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Record W7029396223

An Investigation of Educatorsâ Data Habit of Mind

2011· dissertation· en· W7029396223 on OpenAlexvenueno aff

Bibliographic record

VenueLibrary and Archives Canada (Government of Canada) · 2011
Typedissertation
Languageen
FieldMathematics
TopicStatistics Education and Methodologies
Canadian institutionsnot available
Fundersnot available
KeywordsHabitInterpretation (philosophy)Set (abstract data type)CognitionLiteracyData collection
DOInot available

Abstract

fetched live from OpenAlex

Educators are increasingly being asked to interact with data to facilitate students’ learning in the classroom. However, as an educational measurement community, we have little understanding of the factors and/or contexts that facilitate educators’ successful use of data. Educators’ use of score reports and the relationship to the intended use is integral to the concept of validity. A conceptual model, “Data Habit of Mind,” is proposed to study educators’ understanding, interpretation and potential applications of results from large-scale assessments. The metaphor, “Habit of Mind,” was originally coined by Robert Sternberg and Dan Keating, and has been applied in the education sector to describe educators’ habits of inquiry when interacting with assessments. Based on an extensive review of the literature, Data Habit of Mind is defined as a combination of statistical literacy and score report interpretation. Statistical literacy is the extent to which an individual is able to describe, organize and reduce, represent, and analyze and interpret data. Score report interpretation is the extent to which an individual is able to describe, summarize, question, and propose an application for a given set of elements on a score report. The combination of these two makes up an individual’s Data Habit of Mind. \nTwenty educators were interviewed to assess their level of statistical literacy and their score report interpretation skills. A cognitive interview approach was used to capture the educators’ cognitive processes as they solved performance-based tasks, and protocol analysis procedures were used to encode the responses into the conceptual model. Descriptions of educators’ Data Habit of Mind were then generated through qualitative matrix analysis. Four groups of educators were identified based on the patterns of relationship between their statistical literacy and score report interpretation scores. Demographic factors, including teaching experience, gender and educational background were not meaningful predictors of educators’ Data Habit of Mind. These results add to our understanding of how educators interpret and use test results and have implications for test validation processes.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.685
Threshold uncertainty score0.975

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.060
GPT teacher head0.277
Teacher spread0.216 · 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 designTheoretical or conceptual
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

Citations0
Published2011
Admission routes1
Has abstractyes

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