Abstract

The COVID-19 outbreak disrupted all aspects of people’s lives, including pedagogy and instruction at universities, where its impact was felt globally, and the Kingdom of Saudi Arabia is no exception. This paper discusses and compares the academic results during the conventional mode of learning (spring semester of 2019) and during the online mode of learning (spring semester of 2020), which was throughout the pandemic period. Specifically, the study considers two Saudi groups of fifty students who were enrolled in the Department of Health Information Management and Technology at the University of Hafr Al-batin in the eastern region of Saudi Arabia. The students’ accumulative marks in six first-year courses in two semesters were analyzed and compared by calculating the passing/failure percentages, descriptive statistics (mean, median, mode, standard deviation, variance, maximum, minimum, and range), and the p-value of an inferential t-test. Additionally, the students filled out a six-category survey about their experiences and level of satisfaction with online learning compared to the regular learning mode. It was concluded that more students passed with higher grades in the year 2020 in five theoretical courses. Differently, it was deduced that practical courses such as computer applications had similar passing averages in the two years; the course required students to submit continuous assignments and projects in a fixed time period. Additionally, the participants revealed in their filled online survey that they preferred many features of the remote learning mode and the online assessment methodology. They inclined toward online learning for its flexibility and effectiveness, even though they confessed to having less interaction and focus during online sessions. Thus, the results imply to authorities in the Ministry of Higher Education to carefully and gradually embed online teaching for selected subjects with exam validation measures such as time strict limits, question banks, randomizations, and other security features.

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