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Record W4386532239 · doi:10.22230/ijepl.2023v19n2a1291

Teachers' Perspectives on Teacher Self-Efficacy and Principal Leadership Characteristics

2023· article· en· W4386532239 on OpenAlexvenueno aff
Carolyn Hayward, Matthew Ohlson

Bibliographic record

VenueInternational Journal of Education Policy and Leadership · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicEducation Practices and Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologySelf-efficacyPrincipal (computer security)Scale (ratio)Situational ethicsRanking (information retrieval)Collective efficacyFlexibility (engineering)Medical educationMathematics educationPedagogySocial psychologyMedicineComputer science

Abstract

fetched live from OpenAlex

The purpose of this study was to investigate how elementary teachers rate their level of self-efficacy and to examine the characteristics of school leaders influencing teacher self-efficacy, including when teachers worked from home during the COVID-19 school shutdown. On the Teachers’ Sense of Efficacy Scale (TSES), all 287 participating teachers rated their self-efficacy in the high or moderate range. On the Principal Rating and Ranking Scale (PRRS), teachers reported that Communication, Inspiring Group Purpose, Consideration, and Empowering Staff were the most important characteristics of leaders related to teacher self-efficacy. The teachers interviewed reported that Communication and Flexibility were their principals’ most supportive leadership characteristics during the COVID-19 school shutdown, and that areas for improvement were more Communication, Situational Awareness, and Modelling Instructional Expectations. This work gives district leaders a clearer understanding of practices, strategies, and behaviours they can implement to improve teacher self-efficacy, teacher practice, and student achievement.

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)0.004
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.231
GPT teacher head0.374
Teacher spread0.143 · 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
Published2023
Admission routes1
Has abstractyes

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