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Record W4388140339 · doi:10.9734/ajess/2023/v49i31138

Broken Homes and Intact Homes Students’ Academic Attainment in Mathematics in the Kasena-Nankana Municipality, Ghana

2023· article· en· W4388140339 on OpenAlexaff
Ambrose Kombat, Abugri Mumuni Abdulai, Vincent Ninmaal Asigri, Norbert Ayuah

Bibliographic record

VenueAsian Journal of Education and Social Studies · 2023
Typearticle
Languageen
FieldMathematics
TopicMathematics Education and Pedagogy
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMathematics educationTest (biology)Significant differenceSimple random sampleSample (material)Academic achievementPsychologyMathematicsDemographySociologyPopulationStatisticsPhysics

Abstract

fetched live from OpenAlex

Students’ academic attainment in mathematics can be influenced by several factors including the structure of student’s home. This study was therefore conducted to compare the academic achievement of students from intact homes and their colleagues from broken homes in mathematics in the Kasena-Nankana Municipality, Ghana. The study embraced a survey design using 26 students from broken homes who were purposely and conveniently selected and 26 students from intact homes selected using simple random sampling technique. The study relied on secondary data (mathematics scores from students report cards) and the data was analysed using independent sample t-test. The test results revealed a significant difference in mean scores between the two groups of students and this difference was in favour of the students from intact homes suggesting that students from broken homes performed poorly compared to those from intact homes. The finding implied that, family structure (broken or intact) is a factor that should not be undermined when looking at students’ performances in mathematics. The study concluded that broken home has a negative impact on basic school students’ academic achievement in mathematics.

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)0.002
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score0.425

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.170
GPT teacher head0.486
Teacher spread0.316 · 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 designQualitative
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
Published2023
Admission routes1
Has abstractyes

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