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Record W2239829106 · doi:10.3928/01484834-20041001-01

Development of an Instrument to Assess Individual Student Performance in Small Group Tutorials

2004· article· en· W2239829106 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Nursing Education · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicProblem and Project Based Learning
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSummative assessmentTUTORScale (ratio)Computer scienceInter-rater reliabilityReliability (semiconductor)Consistency (knowledge bases)Variance (accounting)CurriculumMathematics educationFormative assessmentPsychologyPedagogyArtificial intelligenceRating scale

Abstract

fetched live from OpenAlex

Recognizing the need for a valid and reliable method to assess individual tutorial performance in a problem-based learning curriculum, we developed a 31-item instrument from theoretical frameworks and items used elsewhere. A scale was developed for each of three broad learning domains: self-directed learning (SDL), critical thinking (CT), and group process (GP). The instrument demonstrated high internal consistency (SDL = .88, CT = .90, GP = .83) on a sample of 18 tutors and 167 students. Tutor-student interrater reliability coefficients were estimated to be low (SDL = .16, CT = .18, GP = .14) due to lack of variance on the response scale. The instrument showed high correlation (r = .82) with other forms of summative evaluation. In its current form, this standardized and validated instrument is unreliable in differentiating strong from weak tutorial performance but can have a steering effect on student tutorial behaviors. The process of instrument development has general application to other educational programs.

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.

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)0.000
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: Empirical
Teacher disagreement score0.836
Threshold uncertainty score0.433

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.127
GPT teacher head0.423
Teacher spread0.296 · 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