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Gender Differences in Self-efficacy for Programming Narrowed After a 2-h Science Museum Workshop.

Geneviève Allaire-DuquettePierre ChastenayThérèse BouffardSimon A BélangerOlivier HernandezMohamed Amine MahhouPatrick GirouxSophie McMullinEstelle Desjarlais
Published in: Canadian journal of science, mathematics and technology education = Revue canadienne de l'enseignement des sciences, des mathematiques et de la technologie (2022)
Many girls believe they have little natural ability in computer science and girls' perception of self-efficacy beliefs for programming is generally low. Offering engaging hands-on programming activities could be a beneficial strategy to increase girls' self-efficacy beliefs for programming since it has the potential to offer them exposure to mastery experiences. However, a programming workshop in a museum might not offer ideal settings to promote girls' mastery experiences in programming because of its short duration and how gender stereotypes may impact the participation in hands-on activities. In the research presented here, we explore how a science museum's introductory programming workshop focused on robotics can impact pupils' self-efficacy beliefs for programming related to mastery experiences, with a specific focus on girls. H1-Prior to the programming workshop, it is expected that girls' self-efficacy beliefs will be lower than boys'. H2-Boys generally have more positive experiences with STEM activities than girls, irrespective of experimental condition. Thus, following the workshop, we predict that girls' and boys' self-efficacy for programming will have increased, but that boy's self-efficacy beliefs will remain higher than girls'. In total, 172 pupils (94 girls) aged 10-14 years completed a Mastery Experiences in Programming questionnaire before and after taking part in a programming workshop. Our results show that after a 2-h programming workshop in a science museum, gender differences in self-efficacy for programming initially observed narrowed and even disappeared.
Keyphrases
  • mental health
  • public health
  • physical activity
  • machine learning
  • cross sectional