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.

随着工程和制造部门将其过程转换为数字企业的过程，未来的设计师和工程师必须接受培训，以保证在产品的整个生命周期中创建和使用的数字模型的质量。事实证明，形成性培训方法，特别是那些基于在线准则的培训方法，对于改善CAD建模实践和相应结果的质量非常有效。但是，有效使用形成性规则以提高性能必须考虑两个主要因素：对规则的正确理解和准确的自我评估。在本文中，我们通过提出基于自我评估的CAD培训来开发这些因素，这些培训是通过具有丰富适应性资源的在线形成性规则进行自我评估的。我们分析了自我评估数据，例如花费的时间，评估学员与讲师之间的差异，或对14种不同的CAD考试使用可适应的资源。结果表明，在没有任何激励的情况下使用资源会更有效。通过质量标准对评估进行比较可以帮助确定在学习过程中学员可能仍不清楚的问题。这些结果可以指导自培训流程和工具的新策略的定义，这些策略可以有助于模型库工程环境中所需的更高质量的结果和CAD实践。通过质量标准对评估进行比较可以帮助确定在学习过程中学员仍然不清楚的问题。这些结果可以指导自培训过程和工具的新策略的定义，这些策略可以有助于模型库工程环境中所需的更高质量的结果和CAD实践。通过质量标准对评估进行比较可以帮助确定在学习过程中学员可能仍不清楚的问题。这些结果可以指导自培训流程和工具的新策略的定义，这些策略可以有助于模型库工程环境中所需的更高质量的结果和CAD实践。