MétaCan
Menu
Back to cohort
Record W4295993207 · doi:10.5430/ijhe.v11n5p145

The University of Central Florida Knights of Distinction Program: Supporting Undergraduate Student Academic and Career Success

2022· article· en· W4295993207 on OpenAlexvenueno aff
Rocio Tonos, Quynh Dang, Shelby Melfi

Bibliographic record

VenueInternational Journal of Higher Education · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicReflective Practices in Education
Canadian institutionsnot available
Fundersnot available
KeywordsTeamworkExperiential learningPortfolioPsychologyPlan (archaeology)Career portfolioMetacognitionAcademic advisingPedagogyReflection (computer programming)Medical educationMathematics educationHigher educationCareer developmentCognitionManagementPolitical scienceComputer scienceMedicine

Abstract

fetched live from OpenAlex

The purpose of this article is to present the Knights of Distinction, a co-curricular program that encourages undergraduate students to plan, connect, and reflect in the pursuit of their academic and professional goals. Knights of Distinction is a program within the Office of Experiential Learning at the University of Central Florida. Its purpose is to help students make connections between theory and practice and that they possess valuable skills such as problem-solving, teamwork, time-management, and communication that employers and graduate schools prize. This program is intended to help students connect with resources through high-impact practices that support student learning and academic success. The program encourages metacognition, reflection, and integrative learning through the creation of an e-Portfolio which showcases their skills and goals as well as their relevant academic and extracurricular experiences. Most current data show that students positively articulate the benefits of the program as they advance in their professional careers.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.002

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 head0.410
Teacher spread0.386 · 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 designQualitative
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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Higher EducationSame topicReflective Practices in EducationFrench-language works237,207