Effect of IoT-Assisted Sociobiological Case-Based Learning on Students' Scientific Explanation Ability in Water Pollution

Irgi Indrawan; Jujun Ratnasari; Aa Juhanda

doi:10.61142/esj.v5i1.394

Authors

Irgi Indrawan Universitas Muhammadiyah Sukabumi https://orcid.org/0009-0002-9539-426X
Jujun Ratnasari Universitas Muhammadiyah Sukabumi https://orcid.org/0000-0003-0653-6511
Aa Juhanda Universitas Muhammadiyah Sukabumi https://orcid.org/0000-0001-5641-7292

DOI:

https://doi.org/10.61142/esj.v5i1.394

Keywords:

Internet of things, Scientific explanation, Sociobiological case-based learning, Water pollution

Abstract

This study investigated the effect of an Internet of Things (IoT)-assisted Sociobiological Case-Based Learning (SCBL) model on students' scientific explanation ability in the water pollution topic. Scientific explanation is essential for constructing evidence-based claims and logical reasoning, yet its development using authentic IoT-generated environmental data remains understudied. This study employed a quasi-experimental method using a non-equivalent control group design. The participants consisted of 40 students in Class X1 as the experimental group, who learned through the IoT-assisted SCBL model, and 32 students in Class X2 as the control group, who received Discovery Learning instruction. Scientific explanation ability was assessed using six essay questions based on the Claim–Evidence–Reasoning (CER) framework. The data were analyzed using the Shapiro–Wilk normality test, Levene's test for homogeneity of variance, and the Mann–Whitney U test. The experimental group achieved a higher normalized gain (N-gain = 0.68) than the control group (N-gain = 0.55), with both categorized as moderate. The Mann–Whitney U test revealed a significant difference between groups (Asymp. Sig. (2-tailed) = 0.035, p < 0.05) with a moderate effect size (r = 0.25). The greatest improvement in the experimental group was observed in the Reasoning indicator (N-gain = 0.73, high category) and the integrated CER indicator (N-gain = 0.71, high category). These findings indicate that integrating the SCBL model with real-time IoT sensor data provides an effective and evidence-based approach to enhancing students' scientific explanation ability in biology learning.

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