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Record W2994020164 · doi:10.25115/ejrep.v17i49.2421

Latin American undergraduates and learning patterns in the transition to higher education: an exploratory study in Colombia

2019· article· en· W2994020164 on OpenAlex
Martínez Fernández, Laura Betiana García-Ravidá, Cristina Mumbardó Adams

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueElectronic Journal of Research in Educational Psychology · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Outcomes and Influences
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsLearning stylesPath analysis (statistics)Meaning (existential)PsychologyDimension (graph theory)Multivariate analysis of varianceDescriptive statisticsPerceptionPerspective (graphical)Exploratory researchMathematics educationSociologySocial scienceComputer scienceArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

Introduction. The aim of this study was to analyse the relationship between learning patterns, associated factors, and academic performance in 115 Colombian first-year university students. We posed the need to discuss the Vermunt model in other contexts, with an aim to supply evidence toward a more robust, inclusive model in analyzing learning processes. Method. Data were collected using a Spanish version of the Inventory of Learning Style (Martínez-Fernández et al., 2009; Vermunt, 1998). Additionally, we collected data about the students’ age, gender, dedication to study, perception of teaching, effort, and academic performance. The data were processed by means of descriptive analysis, correlation, MANOVA, and path analysis. Results. The results show a structure of four learning patterns consisting of different factor combinations according to Vermunt: 1) Meaning-directed with external regulation (MD/er); 2) Passive-Idealistic (PI); 3) Passive-Motivated (PM); and 4) Reproduction-directed with lack of regulation (RD/lr). The relationship between learning patterns and the different factors was not sustained. However, we found an interesting explanation of academic performance from the perspective of self- and external regulation.Discussion and Conclusion. Based on these results, we defend the need to make the cultural dimension of learning patterns a key topic in the research agenda on learning processes.

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.

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)0.007
metaresearch head score (Gemma)0.000
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 score0.113
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.085
GPT teacher head0.506
Teacher spread0.420 · 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