Online grading platform: A mixed methods approach to measuring impact on grading experience
Bibliographic record
Abstract
Nearly a decade ago a large first year engineering design course moved the collaboratively writtendesign report assignments to an online platform. The switch was made using an existing online wordprocessing tool, Google Drive, that allow for simple sharing and commenting. The students use theonline tool to write their assignments, and members of the teaching team use the same tool to coach andgrade the assignments. Anecdotally there was initially significant evaluator resistance to theimplementation of the online grading platform. This initial resistance has been overcome and the onlinetool continues to be used today. Anecdotal feedback from the teaching team now praises the onlinegrading platform as increasing quality of feedback, but at the expense of increased marking time. Untilrecently the exams in the same course are still written and marked on paper in the traditional style. Forthe first time the teaching team has adopted another online grading platform, Crowdmark. This toolallows for the digitization, online grading, and digital distribution of paper exams. In anticipation ofevaluator resistance, this study will explore how use of this system impacts the quality of the gradingexperience for evaluators, including time on task and satisfaction with the process. This study will use amultiphase mixed methods design with an initial phase of convergent parallel design focusing onquantitative analysis. Time study data measuring time on task for evaluators will be converged with bothquantitative and qualitative survey data collected from the evaluators. In the second phase, individualevaluators who struggled with the online grading platform, indicated either by low marking speed ordirect feedback, will be interviewed. These interviews will be analysed using a qualitative, thematicanalysis to determine the cause and severity of the issues.
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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 | 0.078 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".