FELEing It: Developing a Rubric Through an Interdisciplinary Partnership to Support Graduate Students’ Writing Skills
Bibliographic record
Abstract
Educational leadership demands that school leaders be able to communicate clearly and effectively in writing, both academically and professionally. Leaders of a Master’s in Educational Leadership (M.Ed.) program at a university in the southeast of the United States established an interdisciplinary collaboration with the university’s Faculty Writing Partners program to develop valid and reliable rubrics to assess graduate students’ writing abilities through critical task essays. The critical task essays connect required internship experience in each course with a targeted Florida Principal Leadership Standard (FPLS), coursework, and scholarly research. However, an objective performance measure for the critical task essays was lacking. Accordingly, a criterion-referenced rubric was developed to: 1) specify criteria for writing in relation to grammar, mechanics, and organization, 2) identify quality peer-reviewed research as evidence to support clinical experience, 3) effectively articulate in writing personal strengths, weaknesses, and plans for improvement towards mastery of the FPLS, and 4) provide consistency in expectations and feedback among professors and adjunct instructors. The rubric demonstrated validity and reliability, and likely led to consistently higher scores on both the critical task essays and, most importantly, the Florida Educational Leadership Examination (FELE). Résumé: Le leadership en éducation exige que les cadres scolaires sachent communiquer clairement et efficacement sous forme écrite, tant au niveau pédagogique que professionnel. Les responsables de la maîtrise en leadership éducationnel dans une université du sud-est des États-Unis ont établi une collaboration interdisciplinaire avec les responsables du programme « Faculty Writing Partners » à la même université afin de développer des critères valables et fiables pour évaluer, au moyen de textes d’analyse critique, les aptitudes en rédaction de leurs étudiants diplômés. Ces textes ont comme objectif de relier les expériences acquises par ces étudiants lors d’un stage obligatoire dans chaque cours au Florida Principal Leadership Standard (FPLS), au travail qu’ils ont effectué dans le cours, et à leurs recherches universitaires en général. Il manquait cependant une mesure objective pour évaluer les textes d’analyse critique. On a donc conçu un tableau fondée sur des critères précis pour : 1) spécifier nos attentes en ce qui a trait à la grammaire, la syntaxe et l’organisation du texte; 2) reconnaître l’article de recherche de bonne qualité évalué par les pairs comme complément de l’expérience clinique; 3) articuler efficacement sous forme écrite les qualités, faiblesses et objectifs d’amélioration personnels relatifs à la maîtrise du FPLS; et 4) assurer une uniformité parmi les professeurs et les instructeurs associés dans leurs attentes et leurs conseils. Le tableau s’est avéré valable et fiable. D’ailleurs, en toute probabilité, il a mené à de meilleurs résultats pour les textes d’analyse critique et surtout pour le Florida Educational Leadership Examination (FELE). Keywords / Mots clés : educational leadership, professional writing for school leaders, academic writing for school leaders, improving FELE writing scores / leadership en éducation, rédaction professionnelle pour cadres scolaires, rédaction universitaire pour cadres scolaires, amélioration des résultats en rédaction pour le FELE
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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 | 0.012 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".