2 research outputs found
Towards Computational Models for a Long-term Interaction with an Artificial Conversational Companion
Abstract: In this paper we describe a design approach for an Artificial Conversational Companion according to ear-lier identified requirements of utility, adaptivity, conversational capabilities and long-term interaction. The Companion is aimed to help advanced learners of a foreign language to practice conversation via instant mes-senger dialogues. In order to model a meaningful long-term interaction with an Artificial Conversational Companion for this application case, it is necessary to understand how natural long-term interaction via chat between human language experts and language learners works. For this purpose, we created a corpus from instant messenger-based interactions between native speakers of German and advanced learners of German as a foreign language. We used methods from conversation analysis to identify rules of interaction. Examples from our data set are used to illustrate how particular requirements for the agent can be fulfilled. Finally, we outline how the identified patterns of interaction can be used for the design of an Artificial Conversational Companion.
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The Task is in the Text: Texting and Second Language Oral Fluency
Text messaging is the most popular form of communication (Lionbridge, 2019; Ceci, 2022), and mobile phone ownership is high, especially among university students (Chen & Denoyelles, 2013). Research on mobile language learning is increasingly found on the forefront of computer-assisted language learning (CALL) (Loewen et al., 2019; Stockwell, 2022), and studies exploring the use of text messaging for language learning is no exception (Cavus & Ibrahim, 2009; Derakhshan & Kaivanpanah, 2011; Kennedy & Levy, 2008; Kim, 2011; Li & Cummins, 2019; Tabatabaei & Goojani, 2012). However, vocabulary studies for English as a Second Language (ESL) tend to dominate the literature (Burston & Arispe, 2022). As a communication platform, text messaging offers three intriguing characteristics for supporting the development of language learning skills. First, texting allows users to receive input, produce output, and engage in negotiation of meaning, which interactionist theorists say is essential for language acquisition (Blake & Guillén, 2019). Second, while users text back and forth, they work towards a shared communication goal, and engage in a collaborative, communicative activity, which is a necessary component for language learning in a socioconstructivist framework (Arnold & Ducate, 2019). Lastly, text messaging is a hybrid form of discourse in that it includes elements of both spoken and written discourse.
This study reports on the impact of text messaging on second language (L2) oral fluency of non-native speakers of Spanish. We compare pre- and post-treatment speech samples of two groups of learners who carried out weekly communicative tasks either via WhatsApp (experimental group) or Zoom (control group). The results of the mixed methods study (n=20) suggest that text messaging as a modality for language learning may offer some of the same affordances that speaking face-to-face does, especially as it pertains to speech rate (a measurable variable of fluency). Although there were no statistically differences for the other assessment measures of fluency across the two groups (unique words, total words, pauses, fluency, or percentage of impediment caused by incomprehension), the qualitative measures highlighted more opportunities to practice the Spanish language outside of class, increased opportunities to engage in the language in a low-stress, low-stakes environment, and the partner connection and community building this type of learning supported. The data from this study also offers insight into best practices for task design in communicative language learning activities, particularly, in a mobile environment. Lastly, the data supports previous research that the technological modality needs to align with the learning task itself (Stockwell, 2022)