To ensure smooth communication and collaboration, here are some troubleshooting tips to address common issues:
Check Internet Connection: Verify that you have a stable and reliable internet connection. Use a wired connection when possible, as it tends to be more stable than Wi-Fi. If using Wi-Fi, make sure you have a strong signal.
Update the Browser or App: Ensure that you are using the latest version of the web browser. Developers frequently release updates to address bugs and improve performance.
Clear Browser Cache: Sometimes, cached data can cause conflicts or issues. Clear the browser cache and cookies before joining the meeting.
Test Audio and Video: Before the meeting, check your microphone and camera to ensure they are working correctly. If you are a speaker, you can click on "Start Practice Session" button test to ensure audio and video devices are functioning.
Close Other Applications: Running multiple applications in the background can consume system resources and lead to performance issues. Close unnecessary apps to free up resources for the Dryfta meeting platform.
Restart Your Device: If you encounter persistent issues, try restarting your computer or mobile device. This can help resolve various software-related problems.
Use Supported Browsers: Ensure you are using a browser supported by the meeting platform. Recommended browsers: Chrome, Firefox, Edge, and Brave.
Allow Necessary Permissions: Make sure the Dryfta meeting platform has the required permissions to access your microphone, camera, and other necessary features.
Disable VPN or Firewall: Sometimes, VPNs or firewalls can interfere with the connection to the meeting platform. Temporarily disable them and see if the issue persists.
Switch Devices: If possible, try joining the meeting from a different device to see if the problem is specific to one device.
Reduce Bandwidth Usage: In cases of slow or unstable internet connections, ask participants to disable video or share video selectively to reduce bandwidth consumption.
Update Drivers and Software: Ensure your operating system, audio drivers, and video drivers are up to date. Outdated drivers can cause compatibility issues with the Dryfta meeting platform.
Contact Support: If none of the above steps resolve the issue, reach out to the platform's support team. They can provide personalized assistance and troubleshoot specific problems.
By following these troubleshooting tips, you can tackle many common problems encountered on Dryfta meeting platform and have a more productive and seamless meeting experience.
20230719T101520230719T1315Europe/Amsterdam[SYMP19] Applied Linguistics perspectives on human-robot interaction in language education: possibilities and challengesHybrid Session (onsite/online)AILA 2023 - 20th Anniversary Congress Lyon Editioncellule.congres@ens-lyon.fr
The effects of AI-assisted language learning on the intelligibility of Korean-accented English
Poster Presentation[SYMP19] Applied Linguistics perspectives on human-robot interaction in language education: possibilities and challenges10:15 AM - 01:15 PM (Europe/Amsterdam) 2023/07/19 08:15:00 UTC - 2024/07/19 11:15:00 UTC
The use of conversational AI technologies, such as voice assistants, is increasing. By providing fast and consistent interaction with a device, AI technologies respond to commands or provide services for an individual. The recent COVID-19 pandemic has triggered demand for the use of these technological applications in language learning environments, maximizing learners' affinity with digital devices.
This study was carried out to explore the effect of utilizing AI mobile applications (AI apps) in foreign language pronunciation training, which attempts to expand the adaptation of AI technologies in education. Two recordings were prepared containing 100 sentences, adopted from a test battery, the Oxford Placement Test (Allan, 2004). One audio was made by six Korean-speaking learners (KSLs) who received 4-week English pronunciation practice using instant feedback from the AI apps, and the other one was recorded by six other KSLs who did not make use of the AI apps for pronunciation training. A total of 51 KSLs from two intact classes with a similar English proficiency level listened to one of the two audio materials for word identification task. The mean listening scores were compared between two audio sets in consideration of the eight distinctive features based on the generalized phonological inventory of the Lingua Franca Core (Jenkins, 2000), and non-LFC features.
The findings showed that those who listened to the audio of the experimental group (AI apps intervention group) outperformed the counterpart group. Moreover, significant differences between two groups were observed in three distinctive features, reported as major sources of reduced intelligibility in Korean-accented English (Chung & Bong, 2021): vowel lengths, vowel quality, and coda consonant clusters.
By directing attention to some features in a learnability aspect, this study concludes by suggesting that English language learners who have high digital literacy skills should be engaged in AI apps utilizing pronunciation practice.
Allan, D. (2004). Oxford placement test 1. Oxford, England: Oxford University Press. Chung, B., & Bong, H. K. M. (2021). Intelligibility of Korean-accented English: Effects of listener's familiarity. English Teaching, 76(1), 33-56. Jenkins, J. (2000). The phonology of English as an international language. Oxford, England: Oxford University Press.
