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Learners' continuance intention in multimodal language learning education: An innovative multiple linear regression

Yan Huang1,2, Wei Xu3, Paisan Sukjairungwattana4

  • 1School of Foreign Languages, East China Normal University, 200241, Shanghai City, China.

Heliyon
|April 1, 2024
PubMed
Summary

Multimodal language learning, enhanced by technology, positively influences learner continuance intention. Factors like perceived usefulness and ease of use are key, though personal investment does not directly predict continued engagement.

Keywords:
Continuance intentionMultimodal language learning educationMultiple linear regression modelPersonal investment

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Area of Science:

  • Educational Technology
  • Applied Linguistics
  • Online Learning

Background:

  • The COVID-19 pandemic necessitated widespread adoption of multimodal language learning education.
  • Learners engaged with blended approaches including Massive Open Online Courses, Rain Classroom, and WeChat.
  • Understanding factors influencing continued participation is crucial for effective online education.

Purpose of the Study:

  • To explore factors influencing learners' continuance intention in multimodal language learning.
  • To propose and test a multiple linear regression model for multimodal education.
  • To identify key predictors of sustained engagement in online language learning.

Main Methods:

  • A quantitative study involving 334 participants in China who experienced multimodal language learning.
  • Data collection via a comprehensive online questionnaire administered through Questionnaire Star.
  • Analysis using multiple linear regression to test hypotheses and model fit.

Main Results:

  • Perceived ease of use, perceived usefulness, and attitudes positively correlate with continuance intention.
  • Technology Acceptance Model constructs, Task-technology fit, Individual-technology fit, Openness, and Reputation are associated with engagement.
  • Personal investment in multimodal language learning did not directly or indirectly predict continuance intention.

Conclusions:

  • Technology acceptance factors significantly predict sustained participation in multimodal language learning.
  • While personal investment is important, its direct link to continuance intention needs further investigation.
  • Educators should focus on enhancing perceived value and ease of use to improve learner retention in online language programs.