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Record W3036591762 · doi:10.24908/pceea.vi0.14151

EXPERIENTIAL LEARNING FOR COMPLEMENTARY CREDIT: A COURSE TO EARN CREDIT FOR EXTRACURRICULAR INVOLVEMENT

2020· article· en· W3036591762 on OpenAlexafffundvenue
Stephen Mattucci, Kate Whalen, Daniel Picone, Joshua Yachouh, Ahmed Ali

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicReflective Practices in Education
Canadian institutionsMcMaster University
FundersMcMaster University
KeywordsExperiential learningTeamworkPsychologyActive learning (machine learning)Variety (cybernetics)Cooperative learningMathematics educationPedagogyExperiential educationMedical educationTeaching methodManagementComputer scienceMedicine

Abstract

fetched live from OpenAlex

Students often have significant learning experiences outside of the classroom, and in particular through their involvement in extracurricular activities. McMaster University has a strong student culture rooted in this type of involvement, and wanted to recognize this experiential learning. These students are often learning a variety of durable skills such as leadership, teamwork, conflict management, and communication. This paper describes the development of a course for students to earn complementary credit for a variety of diverse roles in extracurricular settings across campus. The development approach was informed by principles of student ownership and self-directed learning, and implemented by a diverse team including the instructor, staff, and students. Several focus groups conducted with actively involved students provided insight on both the structure and content of the course: workshop-style classes, with active learning modules, and opportunities for students to learn from each other. Critical reflection was the primary assessment to encourage students to derive learning from their extracurricular experiences. Preliminary observations from the first offering of the course are promising, in that students are deriving significant value from the course, and their related extracurricular experiential learning.

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)0.001
metaresearch head score (Gemma)0.006
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.412
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.024
GPT teacher head0.324
Teacher spread0.300 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2020
Admission routes3
Has abstractyes

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