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Record W3106744737 · doi:10.19173/irrodl.v21i4.4663

Teachers’ Use of Education Dashboards and Professional Growth

2020· article· en· W3106744737 on OpenAlexvenueno aff
Shiran Michaeli, Dror Kroparo, Arnon Hershkovitz

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2020
Typearticle
Languageen
FieldComputer Science
TopicOnline Learning and Analytics
Canadian institutionsnot available
Fundersnot available
KeywordsFacilitatorProfessional developmentPsychologyQualitative propertyPedagogyFaculty developmentMedical educationMathematics educationKnowledge managementComputer scienceMedicine

Abstract

fetched live from OpenAlex

Education dashboards are a means to present various stakeholders with information about learners, most commonly regarding the learners’ activity in online learning environments. Typically, an education dashboard for teachers will include some type of visual aids that encourage teachers to reflect upon learner behavior patterns and to act in accordance to it. In practice, this tool can assist teachers to make data-driven decisions, thus supporting their professional growth, however, so far, the use of education dashboards by teachers has been greatly understudied. In this research we report on two studies related to the associations between the use of education dashboards by elementary school teachers and the teachers’ professional growth. We used the framework defined by the International Society for Technology in Education’s (ISTE) Standards for Educators. In the first study, we took a quantitative approach (N=52 teachers), using an online self-report questionnaire, and found that the use of dashboards is positively associated with professional growth in the dimensions of facilitator, analyst, designer, and citizen. In the second study, we took a qualitative approach (N=9 teachers), using semi-structured interviews, to shed light on the mechanisms through which teachers benefit from the use of education dashboards.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation 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 score0.589
Threshold uncertainty score0.420

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
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 science0.0010.001
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 head0.123
GPT teacher head0.457
Teacher spread0.334 · 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

Citations26
Published2020
Admission routes1
Has abstractyes

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