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Record W2065553009 · doi:10.5539/jel.v2n2p1

Effects of Developed Electronic Instructional Medium on Students’ Achievement in Biology

2013· article· en· W2065553009 on OpenAlexvenueno aff
Nsofor Caroline Chinna, Momoh Gabriel Dada

Bibliographic record

VenueJournal of Education and Learning · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsAnalysis of covarianceMathematics educationAchievement testInternal consistencyConsistency (knowledge bases)Test (biology)PsychologyAcademic achievementMathematicsStatisticsStandardized testBiologyPsychometricsDevelopmental psychology

Abstract

fetched live from OpenAlex

The study investigated the effects of developed electronic instructional medium (video DVD instructional package) on students’ achievement in Biology. It was guided by two research questions and two hypotheses, using a quasi-experimental, pretest-postest control group design. The sample comprised of 180 senior secondary, year two students from six schools located in the three education zones of Niger State. The subjects were divided into an experimental group (electronic medium instruction) and a control group (traditional lecture instruction). Structured Biology Achievement Test (SBAT) with internal consistency reliability co-efficient of 0.83 was used to measure the student’s achievement before and after the treatment. The data obtained from the study were analyzed using Analysis of Covariance (ANCOVA). Results from Means, Analysis of Covariance (ANCOVA) and Scheffe test indicated that the achievement of students in biology greatly improved with the use of electronic instructional medium. Students’ gender had no significant effect on their achievement in biology when electronic medium was used. These results have implications for innovative use of instructional media and creating sound strategies for disseminating science in the classroom.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.414
Threshold uncertainty score0.170

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.000

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 head0.009
GPT teacher head0.353
Teacher spread0.344 · 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 teacher head, 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

Citations14
Published2013
Admission routes1
Has abstractyes

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