The article reports that classroom studies with block-structured NetLogo-based environments produced significant STEM and CT learning gains.¶

Claim · All claims
Evidence · 1 study · design r2 · q2
Currency · newest study 2020 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Prior classroom studies of block-structured modeling environments built on NetLogo demonstrated significant learning gains in STEM and CT domains. → Nicole M. Hutchins & Gautam Biswas & Ningyu Zhang & Caitlin Snyder & Ákos Lédeczi & Miklós Maróti 2020

Evidence¶

Nicole M. Hutchins & Gautam Biswas & Ningyu Zhang & Caitlin Snyder & Ákos Lédeczi & Miklós Maróti 2020¶

Nicole M. Hutchins & Gautam Biswas & Ningyu Zhang & Caitlin Snyder & Ákos Lédeczi & Miklós Maróti. (2020). Domain-Specific Modeling Languages in Computer-Based Learning Environments: a Systematic Approach to Support Science Learning through Computational Modeling. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-020-00209-z

q2 · i? · design · r2

The article reports, citing prior studies (e.g., Basu et al. 2017; Sengupta et al. 2012), that "Classroom studies conducted with these systems have produced very successful results". The gains are attributed to CTSiM, ViMap, and CT-STEM; no effect sizes are printed for this summary statement.

"Classroom studies conducted with these systems have produced very successful results, demonstrating significant learning gains in the STEM and CT domains (e.g., Basu et al. 2017; Sengupta et al. 2012), promoting collaborative problem solving skills"

Discussion¶

Related Claims¶

• Novice K-12 students face multiple documented difficulties when learning STEM and CT simultaneously through learning-by-modeling, including translating domain knowledge into computational form and debugging model behaviors. — related
• OLMs of various kinds have demonstrated significant learning gains — related