Number of generated solution methods is the strongest measured predictor of posttest learning from Productive Failure in both topics¶

Claim · All claims
Evidence · 1 study (2 entries) · associational r2 · q3
Currency · newest study 2023 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q3 i? In the ratios unit, number of RSMs significantly predicted posttest performance (F(1, 107) = 62.589, p < 0.001). → Manu Kapur 2023 q3 i? In the variance unit, number of RSMs was the only significant predictor of posttest performance (F(1, 100) = 105.518, p < 0.001). → Manu Kapur 2023 (2)

Evidence¶

Manu Kapur 2023¶

Manu Kapur, Janan Saba and Ido Roll. (2023). Prior math achievement and inventive production predict learning from productive failure. npj Science of Learning 8:15. https://doi.org/10.1038/s41539-023-00165-y

q3 · i? · associational · r2

ANCOVA on ratios-posttest scores with school as factor and PSLE math grade, pretest, and number of RSMs as covariates. Number of RSMs was significant; the authors state "the greater the number of RSMs generated, the better the learning from PF outcomes".

"The analysis of the ratios-posttest revealed that both the number of RSMs, F(1, 107) = 62.589, p < 0.001, and PSLE math grade, F(1, 107) = 4.436, p = 0.032, had significant effects on the posttest performance."

Manu Kapur 2023 (2)¶

Manu Kapur, Janan Saba and Ido Roll. (2023). Prior math achievement and inventive production predict learning from productive failure. npj Science of Learning 8:15. https://doi.org/10.1038/s41539-023-00165-y

q3 · i? · associational · r2

ANCOVA on variance-posttest scores: only the number of RSMs significantly predicted performance; PSLE math score, school, and topic-specific prerequisite knowledge were not associated with posttest scores.

"The analysis of the variance-posttest revealed a significant effect only on the number of RSMs, F(1, 100) = 105.518, p < 0.001."

Discussion¶

Related Claims¶

• Diversity of student-generated representations and solution methods is significantly correlated with learning from PF — related
• Number of canonical components in students' PF solutions correlates with conceptual knowledge, but the range of distinct components does not — related
• Prior math achievement has at most a small effect on learning from Productive Failure, and topic-specific prerequisite knowledge has none — related