246
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Exploring the effects of cross-cultural collaborating on bilingual digital storytelling on students’ intercultural learning

ORCID Icon, ORCID Icon & ORCID Icon
Received 05 Jul 2023, Accepted 16 Nov 2023, Published online: 17 Dec 2023
 

ABSTRACT

As intercultural learning has been increasingly regarded as essential in higher education, researchers have promoted it through integration of digital storytelling into cultural exchange activities. However, most studies have focused on the utilization of only one language whereas the effects of multilingual digital storytelling on students’ intercultural learning is understudied. To help bridge this gap, in this study, the effects of creating bilingual digital stories with students in an English-speaking country on the intercultural learning of students in a Chinese-speaking country were investigated. Data included students’ learning portfolios and reflective essays and were analyzed utilizing content analysis and matrix coding query. The findings revealed that the students became more critically aware of their own and others’ cultural diversity and sensitive to the uses of both English and Chinese language and multimedia resources for communication with people from different cultural backgrounds. They also reported multiple advantages associated with their cross-cultural online interactions, specifically in the domains of language acquisition and cultural understanding. However, they also highlighted challenges, notably the difficulties in coordinating meeting schedules and overcoming communication barriers. Implications and limitations are also discussed.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by National Science and Technology Council: [grant number 112-2628-H-224 -001 -MY3].

Notes on contributors

Cheng-Yueh Jao

Cheng-Yueh Jao is a research fellow in the Department of Applied Foreign Languages. Her research interests lie in computer-enhanced language learning, robot-assisted language learning, and multimodality.

Ching-Huei Chen

Ching-Huei Chen is currently a Professor of the Department of Industrial Education and Technology at National Changhua University of Education in Taiwan. Her scholarly interests involve design and development of effective game-based learning environment, learning analytics strategies, tools and applications, professional development for in-service teachers, and innovative teaching methods and strategies. Dr. Chen has been published in Computers and Education, British Journal of Educational Technology, Journal of Computer-Assisted Learning, Computers in Human Behaviors, among other international educational journals.

Hui-Chin Yeh

Hui-Chin Yeh, Distinguished Professor, is acknowledged in the Scopus database as one of the World’s Top 2% Scientists for the year 2023. Professor Yeh’s scholarly pursuits encompass a broad spectrum of cutting-edge educational technologies and methodologies. Her academic journey is distinguished by the publication of over 70 journal papers, with a remarkable 85% of these contributions being featured in SSCI-indexed journals. Her scholarly reputation is further enhanced by her being honoured as a recipient of the Distinguished Young Scholar Awards from the National Science Technology Council in Taiwan for four times, in the years 2017, 2011, 2020,and 2023. In addition, in recognition of several different language systems developed and five patent achievements, she was honored with the TW Patent DB.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 53.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 296.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.