⬅ Back to Agenda

Enhancing Conditional Knowledge and Its Regulative Functions through Collaborative Learning in Translation Crowdsourcing

Ya-mei Chen
National Taipei University of Technology, Taipei, Taiwan


Translation crowdsourcing, propelled by technological advancements, has opened up new avenues for collaborative translation efforts. Although traditional methods remain prevalent, crowdsourcing stands out as a potent tool, not only for its innovative approach but also for its potential in amplifying students’ conditional knowledge through collaborative learning. However, combining translation crowdsourcing with metacognitive training is still an unexplored territory.

In an endeavor to illuminate this uncharted territory, this paper employed a case study of Global Voices Lingua, integrating it into metacognitive translator training through collaborative learning. The focus was to investigate how and to what extent collaborative efforts can amplify students’ awareness of conditional knowledge within the context of translation crowdsourcing. The research involved fifteen undergraduate students, grouped into mixed-ability teams, translating Global Voices’ English news posts into Chinese. The process included individual translation, self-reflection on problem-solving, and collaborative discussions, all recorded for in-depth analysis.

Findings from this study reveal that ‘contextualized’ problems constituted the majority at 86.4%, while ‘de-contextualized’ problems were at 13.6%. This distribution illustrates the role of crowdsourcing in enhancing students’ understanding of conditional knowledge through a specific focus on situational demands. Following collaborative discussions, there was a noticeable increase in awareness, with 31% of ‘contextualized’ problems and 53% of ‘de-contextualized’ problems showing improvement. The group-wise analysis further underscores the correlation between individual success and collaborative improvements, with varying degrees of enhancement across different groups.

This study uses the specific example of the Global Voices Lingua project to reveal the previously unknown potential of crowdsourcing translation. By emphasizing the impact of collaborative learning, it highlights how such cooperation can significantly contribute to the understanding of students’ conditional knowledge.

International Joint Conference of APLX, ETRA40, and TESPA 2023