Effect of Educational Technology on Students’ Foreign Language Anxiety: A Thematic Literature Review
Bibliographic record
Abstract
Foreign language anxiety (FLA), as a common affective filter, has impeded the language learning process. In order to reduce the FLA, researchers have explored several methods via enhancing language learning settings. Technology as a crucial tool in improving the learning environment has been considered on this topic as well. However, the results of these empirical studies are inconsistent. After reviewing 24 relevant experimental and quasi-experimental research articles from 2016 to 2021 and calculating the effect size for each article, it is evident that 46% of studies reported that technology-assisted instruction significantly decreased FLA, and 54% had no significant effect on FLA. Therefore, this paper aimed to examine the overall effect size on the topic and explore the moderators that caused these inconsistent results through examining five potential moderators (technology type, using methods for integrating technology into a foreign language classroom, exposure duration of technology in experimental groups, FLA type and target language) from the reviewed studies, which are based on the sources of FLA (Young, 1991; Yan & Horwitz, 2008). Two moderators (using methods for integrating technology into foreign language classrooms and target language) were found to get significant predictions on the effect of technology on students' FLA, p<0.05. These findings provide educators, researchers, and practitioners a new direction for future research on different methods of teaching the target language using suitable technology in the classroom.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".