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Record W2754613179 · doi:10.18438/b8pw9b

Flipped Library Instruction Does Not Lead to Learning Gains for First-Year English Students

2017· article· en· W2754613179 on OpenAlexvenueno aff
Kimberly Miller

Bibliographic record

VenueEvidence Based Library and Information Practice · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Marketing Education
Canadian institutionsnot available
Fundersnot available
KeywordsRubricInformation literacyLibrary instructionClass (philosophy)Flipped classroomMathematics educationComputer scienceBlended learningAcademic yearPsychologyMedical educationLibrary scienceEducational technologyMedicineArtificial intelligence

Abstract

fetched live from OpenAlex

A Review of: Rivera, E. (2017). Flipping the classroom in freshman English library instruction: A comparison study of a flipped class versus a traditional lecture method. New Review of Academic Librarianship, 23(1), 18-27. http://dx.doi.org/10.1080/13614533.2016.1244770 Abstract Objective – To determine whether a flipped classroom approach to freshman English information literacy instruction improves student learning outcomes. Design – Quasi-experimental. Setting – Private suburban university with 7,000 graduate and undergraduate students. Subjects – First-year English students. Methods – Students in six sections of first-year “English 2” received library instruction; three sections received flipped library instruction and three sections received traditional library instruction. Students in the flipped classroom sections were assigned two videos to watch before class, as an introduction to searching the Library’s catalog and key academic databases. These students were also expected to complete pre-class exercises that allowed them to practice what they learned through the videos. The face-to-face classes involved a review of the flipped materials alongside additional activities. Works cited pages from the students’ final papers were collected from all six sections, 31 from the flipped sections and 34 from the non-flipped sections. A rubric was used to rate the works cited pages. The rubric was based on the Association of College and Research Libraries’ Information Literacy Competency Standards for Higher Education (ACRL, 2000), Standard Two, Outcome 3a, and included three criteria: “authority,” “timeliness,” and “variety.” Each criterion was rated at one of three levels: “exemplary,” “competent,” or “developing.” Main Results – Works cited pages from the students who received non-flipped instruction were more likely to score “exemplary” for at least one of the three criteria when compared to works cited pages from the flipped instruction students (68.6% vs. 52.7%). Differences were found in the scores for “timeliness” (88.2% non-flipped scored “exemplary” compared to 58% flipped), and “variety” (55.9% non-flipped scored “exemplary” vs. 35.5% flipped). This pattern was not found for the “authority” category, in which 61.8% of non-flipped works cited pages scored “exemplary” vs. 64.5% of flipped works cited pages. Conclusion – The results suggest that the flipped library instruction approach did not improve student learning outcomes. The study’s findings are limited by the small sample size, the unknown impact of the variability of research assignments between sections, and the lack of control over whether students in the flipped sections completed the pre-class assignments. The author also notes that future research should examine how well the content of flipped library instruction mirrors that of non-flipped instruction sessions. The study concludes that the flipped classroom model needs further research to understand whether it is a strong fit for one-shot library instruction.

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)0.001
metaresearch head score (Gemma)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0200.004

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 head0.018
GPT teacher head0.274
Teacher spread0.255 · 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".

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Citations1
Published2017
Admission routes1
Has abstractyes

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