Bohyon Chung is a lecturer teaching various English courses. Her research areas focus on the Pedagogy of English ̶ the development of instructional design combining artificial intelligence applications, and on the areas of Applied Linguistics - Second/foreign language acquisition, English as a lingua franca, and cross-cultural discourse analysis. She is a two-time recipient of a young researcher grant for a three-year period by the National Research Foundation of Korea. Hyun Kyung Miki Bong is Professor at Ritsumeikan University, Japan. She holds BA and MA from Nagoya University in English Linguistics and M.Phil. and Ph.D. from the University of Cambridge in English and Applied Linguistics. Her main research interests lie in practical applications of English language studies: e.g., English language learning and teaching as a foreign language, translation and interpretation, English as a lingua franca, and in the field of Applied Linguistics: i.e., first and second language acquisition.
Using the Integrated Model of Technology Acceptance to evaluate an Artificial Intelligence (AI) speech evaluation program for English speaking practice
Oral Presentation[SYMP19] Applied Linguistics perspectives on human-robot interaction in language education: possibilities and challenges10:15 AM - 01:15 PM (Europe/Amsterdam) 2023/07/19 08:15:00 UTC - 2024/07/19 11:15:00 UTC
Adapted from the Technology Acceptance Model (TAM), the Integrated Model of Technology Acceptance (IMTA) has been used to examine the perceptions and acceptance of computer-assisted language learning (CALL), such as online learning, mobile learning, and learning management systems. In the wake of the artificial intelligence (AI) revolution, whether IMTA can be applied to empirical research on AI-assisted language learning is still an under-researched field. Therefore, this paper intends to analyze an AI speech evaluation system for English speaking practice, in the context of higher education through the IMTA. This study demonstrated how IMTA could be applied in AI programs for EFL speaking practice. The findings also offer insights into further research and development in AI tools for EFL speaking practice.
Artificial intelligence (AI) and machine learning have equipped Computer-Assisted Language Learning (CALL) and Mobile-Assisted Language Learning (MALL) with more advanced technology, such as individual tutoring systems, automatic speech recognition and speech evaluation systems. Some AI speech evaluation programs have been developed to help EFL learners practice speaking skills (e.g. Doulingo, IELTS Smart Learning and EAP Talk). However, very few studies evaluated users' acceptance of AI speech evaluation programs with a framework or model, such as the Technology Acceptance Model (TAM) which is a popular model used for examining users' acceptance of technology (Davis, 1989; Teo, 2010), or the Integrated Model of Technology Acceptance (IMTA) (Fagan, Neill & Wooldridge, 2008), which is adapted from Davis (1989)'s prestigious TAM. The IMTA has been used to examine the perceptions and acceptance of CALL, such as online learning, mobile learning, and learning management systems. In the wake of the AI revolution, whether IMTA can be applied to empirical research on AI-assisted language learning is still an under-researched field. Therefore, this paper intends to analyze an AI speech evaluation system for English speaking practice, in the context of higher education through the IMTA. Research instruments encompassed questionnaires (n = 224) and semi-structured interviews (n =21). All participants who are EFL learners from 47 universities used an AI speech evaluation program to practice speaking skills. The results suggested that (1) most participants found the AI program useful, pleasant and easy to use. They also strongly intended to use it; (2) perceived usefulness (PU) and perceived enjoyment (PE) are significant predicting factors for behavioural intention to use (BI). Meanwhile, problems related to user interface design, the accuracy of automatic feedback and especially the lack of face-to-face interaction were reported. This study demonstrated how IMTA could be applied in AI programs for EFL speaking practice. The findings also offer insights into further research and development in AI tools for EFL speaking practice.
References Davis, F. D. (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340. Fagan, M. H., Neill, S., & Wooldridge, B. R. (2008) Exploring the intention to use computers: An empirical investigation of the role of intrinsic motivation, extrinsic motivation, and perceived ease of use. Journal of Computer Information Systems, 48(3): 31-37. Teo, T. (2010) An empirical study to validate the technology acceptance model (tam) in explaining the intention to use technology among educational users. International Journal of Information and Communication Technology Education, 6(4), 1-12.
Bibliography Bin Zou is an associate professor at the Department of Applied Linguistics, Xi'an Jiaotong-Liverpool University, China. With a PhD degree from the University of Bristol, UK, his research interests include ELT, EAP, CALL and AI. He is the Editor-in-Chief of the International Journal of Computer-Assisted Language Learning and Teaching.
