Secondary Outcomes (explanation)
Confidence: We measure confidence using the questions and methodology proposed in Grosch et al. (2022). Students are asked whether they believe they have the ability to perform well in several professions - the same proposed above - related to STEM and non-STEM fields. Additionally, we will include a variable that captures the number of questions individuals believe they answered correctly after a maths quiz.
Stereotypical thinking: We propose three measurements to capture explicit, implicit stereotypical thinking, and social stereotypical thinking. Explicit measures are borrowed from the Grosch et al. (2022)’s questionnaire. Implicit measures have gained recognition for their capacity to predict outcomes related to maths achievement and engagement, surpassing the predictive capacity of explicit measures. These implicit measures are sensitive to constructs beyond the gender-science stereotype, making them valuable tools for uncovering additional factors that influence outcomes in STEM fields. The implicit stereotypical thinking is generally assessed by means of the Implicit Association Test (IAT) (Greenwald et al., 1998) and will be administrated to both classroom and remote teachers. We also capture this implicit stereotypical thinking for children with the same IAT but in a adapted version from Grosch et al. (2022) and Cvencek et al. (2011). Third, we will incorporate explicit questions about gender stereotypes in science, focusing on the perspective of peers. For instance, we will ask to children whether according to their classmates, males or female are more talented in mathematics?
Growth mindset: We suggest using the measurement approach developed by Bettinger et al. (2018). This instrument consists of several questions that assess fixed/growth mindset. First, students will be asked whether they believe intelligence can be changed. Second, they will be questioned about their perception of their own ability to improve their maths skills. Lastly, a question will be included that correlates a fixed mindset with the level of effort required. All these questions will be administered at the individual level but will be also specific to the gender (i.e., "Do you believe girls/boys can change their level of intelligence?").
Maths preferences: We propose to measure maths preferences using nine questions employed in the HTHT project carried out in 2021. These questions capture children’s preference for maths. To create a comprehensive measure, we will use Principal Component Analysis (PCA) to extract primary factors.
Language Preferences: We suggest measuring this variable by employing the same nine questions we use for mathematics but adapted to language. This approach enables us to assess diverse preferences in language and mitigates the potential influence of children perceiving the experiment as solely maths-related, which might alter their behavior. To create a comprehensive measure, we will use PCA and extract primary factors.
Maths performance: To examine whether interest in STEM is enhanced among individuals with lower maths performance, we require baseline information on maths performance. In this regard, it is important for us to discuss with Ceibal’s team the feasibility of creating a quiz consisting of no more than 10 questions that assesses maths skills.