Indexed metadata

Assessing Self-Reported 3D Modeling Understanding and Engagement Among Vocational Teachers: A Rasch Analysis

Andri Setiyawan, Soeharto Soeharto, Zsolt Lavicza

Source record

Source: Crossref

Published: Sep 1, 2026

DOI: 10.1564/tme_v33.3.06

Open original source ↗

Source abstract

Technical and Vocational Education and Training (TVET) requires teachers who can model and communicate three-dimensional (3D) designs for engineering drawing, yet the validation of instruments to assess understanding of 3D modeling remains limited. This study aimed to validate the Self-Reported 3D Modeling Understanding and Engagement Scale (3DMUS) and examine the distribution of pre-service and in-service vocational teachers’ self-reported measures in the context of the engineering drawing development program. A cross-sectional survey of 278 Indonesian teachers was conducted using a 16-item instrument with three subscales, including 3D modeling understanding, orthographic projection, and engagement. The development program used Shapr3D as the 3D modeling platform. The Rating Scale Model (RSM) calibration estimated item difficulty and participants’ self-reported measures on the latent construct. It also evaluated item fit, category functioning, and targeting using Wright maps. All items showed acceptable fit with low standard errors. In general, the 3DMUS provides initial evidence of acceptable psychometric properties for measuring self-reported 3D modeling understanding and engagement in TVET. Future work should revise and extend the difficulty range, examine test-retest stability, and cross-validate the scale across institutions and learner populations.

Evidence graph

No public relationships recorded yet.

Integrity note: This page is a factual metadata record created by deterministic ingestion. It is not a claim that the work moves a mathematical frontier or has been independently verified.

Assessing Self-Reported 3D Modeling Understanding and Engagement Among Vocational Teachers: A Rasch Analysis — Mathematical Frontier Network