The formative study revealed that novice computer science learners find it challenging to memorize and comprehend the vast and abstract nature of computer science concepts, logic, and formulas. To address this, the researchers designed KoroT-3E, a hybrid AI system that enables users to transform complex concepts into memorable lyrics and compose melodies that suit their musical preferences.
The system consists of three main components: lyrics generation, music generation, and music display. The lyrics generation module employs GPT-4 with prompt engineering to adapt concepts into easy-to-remember lyrics. The music generation module utilizes Suno to compose melodies based on the user-generated lyrics and preferred music style. Users can then play the customized musical mnemonics and save them for future review.
An empirical experiment (n=36) showed that the overall performance of the experimental group (n=18) using KoroT-3E was significantly better than the control group (n=18) in both short-term and long-term memory retention tests. The experimental group's average scores were consistently higher than the control group's, although only one of the results reached statistical significance.
Subsequent surveys and interviews revealed that participants found KoroT-3E easy to use and felt it enhanced their memory retention, improved memory efficiency, and increased their interest and motivation in learning. They also believed that KoroT-3E could be applied to a broader range of fields and user groups.
The study demonstrates the potential of generative AI-based mnemonic techniques, particularly in improving the learning of foundational concepts in computer science, and offers a novel approach to modernizing and advancing mnemonic strategies.
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arxiv.org
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by Xiangzhe Yua... ב- arxiv.org 09-17-2024
https://arxiv.org/pdf/2409.10446.pdfשאלות מעמיקות