ABSTRACT
Due to the impact of the COVID-19 pandemic on the education systems, it becomes important to understand and analyze the perception of e-learning management systems. One essential learning management system (LMS) is Moodle. It is being recently implemented at the University of Warith Al-Anbiyaa (UOWA), Iraq and integrated with Google Meet to serve as a complete e-learning environment. This chapter uses technology acceptance model (TAM) to help the university decision-makers in predicting the behavioural intention to use Moodle. The core TAM model and other external variables like perceived enjoyment and perceived self-efficacy were examined. A quantitative research approach was implemented to perform the survey with a questionnaire to obtain the required data from 164 lecturers at the university. The data analysis processes reveal that lecturers see Moodle as easy to use and enjoyable, and they are able to master its capability easily. The obtained results have confirmed the TAM model's importance in understanding and predicting the acceptance of Moodle by UOWA lecturers. © 2023, IGI Global.
ABSTRACT
The global pandemic (Covid-2019) has severely affected all aspects of our life and even changed the way we live and work. As the pandemic outspread, healthcare professionals need urgent interventions to control the harmful consequences. Encountering such a crisis makes them more prone to negative psychological ramifications in their work environment, making them unable to provide the proper support. Burnout is the most negative feeling increased among healthcare professionals while compacting the virus. One essential move to remedy the impacts of burnout is understanding its determinants and their causal relationships. This paper addresses the design of a computational (cognitive) agent model of burnout for healthcare professionals using a temporal-causal network model. Several determinants of burnout with their causal relationships were identified from the literature and formalised to construct the proposed cognitive agent model. In addition, different simulation experiments were implemented to obtain a clear insight into the causal relationships among burnout determinants and those experiments are exhibited similar behaviours to exiting literature. Furthermore, the developed model was evaluated using two different methods: mathematical analysis to prove its implementation was achieved right and automated logical verification to check several properties as shown in the literature to confirm that the suitable model was built. The obtained cognitive agent model could be helpful to develop a covid-19-aware analytics software agent that can monitor healthcare professionals' mental health © 2022, International Journal of Intelligent Engineering and Systems.All Rights Reserved.