Using multilevel modeling to evaluate science literacy and technology course of the Indonesian non-science students

TL;DR

This study uses multilevel modeling to evaluate how non-science undergraduates in Indonesia perform in a science literacy and technology course, considering nested departmental and faculty influences.

Contribution

It applies multilevel modeling to assess the impact of demographic and institutional factors on science literacy learning among non-science students.

Findings

Demographic factors had minimal impact on student performance.

Department and faculty settings significantly influenced affective learning factors.

Multilevel modeling effectively accounts for nested data structures in educational assessment.

Abstract

Science literacy is being fostered by science education community including the Indonesian education system. Science literacy and technology course has been designed and implemented to strengthen the national initiative empowering scientifically literate Indonesian society. This paper is intended to evaluate to what degree this course can be performed by non-science undergraduate students (N = 160) considering the nested structure of students' department and faculty setting. Multilevel modeling thus was employed to conduct the analysis dealing with this nature. The first level analysis involved students' performance and affective attribute measured using demonstrated science literacy assessment (SLA-D) and motivational beliefs (SLA-MB) respectively. The subsequent level analysis comprised demographic factors gathered from institutional record. Findings demonstrated that the impact of demographic factor to the students' performance on science literacy was not substantial. Different setting of students' department and faculty level drove the association between affective factor and learning process toward science literacy course substantially. Multilevel approach has controlled the equitable student assessment within the nature of students' data structure. This paper suggests many implications to open ideas regarding educational data analysis and to examine the effectiveness of science literacy course for the higher institution specifically for non-science majors.