Numerous factors that influence students’ academic performance involve issues beyond the individuals’ control, such as national policies, government initiatives and university resources among many others. Even if students are aware of these factors, addressing them may be unfeasible. Identifying causes within students’ control could both improve students’ understanding of these factors as well as enabling students to independently deal with related issues. This paper proposes a student-controllable learning factor model that combines the perspectives of Psychology, Self-responsibility, Sociology, Communication, Learning and Health & wellbeing (PS2CLH). The proposed model used qualitative methods to identify underlying aspects affecting academic achievement and selected controllable factors. This research reports on the outcomes of the employment of the PS2CLH model to predict student performance. Initially, data is collected through a self-evaluative web-based questionnaire. Each student’s past performance and factors affecting this are then quantified. This study reveals the impact of students’ controllable factors on student achievement. The PS2CLH model test results indicated 94% accuracy of successful prediction of the student performance based on the proposed PS2CLH model. The importance of “establishing and achieving personal goals” was higher than “stress”, “learning room” and “grammar and vocabulary” among other factors. This research raised participant students’ awareness of PS2CLH perspectives, which helped them manage factors affecting academic performance more effectively. Consequently, most of the students have enhanced their academic performance by addressing these critical factors. However, due to the limitations of the current sample data, the PS2CLH model will be further monitored for various applications.
Arlindo Almada, London Metropolitan University, United Kingdom
Qicheng Yu, London Metropolitan University, United Kingdom
Preeti Patel, London Metropolitan University, United Kingdom
Stream: Learning Experiences
This paper is part of the ACE2019 Conference Proceedings (View)
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