Effects of Mobile App on Memory Retention of Vocabulary Knowledge among Low Proficiency EFL Learners
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it