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Record W2889618147 · doi:10.5539/hes.v8n4p46

Intrinsic Motivation and 21st-Century Skills in an Undergraduate Engineering Project: The Formula Student Project

2018· article· en· W2889618147 on OpenAlexvenueno aff
Iris Talmi, Orit Hazzan, Reuven Katz

Bibliographic record

VenueHigher Education Studies · 2018
Typearticle
Languageen
FieldPsychology
TopicCreativity in Education and Neuroscience
Canadian institutionsnot available
Fundersnot available
KeywordsPaceAutonomyCompetence (human resources)Self-determination theoryIntrinsic motivationCompetition (biology)Project-based learning21st century skillsSet (abstract data type)PsychologyPedagogyMathematics educationPolitical scienceComputer scienceSocial psychologyEcology

Abstract

fetched live from OpenAlex

The 21st century is characterized by new technological developments and a rapid pace of change, challenging the academy to educate students for a future employment market characterized by change and uncertainty. This market requires practitioners to develop a broad set of skills, so-called "21st-century skills," along with more focused practices within traditional disciplines. The present study explores the mutual relationship between intrinsic motivation and the expression of 21st-century skills among students participating in the Formula Student project – an international competition in which participants design and build a racecar while facing challenges such as independent learning, planning, and execution. We found that students’ participation in the Formula Student project enables them to practice 21st-century skills that they will need in their future workplace; this experience, in turn, helps them meet the psychological need for autonomy, competence, and relatedness which are the basis for intrinsic motivation.

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)0.005
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.002
Scholarly communication0.0050.001
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.064
GPT teacher head0.422
Teacher spread0.358 · 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 designObservational
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

Citations48
Published2018
Admission routes1
Has abstractyes

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