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Record W2484181297 · doi:10.5539/hes.v6n3p138

Primary School Teachers’ Views on the Preparation and Usage of Authentic Material

2016· article· en· W2484181297 on OpenAlexvenueno aff
Gülay Bedir, Özlem Yeşim Özbek

Bibliographic record

VenueHigher Education Studies · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Technology Integration
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationPsychologyQualitative researchSchool teachersContent analysisTeaching methodPedagogyMedical educationMedicineSociology

Abstract

fetched live from OpenAlex

The students of primary school, secondary school, high school and university confront a vast array of stimulants along with the developing technology in their daily lives. With the classroom environment’s lack of rich stimulus, it is difficult to get the students’ attention using traditional teaching methods. If teachers choose both technological materials and two-three dimensional materials and use them effectively, lessons will be more understandable to the students. The objective of this research is to record the opinion of primary school teachers about the preparation and usage of educational materials. The research was methodized by employing a qualitative pattern. The working group consists of 106 teachers who attended the Instructional Materials Seminar in Aksaray, Turkey. A semi-structured interview form was used to collect the data of this research. The research data was analyzed by using a content analysis method (specifically, the Phenomenological pattern). Teachers stated that it is of primary importance to use materials for concretizing topics and easier and permanent learning. The most important problems for teachers during preparation of materials are listed as a lack of time, money, equipment and knowledge. All the teachers who attended the research stated that it is necessary to prepare materials in all professions but it is especially important for Mathematics. Teachers also stated that materials that students can touch and see help most while teaching abstract topics.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.722
Threshold uncertainty score0.638

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.070
GPT teacher head0.399
Teacher spread0.329 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations6
Published2016
Admission routes1
Has abstractyes

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