The Cymc Multimedia Progression Model: A Video-supported Framework for Youth Performance Learning

Peiwen Su, California Youth Music Competition (CYMC), USA

ABSTRACT

This study presents the CYMC Multimedia Progression Model (MPM), a technology-enhanced framework developed across the California Youth Music Competition’s regional and international tiers. Because CYMC administers all levels of competition, it implements unified, high-standard multimedia recording that supports both reflective learning and advancement. Drawing on more than 200 professionally captured performance videos from young musicians, the study examines how standardized multimedia documentation functions as a digital portfolio for international selection, enabling students to progress from local performances to CYMC’s global stages. Findings indicate that this multimedia-supported cycle enhances self-efficacy, expressive behavior, and goal-directed motivation. The upward pathway—where each regional performance carries the potential for international exposure—creates a strong motivational loop and strengthens learners’ artistic identity. The model offers a scalable technology-driven approach for developing confidence, resilience, and engagement in youth performance education.

Keywords

multimedia-supported learning, performance pedagogy, digital portfolio, international progression, youth music education.

Hierarchical Worker Evaluation Based on Requester’s Subjective Criteria in Open-Ended Crowdsourcing Tasks

Ryuya Itano¹,Honoka Tanitsu¹,Motoki Bamba¹, Takahiro Koita¹,Ryota Noseyama²,Akihito Kohiga², ¹ Graduate School of Science and Engineering, Doshisha University, Kyoto, Japan,² Faculty of Science and Engineering, Doshisha University, Kyoto, Japan .

ABSTRACT

Crowdsourcing assumes a transient relationship between task requesters and workers, which makes it hard for workers to improve their skills. In addition, with the emergence of AI, crowd work is shifting from simple tasks to more complex and open-ended ones, highlighting the importance of training workers to handle such tasks. Although various methods have been proposed to train and evaluate workers, a method to evaluate them in open-ended tasks among workers has not yet been established. In this study, we propose applying a hierarchical inter-worker evaluation structure based on workers’ skill levels to the evaluation of open-ended tasks, and examine how closely it aligns with the requesters’ subjective evaluation criteria. The experimental results showed that workers’ evaluations were highly aligned with the requesters’ subjective evaluation criteria in terms of relative worker rankings. However, the alignment was weaker for absolute scores, due to workers’ tendency toward generous scoring. These findings are expected to be utilized in future research to enhance worker engagement and retention rates.

Keywords

Crowdsourcing, Worker training, Worker evaluation, Amazon Mechanical Turk