The Effect of Deep Learning-Based Guided Inquiry Learning Model on Students' Science Process Skills in Science Learning
DOI:
10.29303/jossed.v7i2.16159Published:
2026-10-26Downloads
Abstract
The deep learning-based inquiry model can train students to find their own information and knowledge based on scientific methods so that learning is more conscious, meaningful, and enjoyable. This study aims to determine the effect of the deep learning-based inquiry model on students' science process skills in science learning. The research method used is a descriptive and inferential approach. With a quasi-experimental research type and using a Two Group Pretest Postest Design. Before being given treatment, the sample was first given a Pretest (initial test) to measure students' science process skills before and after treatment, then given a Posttest (final test). Descriptive analysis includes calculating the average value (mean), standard deviation, and frequency distribution. Inferential analysis includes a normality test using the Shapiro-Wilk test, a homogeneity test using Levene's Test, and a hypothesis test using the Independent Samples T-Test. Based on the research data, the conclusion that can be drawn is that the average pretest and posttest scores of the control class increased by 11.3 points while the experimental class increased by 50.02 points. The application of science process skills has a significant impact on each indicator of science process skills measured, so that in general there is a significant influence of the application of the guided inquiry learning model based on deep learning on students' science process skills in the science subject of Electrical Circuits for class XI of SMP Negeri 8 Taniwel
Keywords:
Deep Learning Inquiry Model Science Process SkillsReferences
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