Peer Assessment of Student Performance in Designing Unplugged Coding Worksheets Based on Environmental Issues: A Pls-Sem Analysis

Nenden Permas Hikmatunisa; Fitri Nuraeni; Afridha Laily Alindra; Hafiziani Eka Putri; Tiara Yogiarni; Neneng Sri Wulan; Hisny Fajrussalam

doi:10.55681/nusra.v7i3.6508

Authors

Nenden Permas Hikmatunisa Program Studi Pendidikan Guru Sekolah Dasar, Universitas Pendidikan Indonesia, Indonesia
Fitri Nuraeni Program Studi Pendidikan Guru Sekolah Dasar, Universitas Pendidikan Indonesia, Indonesia
Afridha Laily Alindra Program Studi Pendidikan Guru Sekolah Dasar, Universitas Pendidikan Indonesia, Indonesia
Hafiziani Eka Putri Program Studi Pendidikan Guru Sekolah Dasar, Universitas Pendidikan Indonesia, Indonesia
Tiara Yogiarni Program Studi Pendidikan Guru Sekolah Dasar, Universitas Pendidikan Indonesia, Indonesia
Neneng Sri Wulan Program Studi Pendidikan Guru Sekolah Dasar, Universitas Pendidikan Indonesia, Indonesia
Hisny Fajrussalam Program Studi Pendidikan Guru Sekolah Dasar, Universitas Pendidikan Indonesia, Indonesia

DOI:

https://doi.org/10.55681/nusra.v7i3.6508

Keywords:

PLS-SEM, Computational Thinking, Environmental Issues, Pre-Service Teacher Education

Abstract

This study examines the structure and predictors of student performance in designing unplugged coding student worksheets integrated with environmental issues in a coding instruction course. This study employed a peer assessment approach to examine the interrelationships among Cognitive Engagement (CE), Behavioural Engagement (BE), Affective Engagement (AE), Academic Responsibility (AR), Collaboration (COL), and Communication (COM), drawing upon theories of student engagement, cooperative learning, computational thinking, peer assessment, and self-regulated learning. A total of 100 pre-service elementary school teachers enrolled in the course participated as respondents. Data were collected using a 12-item Likert-scale peer assessment rubric and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS4. Results of the outer model confirmed convergent validity (outer loadings ≥ 0.70; AVE ≥ 0.50) and composite reliability (CR ≥ 0.843). The inner model revealed that Affective Engagement significantly predicted Academic Responsibility (β = 0.606, T = 3.845, p < 0.001) with a large effect size (f² = 0.375), and Behavioral Engagement significantly predicted Collaboration (β = 0.571, T = 3.407, p = 0.001) with the largest effect size (f² = 0.598). The model explained 55.2%, 72.7%, and 67.3% of the variance in Academic Responsibility, Collaboration, and Communication, respectively, with a GoF index of 0.78, indicating a strong overall model fit. These findings highlight the pivotal role of affective and behavioural dimensions of engagement in shaping collaborative competence and academic responsibility among pre-service teachers engaged in computational thinking design tasks.

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