Vocal music education is a skill-oriented course. Students not only need to improve their skills through repeated practice, but also need to learn self-reflection on their singing skills to achieve improvement in their vocal music performance. To promote students' reflection, previous studies have introduced peer assessment (PA) in vocal music courses, allowing learners to evaluate the work of others to promote self-reflection and innovate traditional patterns of vocal music instruction.
Studies have indicated that, without proper scaffolding, learners with limited knowledge often have difficulty providing high quality comments and sometimes even provide no feedback to their peers. To solve this problem, the current study integrated the structural reflection-promoting mechanism into PA in a vocal music course at a university.
To verify the effects of the proposed approach, a quasi-experimental study was conducted to investigate the effectiveness of the approach with a total of 22 students recruited as the experimental group and 23 students as the control group.
The findings showed that the approach greatly improved the vocal music performance of the experimental group students, as well as their learning attitudes, self-efficacy and critical thinking. Furthermore, the interview was conducted to investigate learners' perceptions of and learning experience with the proposed approach.
Artificial intelligence (AI) has profoundly impacted education, yet its influence on Chinese language education remains underexplored. This study investigates the distinct latent profiles of Chinese as a foreign language learners in Malaysia regarding their beliefs and engagement in AI-assisted Chinese learning. Using latent profile analysis, three profiles were identified: Enthusiastic but Limited Belief, Moderate Belief and Engagement, and Strong Belief and Engagement. Chi-square analyses revealed significant demographic differences across these groups in gender, AI learning experience, AI learning frequency, and trust levels in AI tools. These findings provide insights into the diverse learner profiles in Malaysia and emphasise the importance of addressing the specific needs of each group to optimise their engagement in AI-assisted Chinese learning environments.