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Record W2618111379 · doi:10.3389/fpsyg.2017.00906

Combinations of Personal Responsibility: Differences on Pre-service and Practicing Teachers’ Efficacy, Engagement, Classroom Goal Structures and Wellbeing

2017· article· en· W2618111379 on OpenAlexafffund
Lia M. Daniels, Amanda I. Radil, Lauren D. Goegan

Bibliographic record

VenueFrontiers in Psychology · 2017
Typearticle
Languageen
FieldPsychology
TopicMotivation and Self-Concept in Sports
Canadian institutionsUniversity of Alberta
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyService (business)Moral responsibilityCluster (spacecraft)Student engagementSocial responsibilityMedical educationPedagogyPublic relations

Abstract

fetched live from OpenAlex

Pre-service and practicing teachers feel responsible for a range of educational activities. Four domains of personal responsibility emerging in the literature are: student achievement, student motivation, relationships with students, and responsibility for ones own teaching. To date, most research has used variable-centered approaches to examining responsibilities even though the domains appear related. In two separate samples we used cluster analysis to explore how pre-service (n = 130) and practicing (n = 105) teachers combined personal responsibilities and their impact on three professional cognitions and their wellbeing. Both groups had low and high responsibility clusters but the third cluster differed: Pre-service teachers combined responsibilities for relationships and their own teaching in a cluster we refer to as teacher-focused responsibility; whereas, practicing teachers combined achievement and motivation in a cluster we refer to as student-outcome focused responsibility. These combinations affected outcomes for pre-service but not practicing teachers. Pre-service teachers in the low responsibility cluster reported less engagement, less mastery approaches to instruction, and more performance goal structures than the other two clusters.

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)0.000
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 score0.072
Threshold uncertainty score0.699

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.043
GPT teacher head0.362
Teacher spread0.319 · 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

Citations41
Published2017
Admission routes2
Has abstractyes

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