Poster #88 - What Predicts Achievement in Computer Science? Considering Gender and Motivation in College Students

• In Event: 3-003 - Poster Session 09
  In Poster Session: PS 09 Section - Education, Schooling

Integrative Statement

Female students achieve at significant levels in biomedical sciences (earning 59% of bachelor degrees), but less so in math-intensive STEM fields (earning 18% of degrees in computer science, for example; NCES, 2014). Explanations range from gender differences in abilities, interests, and motivation to encounters with stereotype and bias in the classroom (see Wang & Degol, 2017, for a review). A sense of belonging in the classroom, engagement and comfort with the material, and self-efficacy predict achievement in STEM fields, particularly for females (e.g., Cheryan et el., 2009). Further, an incremental theory of intelligence (growth mindset as opposed to fixed) predicts persistence and academic achievement in males and females across adolescence in a variety of fields (Rattan et al., 2015). However, recent studies raise doubts about whether a growth mindset predicts achievement in CS1 (introductory computer science; Scott & Ghinea, 2014). It is also not clear how these variables relate to each other among CS1 college students. We measured mindset (Dweck, 2006) and sense of belonging (Good et al., 2012) at the beginning of the semester and total points earned at the end of the semester in 430 college students (190 females) enrolled in CS1 at a highly selective private college. In a subset (N=329, 137 females) we also measured engagement (Authors, 2016), comfort (Wilson & Shrock, 2001), and self-efficacy for programming (Ramalingam & Wiedenbeck, 1998) in the middle and at the end of the semester. Data were collected over three semesters during 2017 and 2018. At the beginning of the course, females scored significantly lower than males on belonging, but similarly on mindset. At midterm (but not end of term) females scored significantly lower on comfort and self-efficacy. There were no gender differences in engagement at any time, and there were no differences in points earned at the end of the term (Table 1). Nine variables were included in a regression model that explained 33% of the variance in points earned (Table 2). Age and growth mindset negatively predicted achievement; engagement at midterm positively predicted achievement as did comfort and self-efficacy at the end of term. These results suggest that although female students felt less belonging, comfort, and self-efficacy at first, they were equally engaged throughout the course, and by the end of term felt equally comfortable and efficacious and achieved equally with males. Questions remain about whether the gender differences observed are related to persistence in the field, despite the equal performance, and whether the age of the students also correlates with an interest in math-intensive fields. Growth mindset was negatively correlated with achievement, and this raises questions for further exploration. Are consistently high-achieving students (perhaps with a belief in their inherent intelligence) more likely to enroll in CS? Does the frequent feedback about success (or failure) of programming attempts in CS courses favor those with a fixed (and positive) view of their abilities? Answers to these questions may lead to pedagogical interventions that enhance learning and persistence in CS for all students.

Author

• Ann Cale Kruger, Georgia State University
  Presenting Author