Young people’s skills and attitudes must be observed, studied and evaluated in order to create appropriate models that would serve an educational purpose. In this paper, we propose a decision-making model with the aim to detect certain attitudinal and behavioral patterns of actively engaged young people. The data used in this research resulted from a questionnaire drawn up by a group of researchers from six European countries with the aim to investigate the youth’s awareness about the Sustainable Development Goals and their engagement as active agents of development and change at regional level. For the purpose of this study, we selected the regional results obtained from administering this questionnaire in Dolj County (Romania) and La Rioja (Spain). We developed a neutrosophic model that determines the Mindchanger profile of the respondents based on a minimal set of questions, which is dependent on the inclusion or exclusion of the nationality-specific traits. The resulting decisions were then compared with the respondents’ self-evaluations, yielding high precisions (more than 0.83) for all the investigated evaluation scenarios. Our results were significantly better than the ones provided by several machine learning models applied on the same set of data. A direct impact of our model is that it can be applied to questionnaires which include linguistic responses that express, among others, unclear or vague thoughts. Additionally, it offers the possibility to identify the minimal set of questions that impact the respondent’s answer choice to a target question.

必须观察、研究和评估年轻人的技能和态度，以便创建符合教育目的的适当模型。在本文中，我们提出了一种决策模型，旨在检测积极参与的年轻人的某些态度和行为模式。本研究使用的数据来自来自六个欧洲国家的一组研究人员起草的调查问卷，旨在调查青年对可持续发展目标的认识以及他们作为区域一级发展和变革的积极推动者的参与程度。出于本研究的目的，我们选择了在多尔日县（罗马尼亚）和拉里奥哈（西班牙）进行问卷调查所获得的区域结果。我们开发了一个中智模型，根据一组最少的问题来确定受访者的思维改变者概况，这取决于是否包含或排除特定国籍的特征。然后将得出的决定与受访者的自我评估进行比较，为所有调查的评估场景提供高精度（超过 0.83）。我们的结果明显优于应用于同一组数据的多个机器学习模型提供的结果。我们的模型的直接影响是它可以应用于问卷调查，其中包括表达不明确或模糊想法的语言反应。此外，它还可以识别影响受访者对目标问题的答案选择的最小问题集。