An Ontology-Based Framework for Psychological Monitoring in Education During the COVID-19 Pandemic.

Alia El Bolock,Slim Abdennadher,Cornelia Herbert

doi:10.3389/fpsyg.2021.673586

Alia El Bolock, Slim Abdennadher + Show 1 more

Open Access

https://doi.org/10.3389/fpsyg.2021.673586

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Abstract

BackgroundEspecially 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.MethodsThis 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.ResultsThe 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.DiscussionThe 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.

Highlights

ONTOLOGIESThe traits and predispositions of an individual are central to their self-regulation capabilities, and subsequently, their emotional state change in response to a specific situation (Gramzow et al, 2004; McCrae and Löckenhoff, 2010)
We show how CCOnto was modified and applied as an automated formal knowledge system to describe self-reported changes in student behavior associated with the current COVID-19 pandemic based on psychological theorizing and psychological data conceptualization as described in Herbert et al, 2021a
Building on existing ventures, including those mentioned above [e.g., Larsen and Hastings (2018), Maimone et al (2018), Dragoni et al (2020), and Hastings et al (2020)], in this manuscript, we present how an ontology is used to identify and structure university student behavior data during the first COVID-19 lockdown (Herbert et al, 2021a)

Summary

Introduction

The traits and predispositions of an individual are central to their self-regulation capabilities, and subsequently, their emotional state change in response to a specific situation (Gramzow et al, 2004; McCrae and Löckenhoff, 2010). One such situation is the recent outbreak of the COVID-19 virus and the lifestyle changes it entailed, including the lockdown most countries suffered from in the first wave to the physical and mental health concerns. This holds for all domains but even more so for the domain of student behavior in relation to education and psychological well-being

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Discussion

Conclusion