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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

TL;DR

KoroT-3E, an AI-based system that creates personalized musical mnemonics to enhance both memory retention and understanding of concepts in CS, demonstrates the effectiveness of integrating multimodal generative AI into CS education to create personalized and interactive learning experiences.

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.

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

TL;DR

KoroT-3E, an AI-based system that creates personalized musical mnemonics to enhance both memory retention and understanding of concepts in CS, demonstrates the effectiveness of integrating multimodal generative AI into CS education to create personalized and interactive learning experiences.

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.
Paper Structure (54 sections, 6 figures, 2 tables)

This paper contains 54 sections, 6 figures, 2 tables.

Table of Contents

  1. INTRODUCTION
  2. RELATED WORK
  3. Mnemonics
  4. Mnemonics in Education
  5. Mnemonics in HCI
  6. The Benefits of Music in Education
  7. CS Concepts Education in HCI
  8. FORMATIVE STUDY
  9. Data Collection and Analysis
  10. Challenges of Participants with a CS Background
  11. Challenges of Participants without a CS Background
  12. Conclusion of Formative Study
  13. SYSTEM: KOROT-3E
  14. Step 1. Lyrics Generation
  15. Step 2. Music Generation
  16. ...and 39 more sections

Figures (6)

  • Figure 1: Overall research design and procedure.
  • Figure 2: The KoroT-3E interface and usage process.S1: Use the lyrics generator to input the CS concepts that be adapted along with the corresponding prompts. S2 & S3: Receive the adapted lyrics and copy and paste them into the music generator; the lyrics can also be downloaded as a txt file. S4: Input the user’s preferred music style. S5: Obtain the generated musical mnemonic and music cover, both of which can be downloaded.
  • Figure 3: Short-term memory experiment flowchart.
  • Figure 4: Single test scores and overall average scores for VNA and LR across three tests.
  • Figure 5: Result of SUS
  • ...and 1 more figures