Implementation of Deep Learning through the 5E Cycle Learning Model on Population Problems Material
Abstract
The low achievement in Geography on the topic of population issues among students in Class XI-2 at SMA Islam Nusantara prompted the implementation of the Deep Learning approach through the 5E Cycle model. This study aims to describe the implementation of the Deep Learning approach through the 5E Cycle model in improving student achievement. The method used was Classroom Action Research (CAR) consisting of two cycles, which included the planning, implementation, observation, and reflection stages. Data collection was conducted using observation sheets and pre-test and post-test questions. The results showed a consistent increase in the average learning achievement score, from 74 in the pre-cycle to 81 in Cycle I, and reaching 87 in Cycle II. Additionally, the implementation of the Deep Learning approach increased by 0.86, while the implementation of the 5E Cycle model increased by 1.48. Significant differences were observed in Cycle II, where students demonstrated deeper analytical skills and higher levels of independent learning, though they were still in the adaptation phase. Future researchers are advised to implement this collaborative method across a broader scope of geography material to test its long-term effectiveness.
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