A systemic functional perspective on automated writing evaluation: formative feedback on causal discourse

Sarıcaoğlu, Aysel

doi:10.31274/etd-180810-4260

Cited by 2 publications

(1 citation statement)

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“…In this study, causal explanations are evaluated based on six categories that were identified through corpus analysis in an earlier study during the development of ACDET (Saricaoglu, 2015). According to this categorization, causal relations can be expressed using (a) causal conjunctions such as for , if , or so that ; (b) causal adverbs such as fatally or in response ; (c) causal prepositions (including prepositional phrases) such as through or as a consequence of ; (d) causal verbs (including phrasal verbs) such as freeze or result from ; (e) causal adjectives such as beneficial ; and (f) causal nouns such as influence .…”

Section: Automated Feedback On Written Causal Explanationsmentioning

confidence: 99%

The impact of automated feedback on L2 learners’ written causal explanations

Sarıcaoğlu¹

2018

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Even though current technologies allow for automated feedback, evaluating content and generating discourse-specific feedback is still a challenge for automated systems, which explains the gap in research investigating the effect of such feedback. This study explores the impact of automated formative feedback on the improvement of English as a second language (ESL) learners’ written causal explanations within two cause-and-effect essays and across pre- and post-tests. Pre- and post-test drafts, feedback reports for first and revised drafts from the automated writing evaluation system, and screen-capturing videos collected from 31 students enrolled in two sections of an advanced-low-level academic writing class were analyzed through descriptive statistics and the Wilcoxon signed-rank test. Findings revealed statistically significant changes in learners’ causal explanations within one cause-and-effect essay while no significant improvement was observed across pre- and post-tests. The findings of this study offer not only insights into how to further improve automated discourse-specific feedback but also pedagogical implications for better learning outcomes.

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Section: Automated Feedback On Written Causal Explanationsmentioning

confidence: 99%