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Record W2756465128 · doi:10.18438/b86w90

A Systematic Review of Information Literacy Programs in Higher Education: Effects of Face-to-Face, Online, and Blended Formats on Student Skills and Views

2017· review· en· W2756465128 on OpenAlexvenueno aff
Alison Weightman, D. J. J. Farnell, Delyth Morris, Heather Strange, Gillian Hallam

Bibliographic record

VenueEvidence Based Library and Information Practice · 2017
Typereview
Languageen
FieldSocial Sciences
TopicTechnology-Enhanced Education Studies
Canadian institutionsnot available
FundersBarwon Health FoundationCardiff UniversityUniversity of Salford ManchesterUniversity of South Florida
KeywordsMedical educationInformation literacyFace-to-faceInclusion (mineral)PsychologyComputer scienceBlended learningMathematics educationThematic analysisMeta-analysisPedagogyEducational technologyQualitative researchMedicine

Abstract

fetched live from OpenAlex

Abstract Objective – Evidence from systematic reviews a decade ago suggested that face-to-face and online methods to provide information literacy training in universities were equally effective in terms of skills learnt, but there was a lack of robust comparative research. The objectives of this review were (1) to update these findings with the inclusion of more recent primary research; (2) to further enhance the summary of existing evidence by including studies of blended formats (with components of both online and face-to-face teaching) compared to single format education; and (3) to explore student views on the various formats employed. Methods – Authors searched seven databases along with a range of supplementary search methods to identify comparative research studies, dated January 1995 to October 2016, exploring skill outcomes for students enrolled in higher education programs. There were 33 studies included, of which 19 also contained comparative data on student views. Where feasible, meta-analyses were carried out to provide summary estimates of skills development and a thematic analysis was completed to identify student views across the different formats. Results – A large majority of studies (27 of 33; 82%) found no statistically significant difference between formats in skills outcomes for students. Of 13 studies that could be included in a meta-analysis, the standardized mean difference (SMD) between skill test results for face-to-face versus online formats was -0.01 (95% confidence interval -0.28 to 0.26). Of ten studies comparing blended to single delivery format, seven (70%) found no statistically significant difference between formats, and the remaining studies had mixed outcomes. From the limited evidence available across all studies, there is a potential dichotomy between outcomes measured via skill test and assignment (course work) which is worthy of further investigation. The thematic analysis of student views found no preference in relation to format on a range of measures in 14 of 19 studies (74%). The remainder identified that students perceived advantages and disadvantages for each format but had no overall preference. Conclusions – There is compelling evidence that information literacy training is effective and well received across a range of delivery formats. Further research looking at blended versus single format methods, and the time implications for each, as well as comparing assignment to skill test outcomes would be valuable. Future studies should adopt a methodologically robust design (such as the randomized controlled trial) with a large student population and validated outcome measures.

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)0.023
metaresearch head score (Gemma)0.096
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.023
Threshold uncertainty score0.122

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.096
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0120.016
Bibliometrics0.0110.012
Science and technology studies0.0010.001
Scholarly communication0.0040.004
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.057
GPT teacher head0.421
Teacher spread0.364 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations44
Published2017
Admission routes1
Has abstractyes

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