Random Forest is the best classifier for discriminating trained from untrained VR trainees¶

Claim · All claims
Evidence · 1 study · associational r2 · q2
Currency · newest study 2020 · not yet checked against its sources · not reviewed by a person · overdue for a recheck since 2023: tool and model accuracy changes quickly

Subclaims¶

q2 i? Among several classifiers evaluated, Random Forest performed best at discriminating trained from untrained students. → 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

Classifier comparison reported in the abstract indicates "the Random Forest algorithm standing out" among several classifiers used to build the student model from VRTS trace data.

"The results show that the model can discriminate between trained and untrained students, the Random Forest algorithm standing out."

Discussion¶

Related Claims¶

• A learning analytics student model discriminates between trained and untrained students in VR lineworker training maneuvers — possibly the same claim (merge candidate)
• Random forest algorithms yielded the best classification performance in K–8 MMLA studies comparing multiple machine learning models — related
• A random forest classified flagged versus non-flagged word problems with the best accuracy of five tested models (AUC = 0.75) — related
• CatBoost achieved the best classification performance among tested algorithms for predicting course completion risk (F-measure .77, accuracy 78%, AUC .87) — reports the opposite