• Search Research Projects
  • Search Researchers
  • How to Use
  1. Back to project page

2019 Fiscal Year Annual Research Report

An Investigation into High-Feedback Engagement Factors in Technology-Enhanced-Learning Materials that tap into the OCEAN Personality Traits of ESL Learners

Research Project

Project/Area Number 16K02896
Research InstitutionTokai University

Principal Investigator

Robert Cvitkovic  東海大学, 国際教育センター, 講師 (00412627)

Co-Investigator(Kenkyū-buntansha) ボビー ヒロユキ  九州産業大学, 語学教育研究センター, 准教授 (20536247)
Praver Max  名城大学, 外国語学部, 准教授 (60598712)
Project Period (FY) 2016-04-01 – 2020-03-31
Keywordspersonality traits / feedback
Outline of Annual Research Achievements

Research has consistently shown an existing relationship between personality traits and preferred learning styles. However, one area that has received little attention, is the connection between personality traits and learning preferences through different modalities, specifically mobile apps. As the landscape of education is transforming with the introduction of mobile digital technologies, we set out to ascertain the degree to which personality traits, English proficiency levels, and app usage patterns correlate with preferences to learn English using mobile apps. Following the validation of a new instrument measuring Perceived Value of Educational English Apps, a follow up survey comprising 70 items was administered to 3000 Japanese university students. The aim was to determine the degree of correlation between perceived app value and 1) OCEAN five dimensions of personality, 2) six facets of conscientiousness, 3) five English proficiency levels, 4) educational app usage patterns, and 5) regulation autonomy index (RAI). Multiple linear regression results showed significant positive correlations between app value and English proficiency, RAI, and app usage patterns. Furthermore, significant positive correlations also were found when personality traits were grouped using cluster analysis.

URL: 

Published: 2021-01-27  

Information User Guide FAQ News Terms of Use Attribution of KAKENHI

Powered by NII kakenhi