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Dealing with Randomness and Concept Drift in Large Datasets

Author

Listed:
  • Kassim S. Mwitondi

    (Industry & Innovation Research Institute, College of Business, Technology & Engineering, Sheffield Hallam University, 9410 Cantor Building, City Campus, 153 Arundel Street, Sheffield S1 2NU, UK)

  • Raed A. Said

    (Faculty of Management, Canadian University Dubai, Al Safa Street-Al Wasl, City Walk Mall, Dubai P.O. Box 415053, United Arab Emirates)

Abstract

Data-driven solutions to societal challenges continue to bring new dimensions to our daily lives. For example, while good-quality education is a well-acknowledged foundation of sustainable development, innovation and creativity, variations in student attainment and general performance remain commonplace. Developing data -driven solutions hinges on two fronts-technical and application. The former relates to the modelling perspective, where two of the major challenges are the impact of data randomness and general variations in definitions, typically referred to as concept drift in machine learning. The latter relates to devising data-driven solutions to address real-life challenges such as identifying potential triggers of pedagogical performance, which aligns with the Sustainable Development Goal (SDG) #4-Quality Education. A total of 3145 pedagogical data points were obtained from the central data collection platform for the United Arab Emirates (UAE) Ministry of Education (MoE). Using simple data visualisation and machine learning techniques via a generic algorithm for sampling, measuring and assessing, the paper highlights research pathways for educationists and data scientists to attain unified goals in an interdisciplinary context. Its novelty derives from embedded capacity to address data randomness and concept drift by minimising modelling variations and yielding consistent results across samples. Results show that intricate relationships among data attributes describe the invariant conditions that practitioners in the two overlapping fields of data science and education must identify.

Suggested Citation

  • Kassim S. Mwitondi & Raed A. Said, 2021. "Dealing with Randomness and Concept Drift in Large Datasets," Data, MDPI, vol. 6(7), pages 1-19, July.
    • Handle: RePEc:gam:jdataj:v:6:y:2021:i:7:p:77-:d:597140
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    References listed on IDEAS

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    1. Robert Lerman, 2019. "Do firms benefit from apprenticeship investments?," IZA World of Labor, Institute of Labor Economics (IZA), pages 1-55, October.
    2. Sarah Randall Johnson & Frances King Stage, 2018. "Academic Engagement and Student Success: Do High-Impact Practices Mean Higher Graduation Rates?," The Journal of Higher Education, Taylor & Francis Journals, vol. 89(5), pages 753-781, September.
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