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Table 1 Most influential factors on the prediction of students’ academic success

From: Predicting academic success in higher education: literature review and best practices

Factor CategoryFactor DescriptionReferences%
Prior Academic AchievementPre-university data: high school background (i.e., high school results), pre-admission data (e.g. admission test results)
University-data: semester GPA or CGPA, individual course letter marks, and individual assessment grades
(Adekitan & Salau, 2019; Ahmad, Ismail, & Aziz, 2015; Al-barrak & Al-razgan, 2016; Almarabeh, 2017; Anuradha & Velmurugan, 2015; Asif, Merceron, Abbas, & Ghani, 2017; Asif, Merceron, & Pathan, 2015; Garg, 2018; Hamoud, Hashim, & Awadh, 2018; Mesarić & Šebalj, 2016; Mohamed & Waguih, 2017; Mueen, Zafar, & Manzoor, 2016; Oshodi, Aigbavboa, Aluko, Daniel, & Abisuga, 2018; Singh & Kaur, 2016; Sivasakthi, 2017; Yassein, Helali, & Mohomad, 2017)44%
Student DemographicsGender, age, race/ethnicity, socioeconomic status (i.e., parents’ education and occupation, place of residence / traveled distance, family size, and family income).(Ahmad et al., 2015; Anuradha & Velmurugan, 2015; Garg, 2018; Hamoud et al., 2018; Mohamed & Waguih, 2017; Mueen et al., 2016; Putpuek, Rojanaprasert, Atchariyachanvanich, & Thamrongthanyawong, 2018; Singh & Kaur, 2016; Sivasakthi, 2017)25%
Students’ EnvironmentClass type, semester duration, type of program(Adekitan & Salau, 2019; Ahmad et al., 2015; Hamoud et al., 2018; Mesarić & Šebalj, 2016; Mohamed & Waguih, 2017; Mueen et al., 2016)17%
PsychologicalStudent interest, behavior of study, stress, anxiety, time of preoccupation, self-regulation, and motivation.(Garg, 2018; Hamoud et al., 2018; Mueen et al., 2016; Putpuek et al., 2018)11%
Student E-learning ActivityNumber of logins times, number of tasks, number of tests, assessment activities, number of discussion board entries, number / total time material viewed(Mueen et al., 2016)3%