Epistemic repair between young language learners in RALL
Oral Presentation[SYMP19] Applied Linguistics perspectives on human-robot interaction in language education: possibilities and challenges10:15 AM - 01:15 PM (Europe/Amsterdam) 2023/07/19 08:15:00 UTC - 2024/07/19 11:15:00 UTC
In robot-assisted language learning (RALL), language learners interact with an embodied and multimodal agent in the foreign language (L2) in different types of conversational settings. However, learners often encounter troubles that disrupt the progressivity of interaction with the robot (Jakonen et al. forthcoming, Veivo & Mutta, in review). One strategy to cope with these problems includes shifting the focus from the robot to other human participants present in this situation and getting advice from them (Honkalammi et al. 2022).
In this paper, our main goal is to study epistemic repair between learners who talk with the robot in small groups of 2-4 learners for the first time. By epistemic repair, we refer to repair that concerns the learners' orientations toward knowledge and information (cf. Bolden 2013). Especially other-initiated repair is one crucial way to maintain progressivity and achieve joint goals in social interactions (Dingemanse et al. 2015). In L2 interactions, other-initiated repairs make it evident that participants monitor and orient to ongoing sequences of interaction (Hellermann 2011: 150). This is important for the development of interactional competencies that are situation-based and context-bound (Pekarek Doehler 2019: 30). In RALL, this means that peer collaboration is necessary to establish progressivity. Previous studies on digital learning environments suggest that some confusion is typical in this context and can be productive for learning, as it induces problem-solving (cf. Arguel et al. 2019). Thus, according to our interpretation, frequent troubles in RALL and subsequent peer collaborations are essential instances for collaborative language learning.
Our corpus consists of 15 video-recorded situations, totaling 5 hours and 13 minutes, from English as a L2 in a Swedish-speaking school in Finland. There were 34 learners, aged 9 to 13, accompanied with their teacher. In this data, we identified c. 160 repair sequences between learners, of which over 60 were epistemic. In our analysis, we focus on the repair sequence – how repair gets initiated and how it is solved.
References
Arguel, A. & al. (2019) Seeking optimal confusion: a review on epistemic emotion management in interactive digital learning environments, Interactive Learning Environments, 27:2, 200-210.
Bolden, G. (2013). Unpacking "Self": Repair and Epistemics in Conversation. Social Psychology Quarterly, 76(4), 314–342.
Dingemanse, M. et al. (2015). Universal Principles in the Repair of Communication Problems. PloS One, 10(9),
Honkalammi, H.-M., Veivo, O., & Johansson, M. (2022). Advice-giving between young learners in robot-assisted language learning. Proceedings of the Conference Human Perspectives on Spoken Human-Machine Interaction, 46–51.
Hellermann, J. (2011). Members' methods, members' competencies: Looking for evidence of language learning in longitudinal investigations of other-initiated repair. In: Hall, K. & al. (eds.). L2 Interactional Competence and Development. Multilingual Matters.
Jakonen et al (forthcoming). 'Am I saying it wrong?' Managing progressivity-related troubles in child-robot L2 interaction.
Pekarek Doehler S. (2019). On the nature and development of L2 interactional competence. In: Salaberry, M. & Kunitz (eds.) Searching and Testing L2 Interactional Competence. Routledge.
Veivo, O. & Mutta, M. (in review). Dialogue breakdowns in robot-assisted L2 learning.
Following boardwork: robot-mediated remote participation during plenary teaching in hybrid language classes
Oral Presentation[SYMP19] Applied Linguistics perspectives on human-robot interaction in language education: possibilities and challenges10:15 AM - 01:15 PM (Europe/Amsterdam) 2023/07/19 08:15:00 UTC - 2024/07/19 11:15:00 UTC
Over the past few years, the Covid-19 pandemic has increased the need to develop remote and hybrid education by identifying effective teaching practices and developing pedagogically meaningful technological infrastructures. Videoconferencing solutions are a key technological resource enabling so called synchronous hybrid education, i.e., teaching which simultaneously allows on-site and remote participation for students.However, new (educational) technologies have also been criticized for unsubstantiated hype and lack of impact on teaching practices (e.g. Selwyn, 2016). In this presentation, I investigate how one recent videoconferencing technology, the telepresence robot, is used as a tool in hybrid language education. Unlike autonomous or semi-autonomous social robots, the telepresence robot isa remotely controlled and moveable videoconferencing device,a kind of a material representation or 'proxy' of the remote participant in another location, such as the classroom.
The presentation is based on an on-going project, currently including 11 lessons of video-recorded language teaching interaction (Finnish, Swedish, English, German) taught at university-level in Finland. In these face-to-face lessons, a Double 2 or 3 robot is used to enable remote student participation. The robot is located in the classroom, and can be connected by a remote student via internet to set up a videocallwith the classroom-based teacher and students. Unlike with many other videoconferencing tools, the remote participant can control and shift visual attention by turning and moving the robot's position and orientation in the classroom space.
