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Title
Japanese: 
English:Designing automated feedback on source text content representation in a web-based formative assessment module for L2 summary writing 
Author
Japanese: Yasuyo Sawaki, GECCHELE Marcello, 山田 寛章, 徳永 健伸.  
English: Yasuyo Sawaki, Marcello Gecchele, Hiroaki Yamada, Takenobu Tokunaga.  
Language English 
Journal/Book name
Japanese: 
English: 
Volume, Number, Page        
Published date June 5, 2023 
Publisher
Japanese: 
English: 
Conference name
Japanese: 
English:​44th Language Testing Research Colloquium (LTRC) 
Conference site
Japanese: 
English:New York City 
Official URL https://ltrc2023.weebly.com/
 
Abstract Succinct representation of source text gist is a critical component of good summary writing performance. Given the importance of this task type in the academic domain (Cumming, 2013; Leki & Carson, 1997; Rosenfeld et al., 2001) and the complexity of the construct of summarization, it is imperative to provide L2 academic writers with actionable feedback to facilitate learning (Boud & Molloy, 2013; Hattie & Timperlay, 2007; Lam, 2021). While the recent technological advancements offer a promising avenue toward this direction, designing and developing feedback on source text content representation in summarization is not a straightforward task. Previous L2 automated writing evaluation research has focused primarily on corrective feedback, while available theoretical and empirical works are scant concerning automated content feedback for summary writing. In this talk, the presenters will discuss a specific challenge faced in designing automated content feedback for a web-based formative assessment module for summary writing in introductory academic writing courses in Japan. Unlike written summaries elicited in some existing automated content feedback programs, those in this module are shorter, around 80 words. Since such summaries often comprise only a few sentences, the initial step toward designing fine-grained content feedback is to identify an appropriate within-sentence meaning unit that is easy to understand for learners. As candidates, the team is currently comparing the idea unit (Kroll, 1977), often employed for qualitative analyses of L2 recall and summary protocols, and the elementary discourse unit (EDU; Carson & Marcu, 2001), originally developed in Rhetorical Structure Theory. The comparison focuses on the reliability of automatic segmentation of summary protocols and alignment of the obtained units to corresponding parts of the source text based on semantic similarity for content feedback generation. Theoretical issues of consideration and small-scale pilot results for comparing them between human coding and automated algorithms will be discussed.

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