A learner corpus analysis of problem-solving schemata and move structures in debating discourse

Problem-solving is essentially a process with schematic, conversational and procedural attributes. This skill set is essential for graduates to solve problems they encounter in their social, academic, and professional lives. A huge portion of problem-solving practice may be found in debating. Due to the magnitude of problem-solving skills, this study aimed to investigate the synergetic role of debating practice on problem-solving language development in a corpus-assisted way. This study compiled a learner corpus containing 32,3975 tokens of 28 transcribed debates from the World University Debating Championship on YouTube (see Appendix). A corpus-based analysis by AntConc explored the schematic features of problem-solving patterns in terms of type-token ratio, collocation, standardized frequencies, and concordance lines. The findings show that problem-solving representations were outstanding in the debating learner corpus with a high type-token ratio, Problem schema, and Solution schema. Patterns concerning problem, need, and solution(s) appear with a highly standardized frequency. In addition, a concordance analysis of the most frequent keywords revealed the schematic variations of problem-solving functions employed by debaters. The genre analysis confirms the presence of problem-solving procedures in the sequence of the Situation, the Problem, the Response or Solution, and the Evaluation and its conversational inherency under diverse opinions. These findings provide corpus evidence for the schematic, conversational and procedural representation of problem-solving. Thus, debating practice is a significant vehicle to facilitate students’ problem-solving sense development of knowledge schema, conversations, and genre prototypes.