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¶

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? All 14 LOs have at least six items in the combined bank, meeting simulation-based minimums for reliable DINA estimation. → Vy Le 2026 q2 i? Most items assessed one or two LOs, with only four items assessing four or more. → 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

Coverage analysis of the final Q-matrices across the three RBAs. "The three RBAs cover all 14 LOs with at least six items per LO," with kinematics and forces concentrated in FCI and FMCE and energy and momentum in EMCS.

"The three RBAs cover all 14 LOs with at least six items per LO, a minimum consistent with simulation studies showing that a ratio of at least four to six items per attribute supports reliable parameter estimation and classification accuracy in DINA-based analyses"

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

Item-complexity tally across the 101 combined items shows "38 items (38%) assessed a single LO" and another 39 two LOs. The authors view the single-LO and multi-LO mix as an asset for targeting isolated and integrated mastery.

"Across all assessments, 38 items (38%) assessed a single LO and another 39 items (39%) assessed two LOs. Twenty items (20%) assessed three LOs, while only four items assessed four or more LOs."

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
• Expert Q-matrix coding closely matched DINA-recovered structure, with only 2.7% of item-LO codings changed during validation — related
• Classification accuracy met or exceeded benchmarks for 19 of 22 LO-assessment combinations, with three EMCS energy LOs below the acceptable range — related
• Misspecification of theoretical diagnostic classification models and inaccurate Q-matrices impact classification accuracy — related