A Comparative Evaluation of Pisa 2003-2006 Results in Reading Literacy Skills: An Example of Top-Five OECD Countries and Turkey.
Notice bibliographique
Résumé
Abstract In this study it is aimed to describe and evaluate comparatively the reading literacy exam results, the finance of education and schools, and socio-cultural status of parents inTurkey and the top-five OECD countries, Finland, Korea, Canada, Australia, New Zealand respectively, in the light reports and publications by OECD regarding PISA 2003 and 2006 evaluations. Pisa evaluation studies are helpful to understand the current outcomes of the educational systems and will definetly enhance the quality of future educatioanl policies with the help of comparison between the accomplishments of the rival countries. In this descriptive study, the data regarding Pisa results and country statistics have been obtained from the online OECD publications. It is observed that higher rate of sudents in Turkey has lower level reading skills, and a small rate of its students can accomplish high level reading skills contrary to the situation in the top-five OECD countries. Great majority of Turkish students lacks of advance skills such as working with abstract ideas, critical thinking, making links with the inferred knowledge with daily experiences. On the other hand, Turkish educational system is below the standarts of OECD countries in terms of educational sources because of lower level finance in education and schools, higher number of students per class and teacher, less amount of teacher salaries, which all paralel to its low level economic wealth. Moreover, Turkish citizens have low level of socio-cultural status with respect to other OECD countries in that most of Turkish students do not attend high school level education, and a great majority of parents, both mothers and fathers, have lower level of education. Key Words PISA, Turkish Education System, Reading Literacy, Primary School Students, Achievement. After 90's, there is a convergence of thougths regarding the future life style of 21st century foreseeing a transition from the industrial society to a new knowledge society (Bengshir, 1996; Cerit, 2001; Drucker, 1995; Ozdas, 1999). In this future context, individuals will run after information, and will need the ability to get it and use it effectively, thus they need to learn to learn in a constant and life-long process (Cerit, 2001; Findikci, 1996; Organisation for Economic Co-operation and Development [OECD], 2003b). An educational system then should be able to create a new generation of information-processors, of innovation and creativity, and problem solvers. Thus, investment in education and in human capital is a must for the growth of both high-skilled individuals and nations in a competetive globalized world (Johansson, Karlsson, & Stough, 2001, cited in Cheung & Chan, 2008). OECD, like other international institutions that produce knowledge and policies for the economic growth and welfare of nations, evaluates educational systems and their educational outcomes in a comparative way, and determines each countries' performance and highlights the good examples for the production of education policies to be put into practice for better education outcomes (Grek, 2009; Rizvi & Lingard, 2006; Rochex, 2006). For this reason, OECD created PISA evaluation program and started to get use of it as a mean to investigate national education systems and to gauge the students' skills in reading, math, science and problem solving, most preferable qualities one should have in a new information society. These evaluations determine the level that students meet the requried skills in the information society, exploit their knowledge in their daily life situation, and measure the level of their working with concepts, and their aplication to reality (T.C. Milli Egitim Bakanligi Egitim Arastirma ve Gelistirme Dairesi Baskanligi [EARGED], 2007; OECD, 2001, 2003). In this sense, it does not measure what they will do with their acquired knowledge, but what they can do in real life with the knowledge acqired in school (Brozo, Shiel, & Topping, 2007, cited in Greg, 2009). …
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 | 0,005 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,006 | 0,005 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 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 ».