A Study of the Relationship between Iranian EFL Learners’ Level of Spatial Intelligence and Their Performance on Analytical and Perceptual Cloze Tests
Bibliographic record
Abstract
During the last two decades, Gardner’s theory of multiple intelligences with its emphasis on learner variables has been appreciated in language learning. Spatial intelligence, as one domain of the multiple structures of intelligence, which is thought to play a great role in reading, writing, and literacy, particularly in L2 learning, has not received sufficient attention as it deserves. The aim of this study was twofold: (1) to examine the relationship between EFL learners’ spatial intelligence and their performance on cloze tests in general; (2) to determine which variety of cloze tests, analytic or perceptual (based on deletion method), may correlate more strongly with learners’ spatial intelligence. Accordingly, a correlational study was conducted with 41 male Iranian EFL learners at Jihad Daneshgahi Language Center of Tehran University (Iran). Participants’ scores on the spatial intelligence test were first compared with their scores on two cloze tests. Next, the obtained correlations were examined to see the effect of deletion method for cloze tests on the strength of the relationship between the spatial intelligence scores and scores on the cloze tests. Significant correlations (0.61 and 0.56) were found between the variables. The findings emphasize making reconsiderations in using cloze tests in EFL contexts. Further research is also suggested to explore spatial intelligence and its role in language classrooms.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".