UNDERSTANDING DEPTH OF REFLECTIVE WRITING IN WORKPLACE LEARNINGASSESSMENTS USING MACHINE LEARNING CLASSIFICATION
Abstract : The collapse of Dam I, owned by Vale S.A, in Brumadinho-MG (Brazil), among other serious socioenvironmental consequences, contaminated the waters of the Paraopeba River in a stretch of hundreds of kilometers. Considering the relevance of monitoring water quality, and knowing that field evaluation is a time-consuming and costly procedure, the use of satellite images, widely available at low cost, emerges as a relevant alternative.This work proposes a systematic experimental evaluation of five machine learning methods - Extra Trees, Multilayer Perceptron, Naïve Bayes, Random Forest and Support Vector Machine, under different configurations and input data treatments, to classify the turbidity of the Paraopeba River waters from Sentinel-2 mission images.
? Building upon the existing coding scheme by Kember et al. and extending the analytical approach by Kovanovic et al. this study designed an automated machine learning system to classify learners.
? Considering some of these research gaps, our work goes beyond the existing approaches of understanding reflective practices within MOOCs, trying to detect the reflective depth exhibited by professionals within workplace learning, which is currently underexplored.
? In contrast, process reflection involves deliberating on the methods of problem-solving and their associated effectiveness and merit. Finally, when engaging in premise reflection, learners criticallyevaluate their fundamental beliefs and the presuppositions underlying their knowledge and reasons for attending to the problem.
? Additionally, the descriptive accounts allow proble-reframing and transverse through different hierarchical levels of reflection
? The proposed approach can effectively support the deployment at scale of the labor-intensive evaluation of reflective writing by highly trained professionals.
? The proposed approach can effectively support the deployment at scale of the labor-intensive evaluation of reflective writing by highly trained professionals
? This study focused on developing an automated classification system to categorize learners' reflections into four levels, according to the proposed coding scheme and explore the features that contribute towards this classification of professional development through leadership learning.
? Considering the relevance of monitoring water quality, and knowing that field evaluation is a time-consuming and costly procedure, the use of satellite images, widely available at low cost, emerges as a relevant alternative.
? This work proposes a systematic experimental evaluation of five machine learning methods - Extra Trees, Multilayer Perceptron, Naïve Bayes, Random Forest and Support Vector Machine, under different configurations and input data treatments, to classify the turbidity of the Paraopeba River waters from Sentinel-2 mission images.
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