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Record W4406313583 · doi:10.21083/ajote.v13i3.7695

Effectiveness of station-rotation and flipped-blended learning strategies in improving study habits and academic performance of secondary school biology students in Osun State, Nigeria

2024· article· en· W4406313583 on OpenAlexvenueno aff
Folake Yoade, Olayemi Asaaju

Bibliographic record

VenueAfrican Journal of Teacher Education · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methods
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationBlended learningTest (biology)PopulationClass (philosophy)Flipped classroomBiologyPsychologyEducational technologyComputer scienceMedicineEcologyEnvironmental health

Abstract

fetched live from OpenAlex

The study investigated the effectiveness of Station-rotation and Flipped-blended learning in improving study habits and Academic achievement of students in Osun State Secondary School. It adopted quasi experimental pre-test, post-test control group design. The population for the study comprised all Biology students in senior Secondary Schools in Osun State. The sample consisted of 115 senior secondary school Biology students randomly selected from three secondary schools in their intact classes. Each intact class was randomly assigned to an experimental or control group. The instrument for the study comprised the Biology Achievement Test (BAT) and Study Habit Inventory (SHI). The result showed that students taught using the station-rotation blended learning strategy performed better than those taught using the flipped blended learning strategy. The result also showed that the study habits of students taught using station-rotation strategy were better enhanced than those taught using the flipped blended learning strategy. The study recommended the use of a more novel strategy like the station-rotation blended learning strategy in delivering Biology contents in secondary schools.

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)0.001
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.406
Teacher spread0.386 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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