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Table 1 Summary of prior work reviewed

From: A revised application of cognitive presence automatic classifiers for MOOCs: a new set of indicators revealed?

Studies by

Algorithm

Main features

Best outcome metrics

Accuracy (%)

Cohen’s κ

McKlin et al. (2001)

Simple neural networks

Dictionary-based words and phrases

68

0.31

Corich et al. (2006)

Bayesian network

Dictionary-based words and phrases

71

–

Kovanović et al. (2014)

Support vector machine

Bag-of-words, n-grams, and structural features

58.4

0.41

Waters et al. (2015)

Conditional random fields

Bag-of-words, n-grams, and more structural features

64.2

0.48

Kovanović et al. (2016)

Random forest

LIWC, Coh-Metrix, LSA, structural features

70.3

0.63

Neto et al. (2018)

Random forest

LIWC, Coh-Metrix, word embeddings, structural features

83

0.72

Farrow et al. (2019)

Random forest

Same as Kovanović et al. (2016)

61.7

0.46

Barbosa et al. (2020)

Random forest

Same as Kovanović et al. (2016)

67

0.32

Neto et al. (2021)

Random forest

Same as Kovanović et al. (2016)

76

67a

57b

0.55

0.2

0.38

  1. a,bNeto et al. (2021) contains three experiments. The first one was on a combined data set. The next two were training the automatic classifier on one set and testing on another, and vice versa