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The Potential of Big Data Application in Mathematics Education in Malaysia

Author

Listed:
  • Sim Jia Jin

    (School of Education, Faculty of Social Sciences and Humanities, Universiti Teknologi Malaysia, Johor Bahru 81310, Johor, Malaysia)

  • Abdul Halim Abdullah

    (School of Education, Faculty of Social Sciences and Humanities, Universiti Teknologi Malaysia, Johor Bahru 81310, Johor, Malaysia)

  • Mahani Mokhtar

    (School of Education, Faculty of Social Sciences and Humanities, Universiti Teknologi Malaysia, Johor Bahru 81310, Johor, Malaysia)

  • Umar Haiyat Abdul Kohar

    (Faculty of Management, Universiti Teknologi Malaysia, Johor Bahru 81310, Johor, Malaysia)

Abstract

The world is facing rapid changes after the emergence of innovative technologies. These changes aim to ensure that a country keeps track of current world developments, strengthens its economy, and reduces its dependence on imports. Hence, every country is now amid technological transformation in the industrial sector by replacing manpower with machines to increase production and efficiency, allowing for mass production. Technology advancements in control, information technology, and automation that are applied to business and industry production processes are referred to as ‘Industry 4.0’. The objective is to increase the autonomy, adaptability, and effectiveness of decision-making and production processes utilizing cyber-physical systems (CPS), Big Data (BD), artificial intelligence (AI), and the industrial Internet of Things (IoT). Specifically, this article first introduces Industry Revolution (IR) 4.0, followed by a delineation of the concept of BD. Correspondingly, we discuss BD in education and relate mathematics education with BD. The article concludes with the implications of BD for Malaysian teaching and learning practices.

Suggested Citation

  • Sim Jia Jin & Abdul Halim Abdullah & Mahani Mokhtar & Umar Haiyat Abdul Kohar, 2022. "The Potential of Big Data Application in Mathematics Education in Malaysia," Sustainability, MDPI, vol. 14(21), pages 1-23, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:13725-:d:950909
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    References listed on IDEAS

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    1. Haitham Nobanee & Mehroz Nida Dilshad & Mona Al Dhanhani & Maitha Al Neyadi & Sultan Al Qubaisi & Saeed Al Shamsi, 2021. "Big Data Applications the Banking Sector: A Bibliometric Analysis Approach," SAGE Open, , vol. 11(4), pages 21582440211, December.
    2. Roberto Moro Visconti & Donato Morea, 2019. "Big Data for the Sustainability of Healthcare Project Financing," Sustainability, MDPI, vol. 11(13), pages 1-17, July.
    3. Torrecilla, José L. & Romo, Juan, 2018. "Data learning from big data," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 15-19.
    4. Lee, In, 2017. "Big data: Dimensions, evolution, impacts, and challenges," Business Horizons, Elsevier, vol. 60(3), pages 293-303.
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    Cited by:

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