Table 2. Features used in predicting student performance.

#	Feature type	Characteristics	Reference
1	Demographic	Identification of the student, gender, age, marital status, previous studies, nationality, city, semester, number of credits passed, income.	Costa et al., 2017; ElGamal, 2013; Salinas, Williams & King, 2019; Sivasakthi, 2017; Vilanova et al. (2019)
2	Grades	Intermediate grades of the tasks, final grade, grade of the first semester, completed assignment, laboratory work in class, qualification introductory programming test, final grade, exams of the period.	Buenaño-Fernández, Gil & Luján-Mora, 2019; de la Peña et al., 2017; Moreno-Marcos et al., 2020; Munson & Zitovsky, 2018; Pereira et al., 2020b; Sivasakthi, 2017; Sunday et al. (2020)
3	Study habits	Participation in forums, clickstreams, class attendance, video playback, persistence in the development of activities, number of times you took a test, number of exam attempts per subject, numbers of logins.	Kuehn et al., 2017; Lu et al., 2018; Moreno-Marcos et al., 2018; Moreno-Marcos et al., 2020; Pereira et al., 2019; Sunday et al., 2020; Vilanova et al., 2019; Yoshino et al. (2020)
4	Programming	Math background, problem-solving ability, previous programming experience, number of attempts, average attempts of submission by problems, number of accepted solutions, individual coding aptitude of the student, number of exercises performed, number of correct exercises, tests in the source code, results of submission, time to solve exercises, keystroke.	Castro-Wunsch, Ahadi & Petersen, 2017; Costa et al., 2017; Leinonen et al., 2016; Moreno-Marcos et al., 2020; Munson & Zitovsky, 2018; Pereira et al., 2021; Pereira et al., 2020a; Pereira et al., 2019; Salinas, Williams & King, 2019; Sunday et al., 2020; Villagrá-Arnedo et al. (2017)