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Table 3 ML Techniques used in TA

From: Teaching analytics, value and tools for teacher data literacy: a systematic and tripartite approach

ML TechniquesExamplesSource
ClassificationTime-Series Classification Analysis, Supervised Binary Classification, AdaBoot Ensemble Classifier, Random Forests, Support Vector Machine (SVM), Generalised Boosted Models (GBM), Logistic Regression and Multinomial Logistic Regression.(Barmaki and Hughes 2015; Prieto et al. 2018; Prieto et al. 2016; Suehiro et al. 2017; Thomas 2018; Xu and Recker 2012)
ClusteringLatent Class Analysis (LCA).(Xu and Recker 2012)
NLPTFIDF, Co-occurrence Analysis, Point-wise Mutual Information, Non-negative Matrix Factorisation Topic Modelling Technique, Jaccard Similarity Co-efficient, Semantic Analysis.(Müller et al. 2016; Taniguchi et al. 2017; Sergis and Sampson 2016)
Deep LearningRecurrent Neural Network (RNN), Convolutional Neural Network (CNN).(Prieto et al. 2018)