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Table 1 Explanatory variables collected at admission to the medical school (UNAM Faculty of Medicine, Mexico City)

From: Predicting students’ academic progress and related attributes in first-year medical students: an analysis with artificial neural networks and Naïve Bayes

Group

Explanatory variables

Students’ demographics

Gender (SEX), age at admission (INITIAL_AGE), marital status (MARITAL_STATUS), children (CHILDREN), employment (STUDENT_EMPLOYMENT).

Family environment

Whether the student is the first, second or third born (NUM_SIBLINGS), academic background of the father (FATHER_STUDIES), academic background of the mother (MOTHER_STUDIES), father’s occupation (FATHER_OCCUP), mother’s occupation (MOTHER_OCCUP).

Socio-economic status

Students’ home (HOME), number of rooms at home (ROOM_NUM), number of lightbulbs in home (NUM_FOCOS), number of people that live in home (LIVE), number of family members that work (NUM_WORK), persons that financially support the student (FIN_SUPPORT), number of financially dependent persons in the students’ family (DEPENDENT), monthly family income (MONTH_INCOME).

Prior educational experience

Type of primary school, public, private or both (PRIMARY), type of junior high school public, private or both (JUNIOR_HIGH_SCHOOL), type of high school public, private or both (HIGH_SCHOOL), high school subsystem (SUBSYSTEM), high school shift (SHIFT), timely completion of high school (three years) (HIGHSCHOOL_THREEYEARS), remedial exams in high school (EX_EXAMS), high school grade average (AVERAGE_HIGHSCHOOL), student’s perception of academic success (SUCCESS), institutional affiliation of high school (AFFILIATION), high school campus (HS_CAMPUS).

Type of admission

Type of admission (TYPE_ADMISSION).

Student progress

Percentage of obtained credits at the end of the first year (PROGRESS). Students were regular or irregular at the end of the first year if they obtained 100% of credits or not (ACADEMIC_STATUS_1STY).