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Record W3126437325 · doi:10.29173/iasl7636

From Dependence to Independence

2021· article· en· W3126437325 on OpenAlexvenueno aff
Intan Azura Mokhtar, Shaheen Majid, Schubert Foo

Bibliographic record

VenueIASL Annual Conference Proceedings · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsnot available
Fundersnot available
KeywordsCoachingInformation literacyPsychologyMathematics educationEmpirical researchPedagogyMedical educationMedicine

Abstract

fetched live from OpenAlex

Although it is widely believed that information literacy (IL) competencies are useful in helping students perform better in their schoolwork and beyond, limited empirical evidence is available showing the relationship between IL competencies and IL education. While a lot of research has been done worldwide and most of the findings have proven that IL is a much-needed skill by students, little research has been conducted on IL teaching approaches or what is termed IL pedagogy. To date, studies on IL have mainly focused only on students’ information skills per se, on library skills or on ICT education. None of these studies has assessed the different approaches to IL education. This paper provides an overview of a research study that investigates the impact of an IL teaching approach in the form of personalised coaching, which is grounded in the pedagogy known as mediated learning, on students’ level and applicability of IL competencies. Through the application of a quasi-experimental pretest-posttest control-group design, as well as student responses in the post-experiment semi-structured group interviews, it was found that personalised coaching (or mediated learning) helps students perform better in the learning and application of IL competencies.

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 categoriesScholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesScholarly communication, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.456
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.014
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.025
GPT teacher head0.299
Teacher spread0.274 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2021
Admission routes1
Has abstractyes

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