| Despite evidence of smaller gaps in recent years, there is still major concern of high student attrition rates in science, technology, engineering, and mathematics (STEM) fields, particularly in engineering and computer science (Chen, 2013, 2015; NSF, 2013). The literature has shown that academic achievement is an important predictor for STEM major retention (Whalen & Shelley II, 2010; Geisinger & Raman, 2013). Furthermore, motivational variables such as students' intelligence mindset (i.e., basic beliefs of intelligence) and academic goal orientations (i.e., goals students focus on to reach certain achievement outcomes) have been shown to relate to and be predictive of students' academic achievement (Dweck & Leggett, 1988; Farrington et al., 2012). Using Dweck's socio-cognitive approach to motivation, the current study extends the work of growth- and fixed-intelligence mindsets to college engineering students.;Using data from 245 unique student participants from four junior- and senior-level electrical engineering courses, I investigated three issues. First, I examined and compared the relationships among student engineering majors' math and science intelligence mindsets and academic goal orientations during the beginning and end of the semester. I found that there were consistent and significant relationships during both time points among students' science- and math-growth intelligence mindsets and mastery-approach goals, students' science- and math-fixed intelligence mindsets and performance-avoidance goals, and students' science- and math-fixed intelligence mindsets and performance-approach goals. There were also some significant relationships among students' science- and math-fixed intelligence mindsets and mastery-approach goals at the end of the semester. There were, however, no significant differences between any of the correlations between students' intelligence mindsets and goal orientations at the beginning and end of the semester. Second, I investigated students' math and science intelligence mindsets across four time points throughout a semester to see if there were any significant differences across time and across the four courses. I found a significant difference across time for students' science-growth and math-growth intelligence mindsets, specifically a decrease in scores between time point 2 and time point 4. Third, I examined the potential predictive relationship of students' course exam grades on their end-of-semester science- and math-intelligence mindset. Exam 1 grades significantly predicted students' end-of-semester math-growth intelligence mindsets. Results and future research are discussed, as well as implications from these findings. |
