The Link between English Language Proficiency and Academic Performance: A Pedagogical Perspective in Tanzanian Secondary Schools
Bibliographic record
Abstract
English, the Language of Instruction (LOI), has fallen victim of blame of some scholars as the cause of poor performance in secondary schools in Tanzania. The blame is directed at English strongly up until recent years (2010 and 2011) when the results of national examinations have been extremely worse. It seems, the public presumes that there is relationship between students’ academic success and their abilities in the LOI. This paper sought to investigate whether there is relationship between English Language Proficiency (ELP) and academic performance in Tanzanian secondary schools. The data used were obtained by administering an ELP test and a review of students’ reports, and were quantitatively analyzed using a computer software, Statistical Package for Software System (SPSS) version 18. The study revealed that there is a significant weak positive relationship between ELP and students’ academic achievement. The relationship was significant in English and insignificant in other subjects which were investigated. On the basis of the findings of this study, it is argued that academic success is a function of several variables and not only proficiency in the LOI. It is therefore recommended that the responsible authorities should invest in improving English language proficiency among students since ELP is positively connected with academic success.
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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".