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Classroom Assessment as a Source of Motivational Messages

2021· book-chapter· en· W4207047314 on OpenAlexaff
Nicole Barnes, Helenrose Fives, Andrew Coombs

Bibliographic record

VenueOxford University Press eBooks · 2021
Typebook-chapter
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsQueen's University
Fundersnot available
KeywordsSummative assessmentExtant taxonPsychologyTask (project management)Theme (computing)Field (mathematics)Event (particle physics)Formative assessmentMathematics educationComputer scienceEngineering

Abstract

fetched live from OpenAlex

Abstract In this scoping article, the authors summarize the extant research on students’ achievement motivation and classroom summative assessment events. The purpose is to understand the ways that different assessment tasks and assessment-related conditions can send motivational messages to students. The authors begin by summarizing two frameworks to illustrate how motivation operates in classroom assessment events. Then the authors present their findings organized into three major categories: overview of articles reviewed, task-focused investigations, and conditions-focused investigations. The goal is to provide a rich description of what is known in the field about relations among assessment tasks, assessment condition, and students’ motivation. A recurring theme in the article is the significance of the entire assessment event, from design to implementation to understanding how classroom assessments send motivational messages to students. In response, the authors call for greater collaboration across fields of educational research so that the integration of assessment and motivation theory can be intentional and lead to the development of sound recommendations for 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 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.993
Threshold uncertainty score1.000

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.282
Teacher spread0.251 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2021
Admission routes1
Has abstractyes

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