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Table 1 Demographic and learner characteristics of participants

From: Reducing withdrawal and failure rates in introductory programming with subgoal labeled worked examples

Characteristic

Data collection

Responses

Age

Open-ended

85% between 18 and 23, range—17–46

Gender

Male, female, other

67% male, 31% female, 2% other

Race

Caucasian, Latinx, Asian, Black, other, mixed

73% Caucasian, 5% Latinx, 8% Asian, 3% Black, 11% other or mixed

Primary language

English, not English

90% English

Family SES

< $25k, 25–50k, 50–100k, 100–200k, > 200k

27% below $50k, 69% $50–200k, 4% above $200k

Major

Computing, engineering, other

43% computing, 40% engineering

Status

Full-time, part-time

92% full-time

High school GPA

Open-ended

Average—3.56/4.0

College GPA

Open-ended

Average—3.42/4.0

Year in school

1st, 2nd, 3rd, 4th, 5th, other

47% 1st, 25% 2nd, 16% 3rd, 12% higher

Expected grade

A, B, C, D, F

64% A, 28% B, 8% C

Expected difficulty

Likert type 1—very difficult to 5—not at all difficult

Average—2.97

Level of interest in course

Likert type 1—not at all interested to 5—very interested

Average—3.84

Reason for taking course (select all that apply)

Advised to, required for major, interested in topic, relevant to career path

31% advised to, 92% required for major, 57% interested in topic, 56% relevant to career path

Prior experience with programming (select all that apply)

Matrix that crossed K-5, 6–8, and 9–12 grades with informal, formal, or self-guided learning

34% had no prior experience; 31% had experience in K-5, 25% in 6–8, and 61% in 9–12; 18% had informal experience, 50% had formal, and 29% had self-guided