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Record W2975976883 · doi:10.18733/cpi29481

Teacher-Centered vs. Student-Centered

2019· article· en· W2975976883 on OpenAlexaffvenue
Lawrence Muganga, Peter Ssenkusu

Bibliographic record

VenueCultural and Pedagogical Inquiry · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsWrightFocus groupStudent centeredMathematics educationPsychologyPedagogyPerceptionSociologyComputer science

Abstract

fetched live from OpenAlex

Wright (2011) distinguishes between teacher-centered and student-centered learning approaches along a spectrum of five dimensions: power balance, course content function, teacher and student roles, responsibility for learning, and assessment purposes and processes. Based on Wright’s framework, this study explores students’ perceptions of their experience with teaching methods at Uganda’s Makerere University. Specifically, the investigation uses a mixed-methods research approach that combines survey data with focus group discussions. A total of 82 students volunteered, with 54 returning questionnaires. From among the 54 students, eight were chosen for focus group discussions. Students provided information about course content, educational philosophy, and teaching activities. In the area of course content, students reported that course completion and examination results outweighed skill development. The results for educational philosophy showed that the preparation of compliant citizens took precedence over the development of self-reliant individuals. Finally, the findings for teaching activities indicated that while teacher-centered tasks still predominated, several students had been exposed to some student-centered activities.

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)0.011
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0040.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.663
GPT teacher head0.533
Teacher spread0.130 · 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 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

Citations68
Published2019
Admission routes2
Has abstractyes

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