Classification accuracy met or exceeded benchmarks for 19 of 22 LO-assessment combinations, with three EMCS energy LOs below the acceptable range¶

Claim · All claims
Evidence · 1 study (2 entries) · design r2 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person · recheck by 2029: tool and model accuracy changes quickly

Subclaims¶

q2 i? Of 22 LO-assessment combinations with data, 13 achieved good and six achieved acceptable classification accuracy; Potential Energy, Conserve Energy, and Kinetic Energy fell below 0.80. → Vy Le 2026 q2 i? The three EMCS energy LOs scored below the acceptable accuracy threshold of 0.8. → Vy Le 2026 (2)

Evidence¶

Vy Le 2026¶

Vy Le, Jayson M. Nissen, Jason W. Morphew, Hua Hua Chang, and Ben Van Dusen. (2026). Mechanics Cognitive Diagnostic: Testing Fine-Grained Learning Objectives in Introductory Physics. https://arxiv.org/abs/2609.09584 doi:10.48550/arxiv.2609.09584

q2 · i? · design · r2

LO-level classification accuracy was computed from DINA classification probabilities on LASSO posttest data, against benchmarks for low-stakes formative assessment (≥ 0.9 good, ≥ 0.8 acceptable). "13 achieved good classification accuracy" and six acceptable.

"Of those 22, 13 achieved good classification accuracy ( ≥0.9) and six achieved acceptable accuracy (0.80–0.90)."

Vy Le 2026 (2)¶

Vy Le, Jayson M. Nissen, Jason W. Morphew, Hua Hua Chang, and Ben Van Dusen. (2026). Mechanics Cognitive Diagnostic: Testing Fine-Grained Learning Objectives in Introductory Physics. https://arxiv.org/abs/2609.09584

q2 · i? · design · r2

Within the same classification-accuracy analysis of 22 LO-assessment combinations, "Three EMCS energy LOs fell below the acceptable range": Potential Energy (0.675), Conserve Energy (0.705), and Kinetic Energy (0.745).

"Three EMCS energy LOs fell below the acceptable range: Potential Energy (0.675), Conserve Energy (0.705), and Kinetic Energy (0.745)."

Discussion¶

Related Claims¶

• Higher item noise, shared items among conceptually nested energy LOs, and smallest sample size explain the EMCS energy LOs' low classification accuracy — related
• The 14-LO attribute structure improved DINA model fit over MCD-v1's four-skill structure for all three RBAs — related
• DINA models fit FCI and EMCS posttest data well but fit FMCE data only marginally — related
• The combined FCI, FMCE, and EMCS item bank covers all 14 learning objectives with at least six items each, mostly single- or two-LO items — related
• Misspecification of theoretical diagnostic classification models and inaccurate Q-matrices impact classification accuracy — a broader claim this one bears on