KoroT-3E: A Personalized Musical Mnemonics Tool for Enhancing Memory Retention of Complex Computer Science Concepts

Xiangzhe Yuan, Jiajun Wang, Siying Hu, Andrew Cheung, Zhicong Lu
{"title":"KoroT-3E: A Personalized Musical Mnemonics Tool for Enhancing Memory Retention of Complex Computer Science Concepts","authors":"Xiangzhe Yuan, Jiajun Wang, Siying Hu, Andrew Cheung, Zhicong Lu","doi":"arxiv-2409.10446","DOIUrl":null,"url":null,"abstract":"As the demand for computer science (CS) skills grows, mastering foundational\nconcepts is crucial yet challenging for novice learners. To address this\nchallenge, we present KoroT-3E, an AI-based system that creates personalized\nmusical mnemonics to enhance both memory retention and understanding of\nconcepts in CS. KoroT-3E enables users to transform complex concepts into\nmemorable lyrics and compose melodies that suit their musical preferences. We\nconducted semi-structured interviews (n=12) to investigate why novice learners\nfind it challenging to memorize and understand CS concepts. The findings,\ncombined with constructivist learning theory, established our initial design,\nwhich was then refined following consultations with CS education experts. An\nempirical experiment(n=36) showed that those using KoroT-3E (n=18)\nsignificantly outperformed the control group (n=18), with improved memory\nefficiency, increased motivation, and a positive learning experience. These\nfindings demonstrate the effectiveness of integrating multimodal generative AI\ninto CS education to create personalized and interactive learning experiences.","PeriodicalId":501541,"journal":{"name":"arXiv - CS - Human-Computer Interaction","volume":"39 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Human-Computer Interaction","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.10446","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

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.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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
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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Equimetrics -- Applying HAR principles to equestrian activities AI paintings vs. Human Paintings? Deciphering Public Interactions and Perceptions towards AI-Generated Paintings on TikTok From Data Stories to Dialogues: A Randomised Controlled Trial of Generative AI Agents and Data Storytelling in Enhancing Data Visualisation Comprehension Exploring Gaze Pattern in Autistic Children: Clustering, Visualization, and Prediction Revealing the Challenge of Detecting Character Knowledge Errors in LLM Role-Playing
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1