Big Data-driven Pre-task Preparation for Consecutive Interpreting Mock Conferences Conducted by Student Interpreters

Pre-task preparation is important and challenging to both veteran and novice interpreters in light of the short time they have before the conferences. This paper probes into students’ utilization of algorithm-driven search engine and big data-powered social media in their efforts to get familiar with the theme of the conference and generate glossaries, and the impact of such a practice on their performance in consecutive interpreting mock conference and the retention rate of terms and terminologies. Students in the experimental group were assigned the task of preparation with the provision of a detailed agenda, while the control group was given the materials similar to the common practice prevalent in the conference interpreting market. Both quantitative and qualitative data were collected and processed with SPSS based on the assessment forms of the mock conference, an online questionnaire and interview. The findings showed that students mainly make full use of search engines and social media with big data-driven recommendations for information about the theme of the conference, and employ online WPS documents for co-creation of glossaries, thus activating their terms and terminologies, enhancing their performances in the consecutive interpreting mock conference and the retention test one week later.