A WSQ-based mobile peer assessment approach to enhancing university students’ vocal music skills and learning perceptions
DOI:
https://doi.org/10.14742/ajet.6832Keywords:
watch-summary-question (WSQ) strategy, peer assessment, vocal music education, mobile learning, reflective thinkingAbstract
Peer assessment has been regarded as an effective learning strategy in art education, such as in music, dance and art design. For music education, technology-supported peer assessment makes it easier for learners to reflect on their learning performance. However, the process of reflection needs solid and systematic theoretical knowledge. In terms of vocal music, which develops students’ singing skills, it is difficult for learners to compare their own works with those of others because of a lack of sufficient theoretical knowledge. Therefore, a WSQ (watch-summary-question)-based mobile peer assessment approach was used to help learners reflect based on their theoretical knowledge with the support of WSQ learning sheets. To investigate the effectiveness of the approach, an experiment was carried out in a Chinese university. The experimental group (N = 22) learned with the WSQ approach, whereas the control group (N = 22) adopted a mobile peer assessment approach without WSQ. Vocal music skills, learning attitude and learning motivation were assessed. The experimental results indicate that the approach effectively enhanced the students’ vocal music skills but did not improve their learning motivation or attitude. In addition, a higher correlation between teacher scoring and peer scoring was found for the experimental group.
Implications for practice or policy:
- Technology has a great potential in supporting art education and integrating technology into vocal music education should follow the rules of music education.
- Developing students’ reflective thinking skills in vocal music education is challenging, but attainable; it needs considered instructional design and sustained efforts from students.
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Copyright (c) 2021 Chenchen Liu, Ping Wan, Yun-Fang Tu, Kai Chen, Youmei Wang
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