Assessing the Causes of Undergraduate Management Students’ Reading Difficulties
Bibliographic record
Abstract
Reading is a vital skill that everyone must develop. Therefore, this study aimed to assess the major causes of management students’ reading comprehension difficulties at Rift Valley University. For this study, descriptive research and a mixed research approach were employed. One hundred twenty-one samples were selected for the survey. A questionnaire and a classroom observation were used to collect data. SPSS version 26 was employed for the analysis of inferential statistics. The result identified the significant factors affecting students’ reading comprehension. These factors are a lack of good teaching methods, a lack of practice, a limited number of reading activities, uninteresting reading activities, and a lack of knowledge of reading strategy. Teachers teaching methodology and reading activities are the most determinant factors that affect students’ reading comprehension. The results of an analysis of variance (ANOVA) that includes multiple comparisons among "mother tongue," "national language," and "English language." that the p-values show less than 0.05 which provides strong evidence that there was a significant difference among the groups. Besides the result of the correlation analysis reveals that majority of pairwise correlation coefficients are high and positive. Mobile device is suggested as an effective tool which is used to improve students’ reading skills.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".