An Investigation on Scientific Writing Difficulties & Writing Process
Bibliographic record
Abstract
Scientific writing is the process of conveying scientific information clearly. However, many students struggle with learning scientific writing, making it challenging for them to explain their findings in a clear and logical manner. This issue underscores the need for investigating scientific writing difficulties and the writing process, ensuring that students develop their writing skills. Thus, the purpose of this study is to investigate how learners perceive the application of learning strategies when writing scientific reports. This qualitative study aims to analyze the relationship between writing challenges and the composition process, as well as how learners perceive their writing issues and the composition process. This study employs a qualitative method, using questionnaires to collect data. The subjects were science and engineering students who needed to prepare scientific reports. 145 participants purposively responded to a qualitative survey from the science and engineering disciplines. The findings showed that paragraphing issues and writing uncertainty are the most common writing challenges. These data indicate that a lack of writing experience causes writing difficulties. The findings from this study contribute to the body of knowledge regarding why learners find their writing tasks difficult. Additionally, the results can be used to make improvements at the institutional or personal level.
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 | 0.019 | 0.122 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".