancement of this educational approach.

Keywords:

• Mobile learning
• educational trends
• Inquiry learning
• Science education
• Pedagogical approaches

Reviewing editor:

• Shuyan Wang, The University of Southern Mississippi, 118 College Dr., Hattiesburg, MS 39406, USA.

Subjects:

• Science Education
• Technology in Education
• Teaching & Learning - Education

Acknowledgement

The authors thank the ‘Direktorat Riset, Teknologi, dan Pengabdian Masyarakat (DRTPM) Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi’ for the funding that has been given to the completion of this research.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Asri Widowati

Asri Widowati contributed to conducting analysis using Shiny Bibliometrics, representing data results, compiling articles and conducting discussions.

Rizki Arumning Tyas

Rizki Arumning Tyas contributed to finding data sources through the Scopus database, recapping and classifying data, visualizing with VOSViewer, compiling and editing articles.