AI Summary & Key Takeaways
AI InsightThe results indicated that musical, visual, and verbal intelligencesignificantly predicted whether recasts will be perceived by students as corrective feedback.Therefore, it was concluded that it is essential to build MI into the picture when evaluating theefficacy of recasts on the grounds that students with certain dominant intelligence benefit fromsubtle clues which in turn help them notice recasts.
📌 3 Key Takeaways
Abstract
There has been a host of research on recast so far, despite which it is still controversial if itis any effective. The main argument against recasts is that they often go unnoticed by studentsdue to their implicit nature. It is hypothesized in the light of the theory of Multiple Intelligences(MI) that certain intelligences might affect the noticeability of recasts. Therefore, this paperaimed to find if certain intelligence help learners notice recasts. To fulfill this, 121 preintermediateEFL learners in 9 different classes were selected using convenience sampling. Aquestionnaire was used to measure their MI. Moreover, they were observed for 104 hours forthe occurrence of recast and uptake using a checklist. For data analysis, multiple regressionwas used to find out if any components of MI were significant predictors of recasts' success inleading to uptake. The results indicated that musical, visual, and verbal intelligencesignificantly predicted whether recasts will be perceived by students as corrective feedback.Therefore, it was concluded that it is essential to build MI into the picture when evaluating theefficacy of recasts on the grounds that students with certain dominant intelligence benefit fromsubtle clues which in turn help them notice recasts.