KoroT-3E:增强复杂计算机科学概念记忆的个性化音乐记忆工具

Xiangzhe Yuan, Jiajun Wang, Siying Hu, Andrew Cheung, Zhicong Lu
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摘要

随着对计算机科学(CS)技能需求的增长,掌握基础概念对于初学者来说至关重要,但也极具挑战性。为了应对这一挑战,我们推出了 KoroT-3E,这是一个基于人工智能的系统,可以创建个性化的音乐记忆法,从而增强对计算机科学概念的记忆和理解。KoroT-3E 使用户能够将复杂的概念转化为可记忆的歌词,并根据自己的音乐喜好创作旋律。我们进行了半结构式访谈(n=12),以调查为什么新手学习者发现记忆和理解 CS 概念具有挑战性。调查结果与建构主义学习理论相结合,确定了我们的初步设计,并在咨询 CS 教育专家后对其进行了改进。实证实验(36 人)显示,使用 KoroT-3E 的学习者(18 人)的成绩明显优于对照组(18 人),他们的记忆效率得到提高,学习动力增强,并获得了积极的学习体验。这些发现证明了将多模态生成式人工智能融入计算机科学教育以创造个性化和交互式学习体验的有效性。
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KoroT-3E: A Personalized Musical Mnemonics Tool for Enhancing Memory Retention of Complex Computer Science Concepts
As the demand for computer science (CS) skills grows, mastering foundational concepts is crucial yet challenging for novice learners. To address this challenge, we present KoroT-3E, an AI-based system that creates personalized musical mnemonics to enhance both memory retention and understanding of concepts in CS. KoroT-3E enables users to transform complex concepts into memorable lyrics and compose melodies that suit their musical preferences. We conducted semi-structured interviews (n=12) to investigate why novice learners find it challenging to memorize and understand CS concepts. The findings, combined with constructivist learning theory, established our initial design, which was then refined following consultations with CS education experts. An empirical experiment(n=36) showed that those using KoroT-3E (n=18) significantly outperformed the control group (n=18), with improved memory efficiency, increased motivation, and a positive learning experience. These findings demonstrate the effectiveness of integrating multimodal generative AI into CS education to create personalized and interactive learning experiences.
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