Academic writing and ChatGPT: Students transitioning into college in the shadow of the COVID-19 pandemic
Bibliographic record
Abstract
Abstract This paper reflects on an educator's perceived experiences and observations on the complex process of ‘passage’ when students transitioning from high school into their first-year of post-secondary education often struggle to adapt to academic writing standards. It relies on literature to further explore such a process. Written communication has become increasingly popular in formal academic and professional settings, stressing the need for effective formal writing skills. The development of online tools for aiding writing is not a new concept, but a new software development known as ChatGPT, may add to the many challenges academic writing has faced over the years. This paper reflects on the students' struggles as they navigate different courses seeking to adapt their writing skills to formal and structured written academic requirements. The COVID-19 pandemic forced many recent high school students into virtual education, uncertain of its effectiveness in developing the writing skills high school graduates require in academia. Many unknowns exist in using ChatGPT in academic contexts, especially in writing. ChatGPT can generate texts independently, raising concerns about plagiarism and its impact on students' critical thinking and writing skills. This paper hopes to contribute to pedagogical discussions on the current challenges surrounding the use of artificial intelligence technology and how better to support beginner writers in academia.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".