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Motivational factors are one active area of research that aims to increase the inclusion of women in physics. One of these factors that has only recently gained traction in physics is intelligence mindset (i.e., the belief that intelligence is either innate and unchangeable or can be developed). We studied 781 students in calculus-based Physics 1 to investigate if their mindset views were separable into more nuanced dimensions, if they varied by gender/sex and over time, and if they predicted course grade. Confirmatory factor analysis was used to divide mindset survey questions along two dimensions: myself versus others and growth versus ability aspects of mindset. Paired and unpairedt-tests were used to compare mindset factors over time and between genders, respectively. Multiple regression analysis was used to find which mindset factors were the best predictors of course grade.

This study shows that intelligence mindset can be divided into four factors: My Ability, My Growth, Others’ Ability, and Others’ Growth. Further, it reveals that gender differences are more pronounced in the “My” categories than the “Others’” categories. At the start of the course, there are no gender differences in any mindset component, except for My Ability. However, gender differences develop in each component from the start to the end of the course, and in the My Ability category, the gender differences increase over time. Finally, we find that My Ability is the only mindset factor that predicts course grade.

These results allow for a more nuanced view of intelligence mindset than has been suggested in previous interview and survey-based work. By looking at the differences in mindset factors over time, we see that learning environments affect women’s and men’s intelligence mindsets differently. The largest gender difference is in My Ability, the factor that best predicts course grade. This finding has implications for developing future mindset interventions and opens new opportunities to eliminate classroom inequities.

 

Gender disparities in retention in pathways to science continue to vary widely by course. Undergraduates intending to study prehealth and premedicine often represent a majority of students enrolled in introductory science courses, contribute to a large number of eventual science degree earners, and are a population that typically includes a high number of women. However, gender differences in attrition, grades, and attitudes persist in the introductory science courses required by undergraduate preheath and premedical programs, particularly within the physical sciences (i.e., Chemistry and Physics). We use structural equation modeling to study 416 undergraduate students across multiple sections of an Algebra‐based Physics course, a common course on the prehealth and premedical track where large gender differences in grades, retention and competency beliefs have been documented. Our analysis focuses on identifying potential academic and attitudinal sources for gender differences in students' beliefs about their Physics abilities at the end of the course, and retention to the second physics course, which is often influenced by these competency beliefs. Results suggest that while men's ability beliefs in Physics are relatively stable and largely derived from early performance indicators, this is a smaller source of ability beliefs for women. Instead, women's ability beliefs are mediated during the course through their sense of belonging in Physics, and the extent to which they believe that Physics ability is fixed or malleable. These findings can inform the design of interventions in Physics courses that specifically target the development of ability beliefs for women intending medical careers.

 

A sizable body of research on instructional practices supports the use of worked examples for acquiring cognitive skills in domains such as mathematics and physics. Although examples are also important in the domain of programming, existing research on programming examples is limited. Program examples are used by instructors to achieve two important goals: to explain program behavior and to demonstrate program construction patterns. Program behavior examples are used to demonstrate the semantics of various program constructs (i.e., what is happening inside a program or an algorithm when it is executed). Program construction examples illustrate how to construct a program that achieves a specific purpose. While both functions of program examples are important for learning, most of the example-focused research in computer science education focused on technologies for augmenting program behavior examples such as program visualization, tracing tables, etc. In contrast, advanced technologies for presenting program construction examples were rarely explored. This work introduces interactive Program Construction Examples (PCEX) to begin a systematic exploration of worked-out program construction examples in the domain of computer science education. A classroom evaluation and analysis of the survey data demonstrated that the usage of PCEX examples is associated with better student's learning and performance. 

Educational robotics programs offer an engaging opportunity to potentially teach core computer science concepts and practices in K–12 classrooms. Here, we test the effects of units with different programming content within a virtual robotics context on both learning gains and motivational changes in middle school (6th–8th grade) robotics classrooms. Significant learning gains were found overall, particularly for groups introduced to content involving program flow, the structural logic of program execution. Relative gains for these groups were particularly high on items that require the transfer of knowledge to dissimilar contexts. Reaching units that included program flow content was also associated with greater maintenance of programming interest when compared with other units. Therefore, our results suggest that explicit instruction in the structural logic of programming may develop deeper transferrable programming knowledge and prevent declines in some motivational factors.