STEM fields

Coding for the future. Effects of a computer assisted learning program on STEM and socio- emotional learning outcomes for girls.

Research Questions and Hypothesis Question 1: Is there an effect of the “Coding for the future” computer-assisted learning program on the choice of pursuing a STEM degree in college for girls? Question 2: Is there an effect of the “Coding for the Future” program on girl’s self-esteem and self- efficacy in middle school? Question 3: To what extent does the relationship with self-esteem and self-efficacy persist across time? Question 4: How do these relationships vary by race? H1: Given existing literature that sustains the positive relationship between computer-assisted learning (CAL) programs and STEM-related skills (Escueta et al., 2020; Malamud & Pop-Eleches, 2011; Campuzano et al., 2009; Rutherford et al., 2014; Van Klaveren et al., 2017), coupled with the literature that supports the fact that the younger age girls develop STEM-related skills the higher chance they will pursue a STEM-related field (Unkovic et al., 2016; Chetty & Barlow-Jones, 2018; Eagly and Karau, 2002), we hypothesize that a CAL learning program targeted to young girls will positively affect the decision of pursuing a STEM degree in college. H2: Existing studies that look into the STEM gender gap point out that senses of self-efficacy and self- esteem during school years are critical determinants of the choice of pursuing STEM fields (Unkovic et al., 2016; Chetty & Barlow-Jones, 2018). We hypothesize that by exposing young girls to STEM abilities at a young age, their sense of self-esteem and self-efficacy will increase. H3: Given that the long-term relationship of CAL and non-cognitive traits such as self-esteem and self- efficacy has not been explored yet, this hypothesis will remain non-directional. However, if H1 is not refuted, we would expect these non-cognitive traits to be higher. H4: Amongst women – who are already underrepresented in STEM fields – Black and Hispanic women are particularly under-represented (Farinde & Lewis, 2012; Price, 2010). We want to explore if the effects captured in RQ1 and RQ2 vary by race. Since our study is innovative, it will be the first to explore the effects of a girl-targeted CAL in pursuing STEM degrees and related non-cognitive traits. No literature breaks up this unique relationship by race. Although we expect to find differentiated effects, the hypothesis is non-directional. Data source: South Bend School District Long Term Data (SBSD-LTD) The South Bend School District Long Term Data is a longitudinal dataset that follows four cohorts of students across critical stages of their lives. It is an initiative by the Kellog Institute at Notre Dame University. Its primary goal

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