Error Analysis of Students Essays: A Case of First Year Students of the University of Health and Allied Sciences
Bibliographic record
Abstract
Writing is considered as a daunting task in second language learning. It is argued by most scholars that this challenge is not only limited to second language speakers of English but even to those who speak English as their first language. Thus, the ability to communicate effectively in English by both native and non-native speakers requires intensive and specialized instruction. Due to the integral role that writing plays in students’ academic life, academic literacy has garnered considerable attention in several English-medium universities in which Ghanaian universities are no exception. It is therefore surprising that prominence is not given to Academic Writing and Communicative Skills at the University of Health and Allied Sciences (UHAS). In this paper, I argue for much time and space to be given to Academic Writing and Communicative Skills, a programme that seeks to train students to acquire the needed skills and competence in English for their academic and professional development. This argument is based on the findings that came out after I explored the errors in a corpus of 50 essays written by first year students of UHAS. The findings revealed that after going through the Communicative Skills programme for two semesters, students still have serious challenges of writing error-free texts. Out of the 50 scripts that were analyzed, 1,050 errors were detected. The study further revealed that 584 (55.6%) of these errors were related to grammatical errors, 442 (42.1%) were mechanical errors and 24 (2.3%) of the errors detected were linked to the poor structuring of sentences. Based on these findings, recommendations and implications which are significant to educators, policy makers and curriculum developers are provided. This study has implications for pedagogy and further research in error analysis.
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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 | 0.004 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".