2012-2018 Direct Writing Assessment Scores for an international school (K-12) in Bangkok, Thailand
Notice bibliographique
Résumé
The participants for the Direct Writing Assessment Scores were students in grades three to twelve who attended a private international (K-12) English immersion school in Bangkok, Thailand between the years 2012-2018. The variables gathered each year include direct writing assessment scores based on the Oregon 6 Traits Rubric scored by two raters from National Scoring Services as well as new student status, sex, nationality based on passport, and ESL pullout program status. This data was used with a binary logistic regression model to develop three predictive models (Year 1, Year 2, Year 3) to show how likely it would be for a participant to reach writing proficiency, and how long it may take to meet that expectation. The research question was, “To what extent can the Annual Writing Assessment scored with the six-traits writing rubric identify at-risk writers from Grades 3-12 at the International Community School BangNa, Thailand?” The independent variables of participant bio data coded and tested for significance in the binary logistic regression model include the following: sex (female 0; male 1) new student status (no 0; yes 1) Thai (no 0; yes 1) USA (no 0; yes 1) Indian (no 0; yes 1) Korean (no 0; yes 1) other nationality (no 0; yes 1) English as L1 (0 no, 1 yes) enrolled in the ESL pull-out program any time during their testing (no 0; yes 1). Nationality was coded based on the passport country the participants used during the admissions process when enrolling at the school. In addition, English as L1 status was based on the participants’ passport country. English as L1 coded (yes = 1) countries included USA, Canada, England, Australia, and Kenya. The consequence of coding with such generalities means that some L2 writers may have been miscoded as L1 writers based on their passports. Other bio data included: participant ID# initial year of Test 1 grade level (3-12; *LS) [*Life Skill students (coded as “LS”) are secondary students who attend the school, but are not on the academic track to complete an accredited high school diploma.] The independent variables of test scores coded and tested for significance in the binary logistic regression model include the following: ideas (scale 0-6) organization (scale 0-6) ideas (scale 0-6) voice (scale 0-6) word choice (scale 0-6) sentence fluency (scale 0-6) conventions (scale 0-6) The binary dependent variable was coded as “0” for students who never achieved a score average higher than 3.9 and “1” for students who scored 4.0 on at least one test. Binary Dependent Variable Passed 4.0 at least once (0 = no; 1 = yes)
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».