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Record W3200963109 · doi:10.29173/slw8237

Information Literacy Skills of High School Students in Botswana

2021· article· en· W3200963109 on OpenAlexvenueno aff
Christinah Dipetso, Kgomotso H. Moahi

Bibliographic record

VenueSchool Libraries Worldwide · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsnot available
Fundersnot available
KeywordsInformation literacyLiteracyMathematics educationSubject (documents)PedagogyPsychologyMedical educationComputer scienceLibrary scienceMedicine

Abstract

fetched live from OpenAlex

In this study, we assessed information literacy skills of secondary school students in Gaborone, Botswana and established whether and how they are being taught IL skills. The study employed the Big 6 model and was a case study design in which qualitative methods were used. The findings showed that students had low to fair information literacy skills in problem definition, information search strategy, and location of information. Use of information, synthesis, and evaluation were areas of significant challenge. Teachers were aware of information literacy, but were not intentional in their teaching of IL skills. The recommendations are four-fold, a) that education authorities in Botswana should ensure that information literacy is an integral part of students' education by requiring subject teachers as well as teacher-librarians to be intentional in their approach to developing students' information literacy, b) collaboration between teachers and teacher-librarians for more coordinated information literacy initiatives, c) training of both subject teachers and teacher-librarians on pedagogies that integrate the teaching of information literacy.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.006
GPT teacher head0.273
Teacher spread0.267 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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