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Record W3013242641 · doi:10.5539/gjhs.v12n5p38

Enhancing Interest Among Senior Secondary Students in Expository Essay Writing in South East Nigeria: The Reciprocal Peer Tutoring Approach

2020· article· en· W3013242641 on OpenAlexvenueno aff
Amuche Patricia Nnamani, Josephine Akabogu

Bibliographic record

VenueGlobal Journal of Health Science · 2020
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsnot available
Fundersnot available
KeywordsReciprocalMathematics educationAnalysis of covariancePsychologyPedagogyMathematics

Abstract

fetched live from OpenAlex

The study focused on investigating the impact reciprocal peer tutoring teaching approach has in promoting interest among senior secondary school students in writing expository essay in Igbo –Etiti Local Government Area, South-East, Nigeria. The effect of gender in promoting interest in expository essay writing among senior secondary school students was also investigated. Two research questions and three null hypotheses guided the study. The study adopted a quasi- experimental design. 75 (32 males and 43 females) in two intact classes consisted of the sample for the study. Expository Essay Writing Interest Inventory (EEWII) which was face validated by four consultants was used as an instrument to data for the study. Mean, standard deviation and analysis of covariance (ANCOVA) were used to analyze the data collected. Results obtained revealed that reciprocal peer tutoring teaching approach was effective in promoting interest in expository essay among the students. The result also indicated that the variation in the mean interest scores of male and female students in expository essay was not significant. The interaction effect of gender and the teaching approach on mean interest scores of senior secondary school students in expository essay writing was also not significant. Hence, the researchers recommended that secondary school teachers should adopt this teaching approach for expository essay writing teaching 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 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)0.026
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 score0.025
Threshold uncertainty score0.932

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0260.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.070
GPT teacher head0.409
Teacher spread0.340 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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