Effects of Basic Versus Cloze Flashcard Formats on Learning Using Anki Software
Bibliographic record
Abstract
Prior research has found that flashcards are an effective study technique because they employ practice testing and distributed practice (Dunlosky et al., 2013). However, little research has examined whether different flashcard formats impact memory recall. The present study compares Basic (front/back) and Cloze (fill-in-the-blank) flashcard types using the flashcard software Anki. The population of interest is university students. A nonrandom convenience sample of 10 participants from the University of Alberta studied 30 imaginary country-capital pairs using either format, reviewing at spaced intervals before a recall test. It was hypothesized that the Cloze format would enhance recall due to the dual encoding of sentence fluency. Results showed that the Cloze group recalled 77% of items on average, compared to 58% in the Basic group. A Mann-Whitney U test indicated a significant difference (p = .035), and Bayesian logistic regression confirmed moderate evidence for the Cloze format (BF₁₀ = 4.26), with a posterior difference of 17.3% (95% CrI [6.6%, 27.9%]). These results support the hypothesis that Cloze flashcards enhance memory by leveraging sentence fluency and dual encoding. Implications are relevant for anyone who wants to study effectively, particularly those who use flashcards. Further research with larger and more diverse samples is needed to confirm and generalize these findings.
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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 | 0.004 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".