MétaCan
Menu
Back to cohort
Record W2587209451

Fostering Student Partnership in Education – A New Model of Curricular Design

2016· article· en· W2587209451 on OpenAlexaff
Parvathy Krishna Krishna Pillai Sathidevi, Lori Goff

Bibliographic record

VenueGlobal Health: Annual Review · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsMcMaster University
Fundersnot available
KeywordsGeneral partnershipHappinessConstruct (python library)Process (computing)Computer scienceMathematics educationWork (physics)PedagogyPsychologyHigher educationActive learning (machine learning)Knowledge managementPolitical scienceEngineeringSocial psychologyArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

Abstract Current research in the field of pedagogy focuses a great deal on the significance of student participation in the learning process. Often, this can be accomplished by giving students the opportunity to propose their learning goals, facilitating their learning experience through instructor assistance and enabling them to reflectively assess their own performance while garnering a sense of accomplishment from their work. Here, a new model of student wellbeing in learning is proposed as a toolset for course design that ensures active student partnership. A psychological model is transposed to a post-secondary education setting and its efficacy is demonstrated. The proposed model envisions student wellbeing as a construct wherein attributes such as superior learning outcomes, higher degree of role satisfaction and happiness for all parties involved are conducive.

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)0.012
metaresearch head score (Gemma)0.011
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 score0.012
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.010
Scholarly communication0.0070.004
Open science0.0020.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.515
Teacher spread0.304 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueGlobal Health: Annual ReviewSame topicHigher Education Practises and EngagementFrench-language works237,207