CAD training for digital product quality: a formative approach with computer-based adaptable resources for self-assessment
[EN] As the engineering and manufacturing sectors transform their processes into those of a digital enterprise, future designers and engineers must be trained to guarantee the quality of the digital models that are created and consumed throughout the product's lifecycle. Formative training...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/197968 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/197968 |
| Access Level: | acceso abierto |
| Palabra clave: | Model-based enterprise CAD model Enhanced quality E-rubric Adaptable resource Formative self-assessment EXPRESION GRAFICA EN LA INGENIERIA |
| Sumario: | [EN] As the engineering and manufacturing sectors transform their processes into those of a digital enterprise, future designers and engineers must be trained to guarantee the quality of the digital models that are created and consumed throughout the product's lifecycle. Formative training approaches, particularly those based on online rubrics, have been proven highly effective for improving CAD modeling practices and the quality of the corresponding outcomes. However, an effective use of formative rubrics to improve performance must consider two main factors: a proper understanding of the rubric and an accurate self-assessment. In this paper we develop these factors by proposing CAD training based on self-assessment through online formative rubrics enriched with adaptable resources. We analyzed self-assessment data, such as time spent, scoring differences between trainee and instructor or use of the adaptable resources, of fourteen different CAD exams. Results show that resources are more effective when used without any incentives. The comparison of assessments by quality criterion can facilitate the identification of issues that may remain unclear to trainees during the learning process. These results can guide the definition of new strategies for self-training processes and tools, which can contribute to the higher-quality outcomes and CAD practices that are required in model-bases engineering environments. |
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