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Record W2604235551 · doi:10.1108/jpcc-10-2016-0027

Collective teacher efficacy research: implications for professional learning

2017· article· en· W2604235551 on OpenAlexaff
Jenni Donohoo

Bibliographic record

VenueJournal of Professional Capital and Community · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsGovernment of Ontario
Fundersnot available
KeywordsProfessional learning communityProfessional developmentPsychologyCollaborative learningPedagogy

Abstract

fetched live from OpenAlex

Purpose Researchers have provided critiques of teacher efficacy research along with suggestions for future research, but no recent reviews have examined the state of collective teacher efficacy (CTE) research as it relates specifically to professional development. This review addressed the following questions: How much research attention has been paid to professional learning and CTE? What does the research tell us about professional learning and CTE? What do we know about influencing CTE through professional learning? The paper aims to discuss these issues. Design/methodology/approach Educational Resources Information Center and EBSCO databases were searched for peer-reviewed articles written in English and published over the last ten years (between 2007 and 2017). The search terms included “collective efficacy” and “teacher or teachers” and “professional development and professional learning” and were extended beyond titles to include keywords contained within the articles. This would help to broaden the search and increase the number of hits. Findings There is little that can be gleaned from the research related to professional learning and the contextual factors that influence collective efficacy beliefs. Only one study (Paxon et al. , 2014) in this review considered the formation of CTE in relation to both remote and proximate sources. Although some of the studies explained Bandura’s (1993) sources of CTE, reference to the sources were notably absent in the reported findings, implications, and conclusions of many of the studies. Contextual variables examined in the component studies included either implementation patterns, trust, sense of belonging, teacher uncertainty, opportunities for teacher leadership, social relationships, and/or labels assigned to low performing districts and/or high performing districts. Research limitations/implications A limitation that influences the findings of this review is that the review was not exhaustive, and articles written in English with the search terms outlined did not capture the population of possible articles. Future reviewers may uncover new patterns in CTE research by searching non-English journals and by examining the range of work completed in graduate theses and dissertations. Practical implications In regard to promising professional learning designs, inquiry based approaches, including collaborative action research, problem-solving groups, and teams’ monitoring and tracking individual student progress seemed to hold promise. In each of these designs, educators collaboratively analyze student evidence for the purpose of evaluating their impact, reflecting on their collective work, and determining optimal next steps. Interpreting results by examining student learning data might help to strengthen connections between perceived levels of difficulty related to teaching tasks and perceptions of group competence. When conversations shift from generalized talk about student’s progress and polite sharing of teaching strategies to more in-depth conversations about the connections between the two, professional learning becomes more impactful. The interpretation of results, leads to shifts in causal attributions – from assumptions which included “I planned and taught the lesson, but they didn’t get it” to “you haven’t taught it until they’ve learned” as a result of engaging in these types of professional learning designs. Originality/value Hattie’s (2015) research, which synthesized major findings from over 1,200 meta-analyses relating to influences on student achievement, demonstrated the magnitude and overall distribution of more than 150,000 effect sizes. In a recent update, Hattie (2016) ranked CTE as the number one influence of all the factors related to student achievement, reporting an effect size of 1.57.This update was based on Eells’ (2011) meta-analysis that synthesized correlational evidence for CTE and student achievement. Eells (2011) found that CTE was strongly and positively associated with student achievement “across subject areas, when using varied instruments, and in multiple locations” (p. 110). Eells (2011) finding is becoming more widely disseminated through the promotion of Hattie’s (2016) Visible Learning Research due to its prominent position within that body of evidence. Thus, the interest of practitioners in the field, including administrators, teachers, and professional learning facilitators has been piqued. Gaining a better understanding of CTE, sources that shape it, and its antecedents and consequences are likely to surface as a major upcoming focus for designers and facilitators of professional learning. There is a small amount of extant research that examined professional development effects on teacher efficacy (Tschannen-Moran and McMaster, 2009; Ross and Bruce, 2007). However, there are many voids in the collective efficacy research. Given this void and the increased interest to gain a better understanding of CTE on part of practitioners, not only is additional research needed, it is imperative to find ways to address the ongoing dilemma of making research and theory relevant to educators’ practice.

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)0.120
metaresearch head score (Gemma)0.174
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.120
Threshold uncertainty score0.636

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1200.174
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0120.012
Science and technology studies0.0050.030
Scholarly communication0.0160.020
Open science0.0040.011
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0080.001

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 head0.501
GPT teacher head0.556
Teacher spread0.056 · 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 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".

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Citations70
Published2017
Admission routes1
Has abstractyes

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