Profile and course variables, along with specific maneuver steps, are the most relevant features for trainees' final performance¶

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

Subclaims¶

q2 i? Feature importance analysis using the best classifier reveals profile and course variables plus specific maneuver steps as most relevant to final performance. → Santamaría Bonfil 2020

Evidence¶

Santamaría Bonfil 2020¶

Santamaría Bonfil, G., Ibáñez Espiga, M. B., Pérez Ramírez, M., Arroyo Figueroa, G., & Martínez Álvarez, F. (2020). Learning analytics for student modeling in virtual reality training systems: Lineworkers case. Computers & Education, 151. https://doi.org/10.1016/j.compedu.2020.103871

q2 · i? · associational · r2

A Feature Importance Analysis was carried out "using the best classifier" to understand the impact of variables on trainees' final performances in the VRTS lineworker training dataset.

"The feature importance analysis revealed that the most relevant features regarding the trainees' final performance were profile and course variables along with specific maneuver steps."

Discussion¶

Related Claims¶

• A learning analytics student model discriminates between trained and untrained students in VR lineworker training maneuvers — a broader claim this one bears on
• Episode-composition and transition features contribute predictive process evidence beyond semantics, with Epi.+Tran. reaching 0.136 permutation importance versus 0.084 for Sem. in the Both (Role) setting — related