The Effect of Instruction Based on Multiple Intelligences Theory on the Attitude and Learning of General English
Bibliographic record
Abstract
The purpose of the present study is investigating the effect of instruction based on Multiple intelligence (MI) theory on attitude and learning of General English course among students of Islamic Azad University, Kermanshah Branch in the second semester of educational year of 2010-2011. 61 male and female students in two different classes participated in the present study that were assigned to experimental (32 students) and control (29students) groups based on random cluster sampling. A quasi experimental method of research with a pre- and post test was used. The experimental group was taught according to the theory of MI and the control group was instructed based on the traditional method of teaching General English in eight weeks time. In order to determine the effect of MI-based instruction compared with traditional method, a researcher constructed test including 30 items were utilized. In order to assess the attitude of the learners toward English, a 15 items scale of attitude toward English Language was also employed. For analyzing data we used ANCOVA and independent sample t-test. The results of the study indicated that there was a significant difference between improving in General English course between experimental and control groups. In other words, students taught based on MI theory exceeded the traditionally instructed students both in general and in each sub-skill of learning English (vocabulary, reading comprehension, and structure). The results also indicated that attitude of students towards learning English in experimental group improved significantly.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".