In the IVR-based PVAS case study, most student teachers identified misbehaving students in all events, but a quarter made 10 or more perception errors¶

Claim · All claims
Evidence · 1 study · design r3 · q2
Currency · newest study 2025 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? In the implementation case study, 15 of 24 student teachers (62.5%) correctly noted misbehaving students in all seven classroom events, while six participants (25%) made 10 or more errors before correctly identifying target students. → Liu 2025

Evidence¶

Liu 2025¶

Liu, T.-C., & Lin, Y.-C. (2025). Development and implementation of an IVR-based assessment system for student teachers' professional vision. Education Tech Research Dev. https://doi.org/10.1007/s11423-025-10463-4

q2 · i? · design · r3

Implementation case study in which 24 student teachers completed the IVR-based PVAS perception subtask across seven classroom events, with responses collected via log file and screen recorder. The article reports that 'Most of the participants (15, 62.5%) correctly noted misbehaving students in all the classroom events.' Six participants made 10 or more errors, the highest count being 25.

"Most of the participants (15, 62.5%) correctly noted misbehaving students in all the classroom events. However, the assessment processes recorded by the system also revealed that the participants made several errors before correctly pointing out the target students"

Discussion¶

Related Claims¶

• Perception was hardest in Event 3, which involved the least noticeable distracted behaviour (looking out a window) — related
• Student teachers made the likely interpretation in only about half of post-perception interpretations in the IVR-based PVAS case study — related
• Cognitive load during the IVR-based PVAS task was generally low despite unsatisfactory perception and interpretation performance — related
• Perception performance was superior for independent-study events (6–7) than for lecture events (1–5) — related
• Experts detect and correct small changes before events become full-blown, while novices are distracted by possibly irrelevant phenomena — a broader claim this one bears on