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Table 6 Summary of the used systems for implicit modeling of personality

From: Implicit modeling of learners’ personalities in a game-based learning environment using their gaming behaviors

Systems Learning Fun Traces Personality Model Data Analysis Method Accuracy
Virtual Personality Assessment Lab
(Bunian et al. 2018)
+ Gaming behavior FFM Hidden Markov Models (HMM),
Baum-Welch algorithm
From 54.1% to 59.1%
Psyops (Tekofsky et al. 2013) + Gaming behavior FFM Not mentioned Not mentioned
Handwriting (Chen and Lin 2017) Hand-writing Not mentioned Support Vector Machine, k-Nearest Neighbour, AdaBoost and Artificial Neural Network From 62.5% to 83.9%
MOOC (Chen et al. 2016) + Learning FFM Gaussian Process and Random Forest Not mentioned
Facebook (Buettner 2017). Social network FFM Generalized linear modeling From 62% to 71%
Twitter (Golbeck et al. 2011) Social network FFM ZeroR and Gaussian Processes Not mentioned
Electronically Activated Recorder (Mairesse et al. 2007) Speech FFM Naive Bayes, AdaboostM1 and Support vector machines From 51.45% to 62.52%
Smart phones (Chittaranjan et al. 2011) Smart phone FFM SVM and C4.5 classifiers From 59.8% to 75.9%
E-learning system (Ghorbani and Montazer 2015) + Learning FFM Fuzzy logic From 78% to 97%
Wearable sensors (Olguın et al. 2009) Sensors FFM Accelerometer signal, IR transmissions, RSSI (radio signal strength indicator), Not mentioned
Our framework (CAG + LA system) + + Gaming behavior FFM Naïve Bayes classifier From 70.58% to 79.41%