The Effect of Deep Learning Approach Assisted by Quizizz on Students’ Learning Achievement in Hydrocarbon Topics
DOI:
https://doi.org/10.33394/hjkk.v14i4.20844Keywords:
Deep Learning, Learning Outcomes, Hydrocarbons, QuizizzAbstract
Low student learning outcomes regarding the topic of hydrocarbons stem from the abstract nature of the concepts and a learning process still dominated by the lecture method. This study aimed to determine the effect of a Deep Learning approach supported by Quizizz media on student learning outcomes regarding hydrocarbons at MAN Dairi. The study employed a quantitative approach using a quasi-experimental design specifically, a pretest-posttest control group design. The sample consisted of 62 students divided into an experimental group 31 students and a control group 31 students, selected via random cluster sampling. The experimental group was taught using the Deep Learning approach supported by Quizizz, while the control group was taught using Direct Instruction. The research instrument was a 20-item learning achievement test that met validity and reliability criteria. Data were analyzed using the N-Gain test, normality test, homogeneity test, and hypothesis testing via the t-test. The results showed that the average N-Gain was 0.79 for the experimental group and 0.73 for the control group; both fell into the high category. However, the improvement in student learning outcomes in the experimental group was statistically higher than in the control group. The t-test results indicated a significance value of Sig < 0.05, confirming a significant difference between the two groups. Based on these findings, it can be concluded that the Deep Learning approach supported by Quizizz media has a significant effect on improving student learning outcomes regarding hydrocarbons at MAN Dairi.
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