Spacing techniques in second language vocabulary acquisition: Short-term gains vs. long-term memory
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article reports the results of two experiments using the spacing technique (Leitner, 1972; Landauer & Bjork, 1978) in second language vocabulary acquisition. In the past, studies in this area have produced mixed results attempting to differentiate between massed, uniform and expanded intervals of spacing (Balota, Duchek, & Logan, 2007). A particular problem has been the point of testing that did not draw a clear line between short-term gains and long-term retention (Roediger & Karpicke, 2010). The experiments presented in this article addressed this issue. In the first experiment, 76 university students enrolled in a Beginning German class learned 24 content and 15 function words during a practice phase with a ‘one plus three’ design followed by three delayed post-tests. Results showed that in regards to short-term gains, the expanded group obtained higher mean scores than the uniform group, whereas in the long-term test it was the other way round. The second experiment used the same methodology with one exception: the practice phase was increased to a ‘one plus four’ design. Results confirmed those of the first experiment; in addition it was shown that function words are particularly difficult to recall for students using the expanded interval.
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.
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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.050 | 0.001 |
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