Scoring Difficulty in Summary Writing Assessment: Toward the Reconstruction of Analytic Rubric
Bibliographic record
Abstract
This study aims to examine whether differences exist in the factors influencing the difficulty of scoring English summaries and determining scores based on the raters’ attributes, and to collect candid opinions, considerations, and tentative suggestions for future improvements to the analytic rubric of summary writing for English learners. In this study, seven trained raters with diverse attribute backgrounds evaluated two kinds of English summaries written by Japanese university students using the analytic rubric with three evaluation items. A questionnaire was used to determine which of the three items were difficult to assess and why the raters perceived such difficulty, as well as what backgrounds and factors influenced their scoring decision-making. Moreover, through the raters’ most recent experience, candid comments were collected for developing future rubrics. The results showed that whether the evaluators’ attributes affected the difficulty of the evaluation was not clear. However, depending on the raters’ experience in teaching English/assessing summary writing, requests for improvements in the descriptors of the evaluation items and in the rubric emerged. This study proposes a tentative analytic rubric for summary writing, providing a foundation for constructing a rubric that can be used more easily by future raters. It also highlights the opinions of expert and novice teachers conducting summary evaluations in education.
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 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 | 0.002 | 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) | 0.000 | 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 teacher head, 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".