Exploring Factors Causing Demotivation and Motivation in Learning English Language among College Students of Quetta, Pakistan
Bibliographic record
Abstract
The prime aim of this research was to determine both demotivating and motivating factors for Pakistani college students of Quetta in learning English language. A quantitative design was employed in which 150 freshman college students studying in three different disciplines: Pre-medical, Pre-engineering and I.C.S at Government Girls college students of Quetta, Pakistan were included. A demotivation questionnaire was adopted from the study by Sakai & Kikuchi(2009) consisting of one open-ended question and 35 close-ended items on six factors of demotivation: grammar-based teaching, teacher’s behaviour, course contents and teaching materials, effects of low test score, classroom environment and lack of self-confidence and interest. Additionally, a modified 20-items AMTB motivation questionnaire along with one open-ended question was adapted from the study by Gardner (1985) which identifies the integrative and instrumental motivation. The closed ended questionnaire was analyzed applying descriptive statistics in SPSS (version, 22) whereas content analysis was performed on narrative data extracted from open-ended questionnaire and was quantified to establish the order and rank of factors causing motivation and demotivation among students in learning English language. The findings revealed that course content and teaching material emerged as the most salient demotivating factor. On the other hand, instrumental motivation emerged as the most influential source of motivation among students. The findings have implication on both teaching and learning of English language in Pakistan.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".