Experiences of Former Markers of Undergraduate Assignments and Examinations at A University: A Case Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PurposeThe present study explored experiences of former markers of undergraduate assignments and examinations at the University of South Africa (Unisa). MethodologyQualitative method of research was used to gather data. Colaizzi’s method (1978) was used to analyze and interpret data. The article’s frame of reference was informed by Mezirow’s transformative learning theory which is aligned to critical theory (Mezirow 2009).FindingsFindings were based on the following: Demographic information, markers’ experiences in marking assignments and exam books, content knowledge, markers’ meetings, duration of marking assignments and examination books as well as students support, and suggestions are the themes that emerged from the data that was gathered.OriginalityIt is recommended that the university must develop a policy for external markers for marking assignments and examinations of undergraduate program. All E-tutors must be trained to support students after the official closure of registration and before examinations are set. All markers must be trained - through a markers’ guide - to mark assignments and examination books.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it