There is a growing body of literature on the predictors of student academic performance. The current study aims to extend this line of inquiry, and has linked stakeholders' participation, goal directness and classroom context with students' academic outcomes. Using the multistage sampling technique, the researchers collected cross-sectional data from 2,758 high school students. This study has employed regression analysis (simple linear regression and hierarchical linear regression modeling) to test the study hypotheses. The results revealed that learning context produces highest variance in students' engagement (R = 59.5%) and their academic performance (R = 42%). It is further evident that goal directness has the highest influence on students' academic performance (Std. β = 0.419) while learning climate of the classroom frequently affects their engagement (Std. β = 0.38) in studies. Results also illustrated that students' overall engagement (R = 99.1%: Model-5 = 0.849) and cognitive induction (R = 79.2%: Model-5 = 0.792) yield highest variance in their academic performance. Although stakeholders' participation causes low variance in students' academic performance but the role of parents, teachers, peers and students (themselves) remained significant. Further, student engagement mediates the direct relationship (s) of independent and outcomes variable. The findings of the present research could be potentially useful for policymakers and schools to ensure the elevation in students' engagement and their academic performance in studies.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9344009PMC
http://dx.doi.org/10.3389/fpsyg.2022.875174DOI Listing

Publication Analysis

Top Keywords

academic performance
28
goal directness
12
students' academic
12
students' engagement
12
participation goal
8
learning context
8
academic
8
student academic
8
student engagement
8
stakeholders' participation
8

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!