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Record W2592619457 · doi:10.5430/wje.v7n1p93

Does Higher Education Curriculum Contribute to Prospective Teachers' Attitudes, Self-Efficacy and Motivation?

2017· article· en· W2592619457 on OpenAlexvenueno aff
Morana Koludrović, Ina Reić Ercegovac

Bibliographic record

VenueWorld Journal of Education · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Professional Development and Motivation
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumPsychologyMathematics educationAtmosphere (unit)Self-efficacyDemocracyQuality (philosophy)PedagogyMedical educationSocial psychologyPolitical sciencePoliticsMedicine

Abstract

fetched live from OpenAlex

In Croatia, another comprehensive reform of the education system is being implemented. Although it proposes anumber of reforms to the school system, we think that providing better training to future teachers during their studieswould further contribute to the quality of education. The initial education of elementary and high school teachers isconsiderably different in terms of the number of courses where teaching competences are acquired, i.e. teachingcourses, and the number of classes of these courses students take every week. Therefore, the initial hypothesis of thestudy was that students who take more teaching courses will prefer a democratic atmosphere, and will be moreintrinsically motivated, more self-efficient and more satisfied with their course of study as compared to students whotake fewer of these courses. A series of questionnaires was given to 383 students at teacher education institutions atthe University of Split to examine their academic self-efficacy, motivation, satisfaction with the studies and to assessthe quality of teaching atmosphere. The obtained results confirm the hypothesis that future teachers who take moreteaching courses prefer a democratic atmosphere and are intrinsically more motivated. When explaining futureteachers’ attitudes towards a democratic teaching process, the predictive role of self-efficacy and satisfaction withthe course of study was not determined.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.167
Threshold uncertainty score0.755

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.020
GPT teacher head0.359
Teacher spread0.339 · 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 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

Citations3
Published2017
Admission routes1
Has abstractyes

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