Table 3 ML Techniques used in TA
From: Teaching analytics, value and tools for teacher data literacy: a systematic and tripartite approach
ML Techniques | Examples | Source |
|---|---|---|
Classification | Time-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) |
Clustering | Latent Class Analysis (LCA). | |
NLP | TFIDF, 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 Learning | Recurrent Neural Network (RNN), Convolutional Neural Network (CNN). |