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Record W4403781337 · doi:10.18488/61.v13i1.3938

Secondary school learners’ academic achievement in mathematics through a practical instruction teaching approach in the Kigezi and Ankole regions of Uganda

2024· article· en· W4403781337 on OpenAlexaff
Frank Murangira, Alphonse Uworwabayeho, Innocent Twagilimana

Bibliographic record

VenueInternational Journal of Education and Practice · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Assessment and Pedagogy
Canadian institutionsEducation and Early Childhood Development
FundersUniversity of Rwanda
KeywordsMathematics educationAcademic achievementComputer scienceMathematics

Abstract

fetched live from OpenAlex

Addressing the challenge of subpar mathematics achievement levels in secondary education has gained global attention. Due to teachers’ inability to teach mathematics without real objects to explain it in real life, learners take the subject as very abstract, hence the poor academic achievement in national examinations. This qualitative case study investigates the impact of practical instruction on the mathematics achievement of secondary school students in Uganda. The research involved interviews with sixteen teachers from selected secondary schools in the Kigezi and Ankole regions to gain insights into their perspectives on the utilization and advantages of practical instruction in mathematics. Thematic analysis was employed to analyze the data. Regarding assessment strategies, the study found that there is a need to incorporate alternative assessment methods that can effectively assess the range of learners’ mathematics abilities. The study’s findings highlight that teachers view practical instruction as a valuable approach that enhances students' mathematics achievement by involving them in hands-on experience with real objects and learner-centered activities. Based on these results, the study recommends the widespread adoption of practical instruction as a means to elevate the quality of mathematics education in Uganda, ultimately addressing the prevalent challenges in mathematics achievement at the secondary school level.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.695
Threshold uncertainty score0.344

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
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 head0.099
GPT teacher head0.498
Teacher spread0.400 · 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
Published2024
Admission routes1
Has abstractyes

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