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Argument-Driven Inquiry Integrated Deep Learning and SDGs Issues: Strategic Solutions to Improve Students' Scientific Communication Skills
Abstract
Improving students' Scientific Communication Skills (SCS) is an essential demand in 21st century science education. However, the results of the assessment show that the SCS of junior high school students in Lampung Province is still relatively low, which is believed to be rooted in the dominance of conventional memorization-based learning methods. This study aims to test the effectiveness of the Argument-Driven Inquiry (ADI) model based on Deep Learning integrated with the Sustainable Development Goals (SDGs) in overcoming the competency gap. This study is a quasi-experimental using Pretest-Posttest Non-Equivalent Control Group Design on 64 grade VII students of SMPN 20 Bandar Lampung. Data were collected through scientific communication ability tests analyzed using the Independent Sample t-Test for improvement differences (N-Gain), as well as observation of syntax implementation and student response questionnaires. The results showed that there was a significant difference in SCS between the experimental class (ADI-DL-SDGs) and the control class (Conventional Learning). The experimental class achieved an average N-Gain = 0.62 (medium category), much higher than the control class (0.29 in the low category). Effect size analysis showed a high influence (1,830), confirming the model's substantial impact on SCS. The highest increase occurred in the indicators of scientific writing and representation information. Overall, the ADI-DL-SDGs model has proven effective in improving students' SCS in science learning through authentic investigative practices and strengthening evidence-based arguments in the context of sustainability issues.
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DOI: http://dx.doi.org/10.29300/ijisedu.v8i1.10058
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