Examining the Dependability and Practicality of Analytic Rubric of Summary Writing Using Multivariate Generalizability Theory: Focusing on Japanese University Students with Lower-Intermediate Proficiency in English
Bibliographic record
Abstract
English teachers, especially those who teach summary writing to students with relatively lower proficiency in English face difficulty in teaching summary writing and while assessing their students’ performances. In the classroom context, an analytic rubric is pedagogically more helpful than a holistic rubric because the teacher can confirm the strengths and weaknesses of their students’ summary performance and the students can receive constructive feedback (Yamanishi et al., 2019). This study examined the practicality of the analytic rubric which consisted of four rating scales, including language use, by investigating seven in-service English teachers’ honest assessment of 160 summaries of Japanese private university students who are inexperienced in writing English summaries and have lower-intermediate proficiency of English. Furthermore, this study examined the dependability of the analytic rubric using multivariate generalizability theory (Brennan, 2001). The results showed that assessing language use and judging summaries, copied, to a lesser or greater extent, from the source text was difficult because of diverse linguistic errors and the use of paraphrasing was lacking. Therefore, it is necessary to define the gravity of language errors and that of copying in more detail to develop a rubric that suits to assess the summaries written by English learners with lower-intermediate level of English.
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 | 0.046 | 0.243 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".