Most participating teachers produce ML-empowered scientific discovery teaching ideas of excellent, good, or fair quality after the TaL session.¶

Claim · All claims
Evidence · 1 study · design r2 · q2
Currency · newest study 2023 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Of 17 teachers posting teaching ideas, 15 reached excellent, good, or fair TCK quality levels and two demonstrated poor understanding. → Jingwan Tang 2023

Evidence¶

Jingwan Tang 2023¶

Jingwan Tang, Xiaofei Zhou, Xiaoyu Wan, Michael Daley, Zhen Bai. (2023). ML4STEM Professional Development Program: Enriching K-12 STEM Teaching with Machine Learning. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-022-00292-4

q2 · i? · design · r2

Rubric-based rating of teachers' posted teaching ideas after the TaL session; each idea was scored 0-2 on three criteria by two independent coders (Cohen's Kappa = 0.76). The article reports "15 participants reached excellent (N = 2), good (N = 8) or fair (N = 5)"; no effect size is printed.

"44 ML-empowered SD teaching ideas are collected from 17 participants. After evaluating the quality of ideas, 15 participants reached excellent (N = 2), good (N = 8) or fair (N = 5), and two demonstrated poor understanding of TCK."

Discussion¶

Related Claims¶

• Short-duration TPACK-based professional development improved teachers' ML knowledge and interest (ML4STEM) — a broader claim this one bears on
• Science teachers show higher TCK understanding levels than math teachers in the ML4STEM study. — related
• Teachers' post-TaL technology knowledge is not significantly associated with their technology content knowledge. — related