MétaCan
Menu
Back to cohort
Record W3038133191 · doi:10.28945/4591

Effective Use of Case Teaching in Large Undergraduate Classes

2020· article· en· W3038133191 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

VenueInforming Science and IT Education Conference · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Marketing Education
Canadian institutionsWestern UniversityToronto Metropolitan University
Fundersnot available
KeywordsExperiential learningComputer scienceField (mathematics)Teaching methodGraduate studentsFocus (optics)Mathematics educationMedical educationPsychologyPedagogyMedicine

Abstract

fetched live from OpenAlex

Aim/Purpose: To guide faculty who wish to use the case method in large undergraduate classes Background: The paper reviews a range of case teaching methods and provides specific guidance on how to use them in various classroom situations. Methodology: Literature review, reflective experience, interviews, and surveys Contribution: This paper addresses a gap in case teaching research which tends to focus on its use in graduate classes Findings: Case teaching can be used effectively in large undergraduate classes, but needs to be used in different ways and with different techniques from those commonly recommended for graduate classes. Recommendations for Practitioners: Be creative and go beyond the Harvard: case method and draw on the broader range of techniques used in active and experiential learning Impact on Society: Better and more relevant classroom experiences Future Research: Examine and evaluate field examples of innovative case teaching, especially in hybrid and online environments.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.583
Threshold uncertainty score0.422

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.004
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 head0.034
GPT teacher head0.289
Teacher spread0.255 · 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