From the Amateur to the Professional in Group Discussions: Exploring the Use of Metacognitive Strategies

Abstract

Group discussion performance of learners is used for shortlisting candidates during campus recruitment across professional courses. The complexity of the task requires learners to make use of several learning strategies to enhance their group discussion performance to become good group discussants. Many professional courses incorporate an orientation programme in developing group discussion skills in their English course. While these programmes focus on the verbal aspects of language, the strategies learners inherently use are often neglected. Consequently, this paper attempts to capture the metacognitive strategies which the good group discussants employ when the discussion is underway, thereby, making them adept. The data of one female and two male first year engineering students from a video recording of a round of group discussion, a strategies use questionnaire and researcher’s observation report of individual performance was qualitatively analyzed to identify the different metacognitive strategies and skills of group discussion which the participants inherently use. The findings suggest that good group discussants exhibit the use of certain metacognitive strategies such as ‘visualization’, ‘activating background knowledge’, and ‘self-monitoring’ which the other discussants do not make use of. Since the findings of the study suggest that strategies play an important role in helping the discussant to augment performance during group discussions, the English teacher aiming to teach group discussion skills ought to focus on these as well. Therefore, this study has implications for the development of a strategies training programme to improve group discussion skills vis-a-vis metacognitive strategy use among tertiary level learners.



Author Information
Shravasti Chakravarty, The English and Foreign Languages University, India

Paper Information
Conference: ACLL2018
Stream: Psychology of the learner

This paper is part of the ACLL2018 Conference Proceedings (View)
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