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Record W568408786 · doi:10.1057/9780230621565_12

The Uses of Error: Toward a Realist Methodology of Student Evaluation

2009· book-chapter· en· W568408786 on OpenAlexaboutno aff
John J. Su

Bibliographic record

VenuePalgrave Macmillan US eBooks · 2009
Typebook-chapter
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsnot available
Fundersnot available
KeywordsGrading (engineering)WonderDocumentationMathematics educationClass (philosophy)PerceptionSet (abstract data type)Quarter (Canadian coin)PsychologyField (mathematics)EpistemologyPedagogyComputer scienceSocial psychologyMathematicsEngineeringPhilosophy

Abstract

fetched live from OpenAlex

What does realist theory have to say about the evaluation of students' performance in the humanities classroom? My question is motivated by the concerns students have expressed to me with the grading systems in their courses, and my own sense that realist theory might provide guidance in addressing these concerns. Particularly in qualitative fields such as English literature, the field in which I work, there is a fairly common perception among students that grade determinations are subjective, owing more to how well their ideas correspond with their instructor's than to their ability to produce work that meets a coherent set of course objectives. And little wonder. In many courses, little or no written documentation is ever provided describing the course objectives, the criteria by which individual assignments are assessed, or the relative weight of assignments in the determination of final grade. Factors such as "class participation" may constitute up to a quarter of a student's final grade without ever being explicitly defined, much less presented in a manner that explains why such factors should be relevant in the determination of grades.KeywordsRealist TheoryStudent EvaluationClassroom DiscussionFinal GradeTeacher ResponseThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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)0.004
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.967
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.211
GPT teacher head0.426
Teacher spread0.215 · 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 designTheoretical or conceptual
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

Citations0
Published2009
Admission routes1
Has abstractyes

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