How can Writing Tasks be Characterized in a way serving Pedagogical Goals and Automatic Analysis Needs?

Abstract

The paper tackles a central question in the field of Intelligent Computer-Assisted Language Learning (ICALL): How can language learning tasks be conceptualized and made explicit in a way that supports the pedagogical goals of current Foreign Language Teaching and Learning and at the same time provides an explicit characterization of the Natural Language Processing (NLP) requirements for providing feedback to learners completing those tasks? We argue that the successful implementation of language learning tasks that can be automatically assessed demands a design process in which both pedagogical and computational requirements feed each other. Extending well-established work in Task-Based Instruction (TBI) and Language Testing, we propose a framework that helps us (a) elucidate the formal features of foreign language learning activities, (b) characterize the relation between the expected and the actually elicited learner language, and (c) assess how the variability in the learner responses impacts interpretation and computational techniques for the automatic analysis of learner language. To validate our approach we apply the framework to two writing tasks for learners of English as a foreign language. Our analysis highlights the relevance of spelling out the pedagogical and linguistic goals of language learning tasks in order to successfully characterize the language expected and actually elicited in learner responses, to evaluate the realization of the task, and to support the design of effective NLP strategies. Given the combination of design-and data-driven perspectives, the framework supports an iterative approach to the creation of language learning tasks and ICALL materials.

Systematic review of research on artificial intelligence applications in higher education – where are the educators?

“…Feedback Articles concerned with feedback included a range of student-facing tools, including intelligent agents that provide students with prompts or guidance when they are confused or stalled in their work (Huang, Chen, Luo, Chen, & Chuang, 2008), software to alert trainee pilots when they are losing situation awareness whilst flying (Thatcher, 2014), and machine learning techniques with lexical features to generate automatic feedback and assist in improving student writing (Chodorow, Gamon, & Tetreault, 2010;Garcia-Gorrostieta, Lopez-Lopez, & Gonzalez-Lopez, 2018;Quixal & Meurers, 2016), which can help reduce students cognitive overload (Yang, Wong, & Yeh, 2009). The automated feedback system based on adaptive testing reported by Barker (2010), for example, not only determines the most appropriate individual answers according to Bloom's cognitive levels, but also recommends additional materials and challenges.…”

Effects of task type on morphosyntactic complexity across proficiency

“…Kormos 2011;Kuiken and Vedder 2008;Way, Joiner and Seaman 2000;Yoon and Polio 2017), computer-assisted language learning (e.g. Quixal and Meurers 2016) and language assessment (e.g. Biber, Gray and Staples 2014;Hinkel 2009;Weigle 2002).…”

Task Effects on Linguistic Complexity and Accuracy: A Large‐Scale Learner Corpus Analysis Employing Natural Language Processing Techniques

“…Task effects are widely recognized as an important aspect of learner language analysis—for L1 writing, see Huot (); for language assessment, see Bachman (), Biber and Conrad (), Biber, Gray, and Staples (), Hinkel (), and Weigle (); for instructed SLA, see Kormos (), Kuiken and Vedder (), and Way, Joiner, and Seaman (); and for computer‐assisted language learning, see Quixal and Meurers (). However, learner corpus research has generally not been linked to research investigating effects of task on L2 production (but cf.…”