An Evaluative Study of Memorization as a Strategy for Learning English
Bibliographic record
Abstract
This exploratory study elicits learners’ views regarding the utility of using memorization as a strategy for learning English. It exclusively investigates the extent of learners’ use of the memorization strategy, the reasons that motivate them to memorize, the problems they encounter and the techniques they resort to to overcome these problems. 66 undergraduate participants answered a thirty-item questionnaire. The results revealed that most efficient as well as inefficient learners used the memorization strategy mainly for learning vocabulary, definitions, and literary texts. They were in favour of using this strategy because it helps them improve their achievements in English. It was found that understanding should be given priority over the memorization activity. Learners who adopt this strategy often forget what they memorized, could not differentiate between important and unimportant information, and were incompetent to make inferences. It can be concluded that memorization is a low-level cognitive strategy that can be used among other high- level cognitive strategies in the process of learning English.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".