Enhancing Vocabulary Acquisition in EFL Education: A Mixed-Methods Analysis of Digital Minimalism and Technology Use
Bibliographic record
Abstract
This study evaluates the effects of digital minimalism versus digital tool-assisted learning on the vocabulary retention of EFL students in the higher education context. It explores vocabulary acquisition using technology avoidance learning strategies as well as technology-assisted ones to inform the effectiveness of both means and identify the helpful learning strategies in boosting vocabulary acquisition. A quasi-experimental mixed methods design was adopted, and 87 participants were placed in either the digital tools or digital minimalism group. Pre-and post-tests with the given vocabulary items were conducted to assess the results, and qualitative data collection subsequently assisted researchers in recording learner experiences. The digital tools group performed better in post-test evaluation as their interactive digital tools provided comprehensive feedback and better learning outcomes. The digital minimalism approach produced students who demonstrated better focus, fewer distractions and enhanced task engagement. The study reveals that while digital tools enhance learning participation, digital minimalism practices improve mental processing and sustainable education methods. The research results validate hybrid instruction frameworks and emphasize the need to control cognitive load when using technology-based systems. Further research is required to explore long-term retention and the role of student self-regulation to optimize digital learning methods.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".