Visualizing the Behavior of Learning European Portuguese in Different Regions of the World Through a Mobile Application

Research output: Contribution to journalArticlepeer-review

Abstract

This study explores the 'Diz lá!' mobile application, an innovative tool released in 2018 that aims to enable users, especially Chinese speakers, to learn Portuguese. This mobile application harnesses the principles of Mobile-Assisted Language Learning (MALL) and Self-Determination Theory (SDT), facilitating continued language education amid the COVID-19 pandemic. Our research exploits user habits reflected in more than five years of data to build multi-dimensional models for visualizing large datasets, with a focus on learning patterns related to verb conjugation. Insights reveal that most users are language school students from Macao, China, and Portuguese-speaking counties/regions, with a remarkable preference for learning Portuguese verb conjugations. The research results also show that people like to learn verbs for comparison. Notably, despite the pandemic, an upward trend was observed in the learning of these conjugations. Our findings offer crucial implications for the design of pedagogical strategies and refinement of language learning (MALL) apps, highlighting areas of difficulty and learner preferences. We also used machine learning (ML) technologies to create a predictive model to recommend relevant learning materials to users. As a result, this study stands at the intersection of technology-enhanced language learning and educational research, demonstrating how they can synergistically contribute to optimizing language learning outcomes.

Original languageEnglish
Pages (from-to)113913-113930
Number of pages18
JournalIEEE Access
Volume11
DOIs
Publication statusPublished - 2023

Keywords

  • COVID-19 pandemic
  • Conjugator
  • Diz lá!
  • European Portuguese
  • data visualization
  • learning patterns
  • mobile-assisted language learning
  • second language acquisition
  • self-determination theory
  • star schema data model

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