In order to ensure optimal teaching conditions for university students, attention should be brought to their personality traits and academic performance. In the paper Personality Questionnaires as a Basis for Improvement of University Courses in Applied Computer Science and Informatics, the authors Vladimir Ivančević, Marko Knežević, and Ivan Luković present the foundation for such an adaptation of the teaching process, supported by an analytical software solution (in its initial version).
The software solution presents two main components: a data warehouse for storing collected data and an analytical software tool (built using the Shiny framework). The data warehouse contains collected data about student academic performance and personality traits, while the analytical tool is a web application that retrieves data from the data warehouse or external CSV files matching the required structure and allows analysts to perform exploration and analysis of data concerning student performance and personality.A data warehouse schema segment for scores of university students on scales of personality questionnaires
In order for the study to take place, undergraduate students had been invited to participate in a workshop organized at the Faculty of Technical Sciences, University of Novi Sad, Serbia. In total, 24 students (19 males and 5 females) aged 19 to 20 responded and fully participated in the workshop. These students were given The Eysenck Personality Questionnaire-Revised (EPQ-R), which contained 12 items for each of the four contained scales: Psychoticism (P), Extraversion (E), Neuroticism (N), and Lie (L).
The EPQ–R data from the workshop were processed and loaded into the data warehouse. By using the new analytical tool, it is possible to inspect and analyze the collected personality data, which includes comparison of two data samples by scores on common scales.
By conducting this study, the authors aimed to show how personality questionnaires may be used to collect data about personality traits of students and how these data, together with data about academic performance of students, may allow the teacher to better understand the student population and perform some adaptations of the teaching process.
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