Does Ability Affect Alignment in Second Language Tutorial Dialogue?

Abstract

The role of alignment between interlocutors in second language learning is different to that in fluent conversational dialogue. Learners gain linguistic skill through increased alignment, yet the extent to which they can align will be constrained by their ability. Tutors may use alignment to teach and encourage the student, yet still must push the student and correct their errors, decreasing alignment. To understand how learner ability interacts with alignment, we measure the influence of ability on lexical priming, an indicator of alignment. We find that lexical priming in learner-tutor dialogues differs from that in conversational and task-based dialogues, and we find evidence that alignment increases with ability and with word complexity.

I Wanna Talk Like You: Speaker Adaptation to Dialogue Style in L2 Practice Conversation

“…Here, we are able to see that this is the case for interlocutors' use of DAs. While Sinclair et al [20] found some evidence of alignment at a lexical level, our work shows this at a more abstract level in terms of the conversational dynamics via DAs.…”

“…Alignment consists of interlocutor interaction adaptation, resulting in convergence, or in their sharing of the same concept space [14,3,6]. Typically, alignment is measured at either a lexical (use of the same words [26,20] or phrases [5] as each other) or a syntactic (use of the same parts of speech patterns e.g. similar noun-phrase constructions, or similar adjuncts [15,16]) level.…”

“…similar noun-phrase constructions, or similar adjuncts [15,16]) level. Methods for measuring alignment range from count statistics [5] to linear regression on prime-to-target distance 3 [25] to using general linear mixed models to account for the random effects present in dialogue [16,20].…”

Text mining in education

“…In addition, the analysis of interactional resources like forums, chats, and blogs have been explored within this goal. In this context, opinion analysis methods have been tried to extract the sentiment of the students (Kechaou et al, 2011;Ortigosa et al, 2014;Sinclair et al, 2018), and the adoption of methods to encourage collaboration (Li, Dong, & Huang, 2008;Trausan-Matu et al, 2012) have been used to keep the students motivated preventing dropout. Moreover, there are a few works that aid the instructor to monitor students within this tools (Perikos, Grivokostopoulou, & Hatzilygeroudis, 2017;Scheuer & McLaren, 2008).…”

“…As the methods for the education domain are adopted for curses with different educational environments and set-ups, they need to be adaptable for different languages. • Sentiment analysis: Although nowadays there are a good number of works that use this technique (Coutinho et al, 2016;Kechaou et al, 2011;Newman & Joyner, 2018;Ortigosa et al, 2014;Rojas et al, 2012;Sinclair et al, 2018;Tucker et al, 2014;Wen, Yang, & Rose, 2014), it continues being a hot topic, and there is much work to do with it in the education field. For instance, it can be applied to all type of educational resources and not only forum, chats, social networks, blogs, and emails, but also essays and online assignments.…”