Bolock Alia El, Abdennadher Slim, Herbert Cornelia
Department of Applied Emotion and Motivation Psychology, Institute of Psychology and Education, Ulm University, Ulm, Germany.
Department of Computer Science, Faculty of Media Engineering and Technology, German University in Cairo, Cairo, Egypt.
Front Psychol. 2021 Jul 22;12:673586. doi: 10.3389/fpsyg.2021.673586. eCollection 2021.
Especially in the current crisis of the COVID-19 pandemic and the lockdown it entailed, technology became crucial. Machines need to be able to interpret and represent human behavior, to improve human interaction with technology. This holds for all domains but even more so for the domain of student behavior in relation to education and psychological well-being.
This work presents the theoretical framework of a psychologically driven computing ontology, CCOnto, describing situation-based human behavior in relation to psychological states and traits. In this manuscript, we use and apply CCOnto as a theoretical and formal description system to categorize psychological factors that influence student behavior during the COVID-19 situation. By doing so, we show the added value of ontologies, i.e., their ability to automatically organize information from unstructured human data by identifying and categorizing relevant psychological concepts.
The already existing CCOnto was modified to automatically categorize university students' state and trait markers related to different aspects of student behavior, including learning, worrying, health, and socially based on psychological theorizing and psychological data conceptualization.
The paper discusses the potential advantages of using ontologies for describing and modeling psychological research questions. The handling of dataset completion, unification, and its explanation by means of Artificial Intelligence and Machine Learning models is also discussed.
尤其是在当前新冠疫情危机及其带来的封锁措施下，技术变得至关重要。机器需要能够解读和呈现人类行为，以改善人类与技术的交互。这适用于所有领域，但在学生行为与教育及心理健康相关的领域更是如此。
本文介绍了一种心理驱动的计算本体CCOnto的理论框架，该框架描述了与心理状态和特质相关的基于情境的人类行为。在本手稿中，我们将CCOnto用作理论和形式化描述系统，对新冠疫情期间影响学生行为的心理因素进行分类。通过这样做，我们展示了本体的附加值，即它们通过识别和分类相关心理概念来自动组织来自非结构化人类数据的信息的能力。
对现有的CCOnto进行了修改，以根据心理学理论和心理数据概念化，自动对与学生行为不同方面（包括学习、担忧、健康和社交）相关的大学生状态和特质标记进行分类。
本文讨论了使用本体来描述和建模心理学研究问题的潜在优势。还讨论了通过人工智能和机器学习模型处理数据集完成、统一及其解释的问题。
