Learning machine learning with young children: exploring informal settings in an African context

Figures & data

Table 1. The teaching session and the video data collected.

Settings	Participants (year old)	Groupings	Collected data from the use of the ML tool (min., including interview)
Location A
Peer teaching a computer with the support of a parent or familiar adult	Samal (5y) and Falil (3y) with their mother	Face detection	20:22
	Akin (8y), Grace (5y) and Tolu (7y) with a familiar adult	Face recognition	25:59
	Evelyn (8y), Babajide (9y), Salewa (12y) and familiar adult	Voice recognition	32:30
Location B
Peer teaching a computer with the support of a parent or familiar adult	Samuel (6y), Vincent (5y), Gloria (5y), Enemona (4y)	Face detection	35:49
	Treasure (13y), Deborah (10y),	Face recognition	18:50
	Jessica (9y), Victoria (8y), Goodluck (7y), and Joel (7y)	Voice recognition	29:00

Names have been changed to ensure anonymity.

Figure 1. Coding and definitions (Adapted from Vartiainen, Tedre, et al., 2020).

Figure 2. Co-teaching a teachable machine.

Figure 3. Children exploring GTM.

Figure 4. The first child teaches the machine a facial expression of sadness while the second child laughs at her sad look.

Figure 5. Children teaching the machine the facial expression of happiness by taking turns.

Figure 6. Co-teaching a teachable machine.

Figure 7. Learning the limits of the classifier that Akin trained.

Figure 8. Learning the limits of the classifier that Victoria trained.

Figure 9. Co-teaching a computer.

Figure 10. Co-teaching a computer.

Vartiainen, H., Tedre, M., & Valtonen, T. (2020). Learning machine learning with very young children: Who is teaching whom? International Journal of Child-Computer Interaction, 25, 100182. https://doi.org/10.1016/j.ijcci.2020.100182

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