Finding time for computer science in the elementary school day: a quasi-experimental study of a transdisciplinary problem-based learning approach

International Journal of STEM Education

Table 11 Students’ socio-demographic characteristics in FSA ELA/mathematics sample

Demographicsss		Percentage (%)
Gender	Male	51.7
Gender	Female	48.3
Race	White	73.7
	African American	20.3
	Asian	6.0
Ethnicity	Hispanic	46.2
Ethnicity	Non-Hispanic	53.8
Grade level	3rd	32.3
	4th	28.6
	5th	39.1
SES	Students receiving free and reduced price lunch	35.6
SES	Students not receiving free and reduced price lunch	64.4
English language proficiency	Instructed on acquiring English as a second language	12.7
	Still being monitored/exiting the program to learn English	9.0
	Native English speaker	78.3
Conditions	Treatment group	49.2
Conditions	Comparison group	50.8
Experience with Code. org	Previous experience completing Code.org activities prior to completing the questionnaires	93.4
Experience with Code. org	No previous experience completing Code.org activities	6.6

Students self-reported their race on the post-questionnaire. When answering about their race, students could select Asian, African American, White, or other. Students who selected multiple answer choices were coded as multi-racial. In the current analyses, we examined data collected from students identifying as White, African American, or Asian to align with the existing literature on CS (e.g., Cooper & Dierker, 2017; Rainey, Dancy, Mickelson, Stearns, & Moller, 2018; Wang, Hong, Ravitz, & Hejazi Moghadam, 2016). Students identifying as “other” race or multi-racial were not included in the current analyses.