Ünlü Ali
School of Social Sciences and Technology, Technical University of Munich, Munich, Germany.
Front Psychol. 2023 Jan 25;13:993660. doi: 10.3389/fpsyg.2022.993660. eCollection 2022.
In self-determination theory (SDT), multiple conceptual regulations of motivation are posited. These forms of motivation are especially qualitatively viewed by SDT researchers, and there are situations in which combinations of these regulations occur. In this article, instead of the commonly used numerical approach, this is modeled more versatilely by sets and relations. We discuss discrete mathematical models from the theory of knowledge spaces for the combinatorial conceptualization of motivation. Thereby, we constructively add insight into a dispute of opinions on the unidimensionality vs. multidimensionality of motivation in SDT literature. The motivation order derived in our example, albeit doubly branched, was approximately a chain, and we could quantify the combinatorial details of that approximation. Essentially, two combinatorial dimensions reducible to one were observed, which could be studied in other more popular scales as well. This approach allows us to define the distinct, including even equally informative, gradations of any regulation type. Thus, we may identify specific forms of motivation that may otherwise be difficult to measure or not be separable empirically. This could help to dissolve possible inconsistencies that may arise in applications of the theory in distinguishing the different regulation types. How to obtain the motivation structures in practice is demonstrated by relational data mining. The technique applied is an inductive item tree analysis, an established method of Boolean analysis of questionnaires. For a data set on learning motivation, the motivation spaces and co-occurrence relations for the gradations of the basic regulation types are extracted, thus, enumerating their potential subforms. In that empirical application, the underlying models were computed within each of the intrinsic, identified, introjected, and external regulations, in autonomous and controlled motivations, and the entire motivation domain. In future studies, the approach of this article could be employed to develop adaptive assessment and training procedures in SDT contexts and for dynamical extensions of the theory, if motivational behavior can go in time.
在自我决定理论（SDT）中，提出了多种动机的概念性调节方式。SDT研究者尤其从质的角度看待这些动机形式，并且存在这些调节方式相互组合的情况。在本文中，我们并非采用常用的数值方法，而是用集合和关系对其进行更通用的建模。我们讨论知识空间理论中的离散数学模型，用于动机的组合概念化。由此，我们建设性地深入探讨了SDT文献中关于动机的一维性与多维性的观点之争。我们例子中得出的动机顺序虽然是双分支的，但大致是一个链，并且我们能够量化该近似的组合细节。本质上，观察到两个可简化为一个的组合维度，这在其他更流行的量表中也可以进行研究。这种方法使我们能够定义任何调节类型的不同等级，包括同样具有信息性的等级。因此，我们可以识别出可能难以测量或在经验上无法区分的特定动机形式。这有助于消除该理论在区分不同调节类型的应用中可能出现的不一致性。关系数据挖掘展示了在实践中如何获得动机结构。所应用的技术是归纳项目树分析，这是一种成熟的问卷布尔分析方法。对于一个关于学习动机的数据集，提取了基本调节类型等级的动机空间和共现关系，从而列举出它们潜在的子形式。在那个实证应用中，基础模型是在自主和受控动机的内在、认同、内摄和外部调节以及整个动机领域中分别计算的。在未来的研究中，如果动机行为可以随时间变化，本文的方法可用于在SDT背景下开发适应性评估和训练程序以及该理论的动态扩展。
