A cognitive diagnostic assessment of PISA math items: What skills have Canadian student mastered?
Notice bibliographique
Résumé
The Organization for Economic Cooperation and Development (OECD)-sponsored Program for International Student Assessment (PISA) plays an essential role in encouraging educational reform and strengthening educational policy. According to recent research issued by the C.D. "Howe Institute," a Canadian think tank, Canadian students’ mathematics results have steadily dropped in foreign tests since 2003 (Richards, 2017). To investigate the declining trend of Canadian students’ mathematical literacy, this study proposes the use of cognitive diagnostic models (CDMs), which provide profiles of mastery and non-mastery of mathematical skills or attributes (e.g., quantity, change and relationship, space and figure, uncertainty and data, etc.) that are critical for students’ success in mathematics achievement. Compared to standard testing and evaluation methods such as item response theory (IRT) models, CDM is regarded as a person-centered statistical model, which provides detailed mastery of each individual’s cognitive attributes or knowledge and skills and offers important guiding information to teachers’ teaching and test writing (Leighton & Gierl, 2007). Given the fact that the Deterministic Inputs, Noisy “and” Gate (DINA) model is a widely employed CDM, DINA model is applied in this study to demonstrate specific learning outcomes of 15-year-old students across Canada, and identify differences within-Canada in PISA test. The aims of this study are to (1) examine whether the PISA data fit to the constructs or skills as stated in their manual with the DINA model, (2) explore the essential reasons causing declining Canadian scores by identifying the mathematics skills that Canadian students have or have not mastered in PISA testing, (3) examine differences in mastering mathematics skills in PISA testing across ten Canadian provinces. The results showed that (1) the DINA model is applicable to analyze the Canadian students mastering of mathematical literacy as evidenced by reasonable parameter estimates including slipping, guessing, Item Discrimination Index (IDI) and Root Mean Square Error of Approximation (RMSEA); (2) the mathematics skill, geometry (space and shape skill), has the lowest mastery rate for 15-year-old Canadian students. On the contrary, the mathematics skill, application of mathematics thinking in daily life (employing skill), has the highest mastery rate for Canadian students; (3) students in Quebec not only performed better in test but also acquire the highest mastery rate in all 11 mathematical skills defined by PISA. The t test was utilized to analysis the differences in mastery rate within-country, and the p value showed that Quebec and British Columbia had comparative better personal and occupational skills compared to the students in other provinces. The findings illuminate the mathematical abilities that Canadian students have acquired or have not entirely grasped, thereby assisting educators and policymakers in establishing and enhancing the educational curriculum for mathematics instruction in Canada.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,005 | 0,004 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».