Dealing with randomness and concept drift in large datasets

dc.contributor.author Mwitondi, Kassim S.
dc.contributor.author Said, Raed A.
dc.date.accessioned 2021-08-10T13:56:31Z
dc.date.available 2021-08-10T13:56:31Z
dc.date.copyright © 2021
dc.date.issued 2021-07
dc.description This article is not available at CUD collection. The version of scholarly record of this article is published in Data (2021), available online at: https://doi.org/10.3390/data6070077 en_US
dc.description.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 appli-cation. 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 educa-tionists 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. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. en_US
dc.description.sponsorship Assess United Arab Emirates United Nations World Data Forum Education Principal Component Polar Environment Data Science Centre en_US
dc.identifier.citation Mwitondi, K. S., & Said, R. A. (2021). Dealing with Randomness and Concept Drift in Large Datasets. Data, 6(7), 77. https://doi.org/10.3390/data6070077 en_US
dc.identifier.issn 23065729
dc.identifier.uri https://doi.org/10.3390/data6070077
dc.identifier.uri http://hdl.handle.net/20.500.12519/422
dc.language.iso en en_US
dc.publisher MDPI AG en_US
dc.relation Authors Affiliations : Mwitondi, K.S., College of Business, Technology & Engineering, Sheffield Hallam University, Industry & Innovation Research Institute, 9410 Cantor Building, City Campus, 153 Arundel Street, Sheffield, S1 2NU, United Kingdom; Said, R.A., Faculty of Management, Canadian University Dubai, Al Safa Street-Al Wasl, City Walk Mall, P.O. Box 415053, Dubai, United Arab Emirates
dc.relation.ispartofseries Data;Volume 6, Issue 7
dc.rights Creative Common Attribution 4.0 International (CC BY 4.0) License
dc.rights.holder Copyright : © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.subject Artificial neural networks (ANNs) en_US
dc.subject Big Data en_US
dc.subject Concept drift en_US
dc.subject Data science en_US
dc.subject Supervised modelling en_US
dc.subject Sustainable development goals en_US
dc.subject Unsupervised modelling en_US
dc.title Dealing with randomness and concept drift in large datasets en_US
dc.type Article en_US
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