From: Predicting academic success in higher education: literature review and best practices
Factor Category | Factor Description | References | % |
---|---|---|---|
Prior Academic Achievement | Pre-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 Demographics | Gender, 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’ Environment | Class 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% |
Psychological | Student 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 Activity | Number 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% |