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Table 1 Comparative analysis

From: Educational data mining: prediction of students' academic performance using machine learning algorithms

References Variables Objectives Level Dataset Algorithms Accuracy
Min Max
Asif et al. (2017) The marks for all the courses that are taught in the four years of the degree programme Predicting students' performance Undergraduate students 210 DT, 1-NN, NB, NN, RF NN (62.50%) NB (83.65%)
Cruz-Jesus et al. (2020) Year of the study cycle, gender, age, number of enrolled years in high school, scholarship, internet access, class size, school size, economic level, population density, number of unit courses attended Predicting students' performance High schools students 110627 ANN, DT, ET, RF, SVM, kNN, LR LR (81.1%) SVM (51.2%)
Fernandes et al. (2019) Class with persons with special needs, Classroom usage environment, Gender, age (mean), Student benefit, city, neighbourhood, Student with special needs, Grade (mean), Absence (mean) Predict academic outcomes of student performance High schools students Dataset1:19000
Gradient Boosting Machine 89.5% 91.9%
Hoffait and Schyns (2017) Gender, Nationality, Studies, Prior schooling, math, scholarship, success Predicting students at high risk of failure secondary school students 2244 RF, LR, ANN ANN (70.4%) RF (90%)
Rebai et al. (2020) Socioeconomic status, school type, school location, competition, teacher characteristic (experience, salary), class size, school size, gender, parental education, political context, parental pressure to identify the key factors that impact schools' academic performance and to explore their relationships Secondary schools 105 schools RT, RF   
Ahmad and Shahzadi (2018) Previous degree marks, Home environment, Study habits Learning skills, Hardworking and Academic interaction Identification of students in the risk group Undergraduate students 300 MPNN   95%
Musso et al., (2020) Learning strategies, coping strategies, cognitive factors, social support, background, self-concept, self-satisfaction, use of IT and reading Grade point average, academic retention, and degree completion Undergraduate students 655 ANN 60.5% 80.7%
Waheed et al., (2020) Students’ demographics, clickstream events Pass-fail, withdrawn-pass, distinction-fail, distinction-pass Undergraduate students 32593 ANN, SVM, LR 84% 93%
Xu et al. (2019) Internet usage behaviours comprise online time, internet connection frequency, internet traffic volume, and online time Predicting students' performance Undergraduate students 4000 DT, NN, SVM 71% 76%
Bernacki et al. (2020) Log records in the learning management system Predict achievement Undergradeate students 337 LR, NB, J-48 DT, J-Rip DT J-48 (53.71%) LR (67.36%)
Burgos et al. (2018) Historical student course grade data Drop out of a course Undergradeate students 100 SVM, FFNN, PESFAM, LOGIT_Act SVM (62.50) LOGIT_Act(97.13%)