Effects of Mobile App on Memory Retention of Vocabulary Knowledge among Low Proficiency EFL Learners
Bibliographic record
Abstract
A significant challenge in EFL vocabulary learning is ensuring long-term retention and effective use of newly acquired words, often hindered by limited exposure and meaningful practice. Considerable research has been conducted on mobile technologies for vocabulary learning in a second language (L2), but the comprehensive mastery of EFL vocabulary form, meaning, and use via mobile platforms in short-term and long-term memory has rarely been addressed. This quasi-experimental study investigated the effects of a mobile vocabulary app versus a paper-based wordlist on high-frequency core vocabulary from CET 4 among Chinese university students. Data were collected from 82 EFL freshmen at a private university in China from two intact groups. The experimental group used the Bai Cizhan app for out-of-classroom learning, while the control group used traditional paper-based methods. Vocabulary knowledge was tested through pretests, immediate recall tests, and delayed recall post-tests. Findings indicated that Bai Cizhan group significantly enhanced L2 vocabulary learning in improving high-frequency core words vocabulary in terms of form and meaning (Form: F (1, 80) = 23.957, p < .05, η2 = .230; Meaning: F (1, 80) = 16.342, p < .05, η2 = .170) in short-term memory, but no significant difference (Wilks’ Lambda=.187, F(3, 78)=1.641; P>.05) in long-term memory. This study provides empirical evidence for the effectiveness of mobile-assisted vocabulary learning and offers insights into meeting the vocabulary needs of EFL learners.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".