Peer feedback as a collaborative task used in improving writing skills: A case study
Learning to write in English as a foreign language has become one of the basic skills for those who need and want to read and produce academic discourse. The problems of EFL learners in connection with writing are usually related to the transfer of knowledge of their mother tongue in terms of lexica...
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| Format: | Article |
| Language: | English |
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Pedagoško društvo Srbije i Institut za pedagogiju i andragogiju Filozofskog fakulteta Univerziteta u Beogradu
2017-01-01
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| Series: | Nastava i Vaspitanje |
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| Online Access: | https://scindeks-clanci.ceon.rs/data/pdf/0547-3330/2017/0547-33301703515P.pdf |
| Summary: | Learning to write in English as a foreign language has become one of the basic skills for those who need and want to read and produce academic discourse. The problems of EFL learners in connection with writing are usually related to the transfer of knowledge of their mother tongue in terms of lexical structures, text conventions and line of argumentation. In that context, error treatment and adequate feedback have a huge impact on the level of attainment and progress in the field of writing. The aims of this small-scale study were to find the correlation between different types of feedback and types of learning, and to determine the effects of these variables on the level of attainment of students in reference to specific written assignments. Both quantitative (descriptive statistics based on the percentage share within the structures of the categories) and qualitative methods were used. The results of the study indicate that the preferences for a specific type of feedback are culture-specific and that a collaborative task-based approach had a positive impact on improving specific skills related to writing and giving and receiving feedback. The findings led to the suggestion to include more of these activities in curricula and to redesign assessment methods as providing and responding to adequate feedback has huge pedagogical implications for deep learning. |
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| ISSN: | 0547-3330 2560-3051 |