Published

Prediction of Elementary School Student Performance and Writing Interest Competency Based on Learning Engagement: Analysis with Binary Logistic Regression

Authors

1

Sofie Savitri

Institut Prima Bangsa

2

Anasya Putri Febgiyo

Institut Prima Bangsa

3

Intan Nurfadillah

Institut Prima Bangsa

Abstract

This study aimed to predict the influence of age, grade level, and gender on learning engagement, writing interest competencies, and academic achievement of elementary school students. This study used a non-experimental quantitative approach with data collection techniques through structured observation and questionnaires, involving 240 elementary school students in the Cirebon City area. Data analysis was carried out using binary and multinomial logistic regression as well as Partial Least Squares Structural Equation Modeling (PLS-SEM) modeling through SmartPLS and SPSS software. The results showed that grade level was a significant predictor of writing interest competency and learning engagement while age and gender did not show a significant influence. In addition, learning engagement has a very strong positive impact on writing interest, and writing interest competency significantly affects academic achievement indicating the mediating role of writing interest in the relationship between engagement and learning outcomes. These findings confirm the importance of learning approaches that are able to increase students' active engagement to support the development of writing skills and academic achievement. This study contributes to educational practice by providing an empirical basis for the development of more contextual and interest-based learning strategies at the primary school level.

Publication Info

Volume / Issue
Vol. 2, No. 1
Year
2025
Pages
1-13
Submitted
06 May 2025
Published
01 November 2025

Original Article

View this article on the original journal website for additional features and citation options.

View in OJS

Share

Publication History

Transparent editorial process timeline

Submitted

06 May 2025

Sent to Review

07 May 2025

Review Completed

15 May 2025

Review Completed

16 May 2025

Revisions Required

21 May 2025

Accepted

11 Jun 2025

Sent to Production

08 Jul 2025

Published

01 Nov 2025