EFL Learners’ Use of Data-driven Learning and their Attitudes in the Improvement of English-speaking Skills

The use of data-driven learning (DDL) to improve the English-speaking skills of learners of English as a foreign language (EFL) remains rare. Hence, this research examines whether two types of DDL – namely, hands-on DDL using the computer software directly and hands-off DDL using paper-based materials – can improve vocabulary production in the English speaking of EFL learners. The EFL learners’ attitudes toward both types of DDL were also examined. A total of 45 Thai EFL learners were involved in this research; they were divided equally into two experimental groups, one using hands-on DDL and hands-off DDL, and one control group. A questionnaire and interviews were used to examine the EFL learners’ attitudes toward DDL and a paired sample t-test and a one-way analysis of variance (ANOVA) were conducted. The results reveal that both hands-on and hands-off DDL approaches significantly improved vocabulary production in the EFL learners’ spoken English. In addition, the hands-on DDL had a significant effect on the quantity (sig. = .000, p <0.05), accuracy (sig. = .000, p <0.05), and complexity (sig. = .000, p <0.05) of the participants’ vocabulary production, while the hands-off DDL only had a significant effect on the accuracy (sig. = .000, p <0.05) of vocabulary production. Furthermore, although the EFL learners had relatively positive attitudes toward DDL, less enjoyable experiences were also noted. Experiences of boredom and stress while using DDL were reported, and the participants did not consider DDL to be suitable for all EFL learners.

Authors

Institutions

Publication Details

Journal
International Journal of Learning Teaching and Educational Research
Published
2024-08-30
DOI
https://doi.org/10.26803/ijlter.23.8.6
Citations
1
Primary Topic
Second Language Learning and Teaching
Type
article
Field-Weighted Citation Impact
3.91
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

EFL Learners’ Use of Data-driven Learning and their Attitudes in the Improvement of English-speaking Skills

Zhaoyi Pan
1 citations
International Journal of Learning Teaching and Educational Research
Second Language Learning and Teaching
3.91
article

EFL Learners’ Use of Data-driven Learning and their Attitudes in the Improvement of English-speaking Skills

Zhaoyi Pan
article en
1 citations

Abstract

The use of data-driven learning (DDL) to improve the English-speaking skills of learners of English as a foreign language (EFL) remains rare. Hence, this research examines whether two types of DDL – namely, hands-on DDL using the computer software directly and hands-off DDL using paper-based materials – can improve vocabulary production in the English speaking of EFL learners. The EFL learners’ attitudes toward both types of DDL were also examined. A total of 45 Thai EFL learners were involved in this research; they were divided equally into two experimental groups, one using hands-on DDL and hands-off DDL, and one control group. A questionnaire and interviews were used to examine the EFL learners’ attitudes toward DDL and a paired sample t-test and a one-way analysis of variance (ANOVA) were conducted. The results reveal that both hands-on and hands-off DDL approaches significantly improved vocabulary production in the EFL learners’ spoken English. In addition, the hands-on DDL had a significant effect on the quantity (sig. = .000, p <0.05), accuracy (sig. = .000, p <0.05), and complexity (sig. = .000, p <0.05) of the participants’ vocabulary production, while the hands-off DDL only had a significant effect on the accuracy (sig. = .000, p <0.05) of vocabulary production. Furthermore, although the EFL learners had relatively positive attitudes toward DDL, less enjoyable experiences were also noted. Experiences of boredom and stress while using DDL were reported, and the participants did not consider DDL to be suitable for all EFL learners.

International Journal of Learning Teaching and Educational ResearchVol. 23(8)
King Mongkut's University of Technology Thonburi (TH)
Openalex Percentile: Top 6%
Second Language Learning and Teaching
3.91
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.