Dealing with randomness and concept drift in large datasets
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.
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
Artificial neural networks (ANNs), Big Data, Concept drift, Data science, Supervised modelling, Sustainable development goals, Unsupervised modelling
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