Design computational learning environments to treat seeming mistakes as expansion possibilities and surface material contributions¶

Strategy · All strategies
Evidence · 4 claims (4 for) · 2 studies (2 qualitative), q1–q2 · 0 of 2 report an effect size · 4 claims rest on one study

Description¶

The study's findings inform design decisions for computational learning environments built on human-material collaboration. The article recommends "valuing seeming mistakes as a possibility for expanding conceptual productions within the learning environment," as when the gap pattern's obstructed heddle reorganized the algorithm. It also guides design toward facilitating student awareness of how materials contribute to computational design, and toward studying what youth learn when they identify materials as collaborators.

Design Implications¶

Context¶

Requirements¶

• Learning environments that integrate tangible materials whose material constraints can reshape algorithmic activity, such as looms and fabric

Constraints¶

• Implications are drawn from a single qualitative study in one charter school setting, so transfer is framed as future research relevance

Target Learners¶

• middle school youth, especially those deciding whether to pursue STEM

Target Learning Goals¶

• learning about algorithms and collaborative computational production

Claims¶

• Algorithms developed over time through changes in youth-material collaborations, defining learning as physical expansion of algorithmic production [+W]
• Neither youth nor loom alone drove the algorithmic production and fabric output; both actively produced it collaboratively [+W]
• In weaving, performed algorithms emerged as repeated youth-material movements one dimension at a time, producing reliable fabric outputs [+W]
• The editorial reports that human-material interactions in fibre crafting produce algorithmic outcomes, with materials acting as collaborative agents (Keune) [+W]

Related Strategies¶

• Creating a Culture of Error
• Mistake-Friendly Classroom

Examples¶

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Key Sources¶

• Anna Keune. (2024). Learning within fiber-crafted algorithms: Posthumanist perspectives for capturing human-material collaboration. Intern. J. Comput.-Support. Collab. Learn. https://doi.org/10.1007/s11412-023-09412-1