| Autor: |
Han, Songhee, Liu, Min, Pan, Zilong, Cai, Ying, Shao, Peixia |
| Předmět: |
|
| Zdroj: |
International Journal of Artificial Intelligence in Education (Springer Science & Business Media B.V.); Sep2023, Vol. 33 Issue 3, p752-780, 29p |
| Abstrakt: |
In this study, we examined interaction behaviors between a small number of participants in two massive open online courses (MOOCs) and an FAQ chatbot, focusing on the participants' native language markers. We used a binary native language marker (non-native English user vs. native English user) to distinguish between two groups in this multiple case study, which is based on the knowledge gained from a previous study reporting students' significantly different experiences based on the two language markers (Han & Lee, Computer & Education, 179, Article 104395, 2022). We utilized a multimodal computer-mediated communication approach as a research guiding tool for examining the data—chatbot logs and the students' self-reported open-ended responses. We analyzed 42 students' (non-native English users: n = 27 and native English users: n = 15) precise language uses and constructional practices during student–chatbot interactions to investigate the similarities and differences between the two cases. Most importantly, we focused on the experiences of non-native English users with newly adopted technology, such as chatbots, highlighting a lack of attention to the challenges this group faces despite comprising the majority of MOOC students. By comparing the interactions between the two groups, we identified various student attitudes towards the chatbot with accompanying descriptions, hypothesizing that non-native English users' positioning effects might have contributed to their negative experiences with the chatbot. We also found that misinterpreted contextualized cues could influence non-native English users more adversely than native English users from an interactional sociolinguistics perspective. Thus, we provide chatbot response design strategies that could support non-native English users better, such as informing users of the chatbot's capabilities and limitations as early as possible during interactions. We argue that concentrating on the experiences of non-native English users with chatbots promotes a more inclusive learning environment regarding new technologies employed in MOOCs. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
|
Full text from SpringerLink