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Lesson 8 of 10 · 7 min

Inclusion and gender balance

From the age of six, children associate robots and programming with boys. The way the activity is presented and the work organised matters.

In 2017, Master and colleagues questioned 96 six-year-olds in the United States. They already associated robotics and programming with boys, but not mathematics or science. After a short session in which they programmed a robot, the girls reported more interest in technology and more self-confidence than the girls in the control groups. On these measures, the gap with the boys disappeared. The stereotypes, however, did not change.

In 2016, Sullivan and Bers followed 45 children in the first years of school in the United States through eight weeks of robotics. Girls and boys did equally well on robotics and simple programming tasks. Boys did better on advanced tasks, such as loops that depend on a sensor. Zhang’s meta-analysis also notes a larger effect among boys. So it is worth watching who handles the robot and who looks on.

  • Roles that rotate at the signal: driver, navigator, reporter.
  • Pairs formed by the teacher for the whole sequence.
  • Varied challenges: delivering a message, helping a character, measuring, telling a story, not just races.
  • Varied role models: showing women and men who design robots.
  • Several ways into the activity: printed arrow cards for those who cannot read yet, large buttons for those who find handling difficult, instructions both spoken and written.

References

  1. Master, Cheryan, Moscatelli, Meltzoff (2017). Programming experience promotes higher STEM motivation among first-grade girls. Journal of Experimental Child Psychology 160, p. 92–106. doi.org/10.1016/j.jecp.2017.03.013
  2. Sullivan, Bers (2016). Girls, Boys, and Bots: Gender Differences in Young Children’s Performance on Robotics and Programming Tasks. Journal of Information Technology Education: Innovations in Practice 15, p. 145–165. doi.org/10.28945/3547
  3. Zhang, Luo, Zhu, Yin (2021). Educational Robots Improve K-12 Students’ Computational Thinking and STEM Attitudes: Systematic Review. Journal of Educational Computing Research 59(7), p. 1450–1481. doi.org/10.1177/0735633121994070
  4. CAST (2024). Universal Design for Learning Guidelines version 3.0. CAST, Lynnfield (MA). udlguidelines.cast.org/