A Spacing Contribution on the Retention of L2 Word Forms and Meanings
Bibliographic record
Abstract
This study examined the contribution of spacing on facilitating learning and retention of L2 word forms and meanings. 38 Saudi Arabian learners of English studied 30 English words (beyond their current language proficiency) using a spaced and massed displays. The former refers to the frequent distribution of repetition across multiple learning sessions, whereas the latter deals with the repetition of words into one single learning session. The 15 lexemes, hence, under the massed condition were classified into three categories of five words, each set was studied three times in one of the three sessions, whereas the other set of words were studied under the spaced condition in which all 15 words were learned once in each of the three sessions. One offline test was conducted to measure the meaning of word knowledge, and one online lexical decision task measured respondents’ accuracy and speed in recognizing the form of target words. These two tests were administrated in immediate post-test (IPT) and delayed post-test (DPT), conducted two weeks later. The results show that the meaning and form of spaced L2 words are learned and retained better than those of massed L2 words. The reaction time (RT) results also show that L2 learners who are under the spaced condition are faster in recognizing L2 target word forms than those who are under the massed condition. These findings can have meaningful theoretical and pedagogical implications for developing L2 vocabulary learning and retention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.005 |
| 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 | 0.001 | 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".