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Record W2608776259 · doi:10.5539/ies.v10n5p26

Do Prospective Teachers Care About PISA Studies? A Pilot Study among University Students in Germany

2017· article· en· W2608776259 on OpenAlexvenueno aff
Olga Ioannidou, Despoina Georgiou, Andreas Obersteiner, Nilüfer Deniz Bas, Christine Mieslinger

Bibliographic record

VenueInternational Education Studies · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicEducation Practices and Challenges
Canadian institutionsnot available
FundersTechnische Universität MünchenDeutsche Forschungsgemeinschaft
KeywordsPsychologySample (material)PerceptionMedical educationMathematics educationTeacher educationPedagogyMedicine

Abstract

fetched live from OpenAlex

The results of international comparison studies such as the Program for International Student Assessment (PISA) have initiated intense discussions about educational reforms in Germany. Although in-service and pre-service teachers are an essential part of such reforms, little is known about their attitudes towards PISA studies. The present study aims to fill this gap through the investigation of pre-service teachers’ awareness, interest, perception, and attitudes towards PISA. A questionnaire was used to survey a sample of 107 university students who were participating in a teacher education program. The results reveal that 100% of the participants are aware of PISA. Nearly 69% of the participants think that the impact of PISA is rather high or very high, while 41% of them believe that PISA results are reliable. Accordingly, half of the participants seem to be interested in PISA results for their country. The present study discusses these findings in the light of the expected outcomes as proposed in standards for teacher education.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.148
GPT teacher head0.433
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2017
Admission routes1
Has abstractyes

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