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Record W3004397794 · doi:10.5539/ies.v13n2p115

Experience Changes Perceptions: Arabic-Speaking Students’ Perceptions Regarding the PDS Model and Teacher Training

2020· article· en· W3004397794 on OpenAlexvenueno aff
Orr Levental, Edni Naifeld, Saleh Kharanbe, Marcel Amasha

Bibliographic record

VenueInternational Education Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Professional Development and Motivation
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyPerceptionMulticulturalismProfessional developmentPedagogyArabicFaculty developmentTeaching methodProcess (computing)Identity (music)Mathematics educationTeacher educationMedical educationMedicine

Abstract

fetched live from OpenAlex

During their studies, education students are required to engage practice-based experience in a collaborative model: Professional Development School (PDS), where there are many options for building professional and personal development processes. Through this experience, students formulate professional identity and perceptions about teaching. This study sought to examine the impact of this experience model on Arabic-speaking education students attending a Hebrew speaking college. The effect of the practice-based experience was examined on both the concept of teaching as a profession, the process of teaching instruction and social and cultural aspects. The findings of the study showed that PDS practice-based experience directly and indirectly contributes to the way students perceive teaching, the role of the teacher, the education system, as well as the importance of the practical experience in the teaching training process. However, there was no significant contribution of PDS practice-based experience to students’ perceptions of multicultural aspects of campus life.

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.002
Scholarly communication0.0040.001
Open science0.0000.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.262
GPT teacher head0.477
Teacher spread0.214 · 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 designQualitative
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

Citations2
Published2020
Admission routes1
Has abstractyes

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