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Record W4313552311 · doi:10.1145/3579632.3558395

Closing the Assessment Excellence Gap: Why digital assessments should go beyond recall and be more inclusive

2023· article· en· W4313552311 on OpenAlexaff
Gavin Cooney, J.D. Kleeman

Bibliographic record

VenueeLearn · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Learning Practices
Canadian institutionsQuest University Canada
Fundersnot available
KeywordsClosing (real estate)ExcellenceRecallImplementationComputer scienceAction (physics)Public relationsPsychologyPolitical scienceCognitive psychologySoftware engineering

Abstract

fetched live from OpenAlex

Stakeholders in digital assessments have requirements and expectations that technology implementations do not always meet. This is the "Assessment Excellence Gap." This article explains why digital assessment is important and focuses on two particular aspects with a call to action to make assessments more inclusive and to write questions that go beyond recall when creating assessments.

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)0.119
metaresearch head score (Gemma)0.207
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.119
Threshold uncertainty score0.628

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1190.207
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0090.032
Scholarly communication0.0230.042
Open science0.0030.023
Research integrity0.0110.013
Insufficient payload (model declined to judge)0.0040.002

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 head0.082
GPT teacher head0.465
Teacher spread0.383 · 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 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

Citations1
Published2023
Admission routes1
Has abstractyes

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