Exploring Changes in Computer Science Students' Implicit Theories of Intelligence Across the Semester

Abstract

Our study was based on exploring CS1 students' implicit theories of intelligence. Referencing Dweck and Leggett's [5] framework for implicit theories of intelligence, we investigated (1) how students' implicit theories changed over the course of a semester, (2) how these changes differed as a function of course enrollment and students' self-regulation profiles, and (3) whether or not implicit theories predicted standardized course grades and performance on a computational thinking knowledge test. For all students, there were significant increases in entity theory (fixed mindset) and significant decreases in incremental theory (growth mindset) across the semester. However, results showed that students had higher scores for incremental than entity theory of intelligence at both the beginning and end of the semester. Furthermore, both incremental and entity theory, but not semester change in intelligence theory, differed based on students' self-regulation profiles. Also, semester change in entity theory differed across courses. Finally, students' achievement outcomes were weakly predicted by their implicit theories of intelligence. Implications for student motivation and retention in CS and other STEM courses are also discussed.

Programming, Problem Solving, and Self-Awareness

“…Even when learners complete these courses, they still score poorly on tests of basic coding knowledge [33]. Worse yet, recent work has found that introductory CS courses can convince learners' that their abilities are fixed and cannot be improved with practice [21,49], deterring them from not only learning to code, but learning any new skill.…”

Investigating Differences in Wiki-based Collaborative Activities between Student Engagement Profiles in CS1

“…In conclusion, we have studied how student models determined by education research in the form of student motivated engagement profiles [6,11,14,16] can be used to predict student behaviors in technology-mediated instruction and e-learning environments. Considering 7 CS1 courses with 249 students spanning 3 semesters, we investigated how students with different engagement profiles behaved in an online, wiki-based CSCL system while performing collaborative creative thinking exercises.…”

“…A learned helpless student who is motivated to engage but ineffective in their learning and strategies, eventually causing them to lose motivation and begin to disengage. Recent studies have found these five profiles among undergraduate CS, engineering, and other STEM students taking CS1 courses [6,11,16]. In these CS courses, students adopting the strategic and knowledge builder profiles have higher course achievement and learning [11,16].…”

“…Profiles were derived using cluster analysis with SPSS. All instruments and procedures are fully described in Shell and Soh [16], Nelson et al [11], and Flanigan et al [6].…”

Growth Mindset in Computational Thinking Teaching and Teacher Training

“…By contrast, Flanigan et al [7] analyzed (without intervention) changes in students of CS courses across the semester, nding a signi cant increase in xed mindset and a signi cant decrease in growth mindset.…”