Generating Feedback for English Foreign Language Exercises

Abstract

While immediate feedback on learner language is often discussed in the Second Language Acquisition literature (e.g., Mackey 2006), few systems used in real-life educational settings provide helpful, metalinguistic feedback to learners.In this paper, we present a novel approach leveraging task information to generate the expected range of well-formed and ill-formed variability in learner answers along with the required diagnosis and feedback. We combine this offline generation approach with an online component that matches the actual student answers against the pre-computed hypotheses.The results obtained for a set of 33 thousand answers of 7th grade German high school students learning English show that the approach successfully covers frequent answer patterns. At the same time, paraphrases and meaning errors require a more flexible alignment approach, for which we are planning to complement the method with the CoMiC approach successfully used for the analysis of reading comprehension answers .

Text mining in education

“…(Loukina, Zechner, Bruno, & Beigman Klebanov, 2018) Question Classification Evaluation This paper compares the performance of an automated speech scoring engine using two corpora. (Rudzewitz et al, 2018) Question Classification Feedback This paper presents a novel approach leveraging task information to generate the expected range of well-formed and ill-formed variability in learner answers along with the required diagnosis and feedback. (Kulkarni & Boyer, 2018) Question Classification Generation This paper explores the possibility of building a tutorial question answering system for Java programming from data sampled from a community-based question answering forum.…”

Scaling Up Intervention Studies to Investigate Real-Life Foreign Language Learning in School

“…The basic FeedBook system functionality described in Rudzewitz, Ziai, De Kuthy, and Meurers (2017) was piloted in the school year 2017–2018. It included a student interface as well as a teacher interface that allows the teacher to manually provide feedback to students with the help of some system support, such as a feedback memory recognizing recurring student responses and inserting the feedback that was given before (inspired by translation memories supporting human translators).…”

“…Students, parents, and teachers essentially expect technology used in daily life to work on all types of devices, including mobile phones and tablets, all operating systems, and all browser types and versions, and to provide the functions they are used to (though we ultimately convinced the students that adding a feature to invite your friends was not as essential as they thought). We then turned the FeedBook from a web-based exercise book into an ITS by adding an automatic, interactive form feedback (Rudzewitz et al, 2018). While our original motivation for the FeedBook project was the decade of research we spent on the automatic meaning assessment of short-answer activities in the CoMiC project (), the real-life focus of homework assignments in workbooks in our experience clearly is on practicing forms, even when the textbook itself is TBLT-inspired.…”

“…In the next step, we expanded the automatic feedback in the FeedBook to also provide feedback on meaning , as needed for meaning-based reading/listening comprehension exercises (Ziai, Rudzewitz, De Kuthy, Nuxoll, & Meurers, 2018). Including such meaning-based activities in the FeedBook also provides opportunities for the system to give (incidental) focus on form feedback (Ellis, 2016).…”

“…Second, the exercise properties are directly taken into account by the computational modeling of the well-formed and ill-formed variability of the learner language. The approach in Rudzewitz et al (2018) derives the structure of the answer space that the system can respond to, which is based on the target hypotheses provided by the publisher in the teacher version of the workbook, combined with a flexible online matching mechanism.…”