In this presentation, I address the research gap regarding multimodal practices of hybrid education (e.g. Rae et al., 2020) and focuson remote, robot-mediated participation in instructional activities involving the use of a classroom whiteboard or blackboard. Drawing on the methodological perspective of multimodal conversation analysis (e.g. Sidnell & Stivers, 2012),Iaim to show how the telepresence robot is managed by participants in ways that enable the remote student to follow, and participate in, board work. This includes practices for initiating board-centered interactions, identifying and navigating the robot into classroom locations that provide sufficient visibility to the board, attending to situationally-relevant text on the board, and closing board-centered instructional activities.
The findings are expected to shed light on asymmetries in multimodal participation between classroom-based and remote students in hybrid education, and social practices that participants deploy to overcome such asymmetries.More broadly, the findings will also illustrate how educational practices are shaped by new kinds of material and technological spaces. The presentation will conclude by discussing the implications of the study's findings, and of telepresence robots more broadly, for hybrid teaching praxis.
References
Raes, A., Detienne, L., Windey, I., & Depaepe, F. (2020). A systematic literature review on synchronous hybrid learning: Gaps identified. Learning Environments Research, 23(3), 269–290. Selwyn, N. (2016). Minding our language: why education and technology is full of bullshit… and what might be done about it. Learning, Media and Technology, 41(3), 437–443. Sidnell, J., & Stivers, T. (Eds.). (2012). The handbook of conversation analysis. Malden, MA: John Wiley & Sons.
The willingness to respond to and communicate with a robot in an interactive learning task in French.
Oral Presentation[SYMP19] Applied Linguistics perspectives on human-robot interaction in language education: possibilities and challenges10:15 AM - 01:15 PM (Europe/Amsterdam) 2023/07/19 08:15:00 UTC - 2024/07/19 11:15:00 UTC
The most important function of a language is to get the message across. With robot-assisted language learning (RALL), a teacher-led situation has a complementary element when the learner can interact independently with the robot. Starting a new sentence is always a threshold you must dare to cross - it involves the student's willingness to communicate (MacIntyre 2007) and the student's public speaking language anxiety (Yaikhong, K. & Usaha, S., 2012).
Previous studies have shown that feminine and human-like features in a robot contribute to experiencing the robot as warm and competent. However, it is not yet known how the robot's attributes correlate with the desire to interact with the robot (Carpinella et al., 2017). Moreover, the RoSAS scale has been tested with images and videos, but has not yet been validated using robots in real-life situations. In this study, the robot is presented as a gender-neutral character. It has also been suggested that negative attitudes toward robots and anxiety in L2 learning may prevent participants from learning in robotic tutor mode (Kaneiro et al., 2022).
This study is a long-term study: the research design is a continuation of a study on the relationship between a child and a robot (Peura, L. & Johansson M., 2022 sub.), in which students interviewed the robot. In this research setup, the robot interviews the student. The study aims to find out how the interaction between a child and a social robot (CHI) affects the willingness to communicate in French (L2) when the robot is proactive.
RQ1: How does the robot affect the stress associated with speech production?
RQ2: How do the features of the robot correlate with the desire to interact with the robot?
The target group of the study is 10–11-year-old students (4th graders who studied with a robot and 5th graders who studied without a robot as a comparison group), for whom French is the first foreign language (L2). The research material consists of videotaped conversations with the robot and related surveys (RoSaS, PSCAS). RALL and multimodal interaction analysis serve as a methodological approach to research questions. Bibliography: Carpinella, C. M., Wyman, A. B., Perez, M. A., & Stroessner, S. J. (2017). The robotic social attributes scale (RoSAS) development and validation. Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, 254–262. Kanero, J., Oranç, C., Koşkulu, S., Kumkale, G. T., Göksun, T., & Küntay, A. C. (2022). Are tutor robots for everyone? the influence of attitudes, anxiety, and personality on robot-led language learning. International Journal of Social Robotics, 14(2), 297-312.
MacIntyre, P. D. (2007). Willingness to communicate in the second language: Understanding the decision to speak as a volitional process. The modern language journal, 91(4), 564-576.
Peura, L., Johansson M., A Friend or a Machine? A Study on the child-robot relationship in a foreign language class of young learners. Sub.
Yaikhong, K., & Usaha, S. (2012). A Measure of EFL Public Speaking Class Anxiety: Scale Development and Preliminary Validation and Reliability. English Language Teaching, 5(12), 23-35.
Hilla-Marja Honkalammi