MétaCan
Menu
Back to cohort
Record W2732905644 · doi:10.5430/wje.v7n3p103

Impact of Different Levels of Epistemic Beliefs on Learning Processes and Outcomes in Vocational Education and Training

2017· article· en· W2732905644 on OpenAlexvenueno aff
Florian Berding, Katharina Rolf-Wittlake, Janes Buschenlange

Bibliographic record

VenueWorld Journal of Education · 2017
Typearticle
Languageen
FieldPsychology
TopicEducational Strategies and Epistemologies
Canadian institutionsnot available
Fundersnot available
KeywordsConceptualizationPsychologyVocational educationContext (archaeology)Construct (python library)EpistemologyMathematics educationSocial psychologyPedagogyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Epistemic beliefs are individuals’ beliefs about knowledge and knowing. Modelling them is currently based on two central assumptions. First, epistemic beliefs are conceptualized as a multi-level construct, i.e. they exist on a general, academic, domain-specific and/or topic-specific level. Second, research assumes that their more concrete levels predict learning processes and outcomes more strongly than their more general levels. However, studies directly investigating these assumptions are still missing. 975 prospective retailers, wholesalers, bank assistants, and industrial assistants reported their grades and learning motivation in accounting and marketing as well as their epistemic beliefs in an effort to prove both assumptions within the context of Vocational Education and Training. Second-order confirmatory factor analysis confirms the multi-level conceptualization of epistemic beliefs. The findings here indicate a superiority of domain- and topic-specific epistemic beliefs compared to general epistemic beliefs for predicting motivation and achievement in marketing and accounting. The study concludes that domainand topic-specific epistemic beliefs explain different facets of learning phenomena. In addition, further research should concentrate more on both the domain- and topic-specific levels.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.062
Threshold uncertainty score0.337

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.076
GPT teacher head0.418
Teacher spread0.343 · 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 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

Citations13
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueWorld Journal of EducationSame topicEducational Strategies and EpistemologiesFrench-language works237,207