An Instructional Application of the Multiple-Choice Cloze: A Case Study in the EFL Classroom
Bibliographic record
Abstract
We explored using multiple-choice cloze (MCC) tests for classroom instruction. The practice of “testing leading teaching” is frequently criticized because it might distort the original teaching objectives. We do not primarily emphasize how to get high scores; instead, we show how to use testing techniques and teaching activities to provide feedback that energizes teaching methods and increases learning effectiveness. We analyzed MCC test-taking strategies, which include leading students to: 1) skim for the first and the last sentence in cloze passages; 2) read the whole cloze passage to grasp its general idea; 3) look for contextual clues; 4) orally express (“thinking out loud”) their reasons for choosing one MCC test item instead of another; and 5) conduct group discussions. Finally, 6) teachers guided the entire class, discussed contextual and situational clues, and provided feedback about student choices and reasons. The experimental design of this research primarily compared the performance between two groups: Experimental and Control. Differences in cloze scores between the two groups were significant, but differences in reading comprehension scores were not. After six 25-minute MCC test lessons, Experimental group students had better MCC test scores than did Control group students. Our findings supported our hypothesis that MCC instruction, even for a short time, would improve performance on a cloze test. We also discuss how to use MCC tests to teach strategies for answering MCC test items.
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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".