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Record W2772120065 · doi:10.21083/ajote.v6i0.3624

Challenges in Ethiopian Teacher Education Pedagogy: Resistance Factors to Innovative Teaching-Learning Practices

2017· article· en· W2772120065 on OpenAlexvenueno aff
Abiy Agegnehu Zewdu

Bibliographic record

VenueAfrican Journal of Teacher Education · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsQuality (philosophy)Resistance (ecology)Process (computing)Affect (linguistics)Mathematics educationTeaching methodPsychologyDimension (graph theory)PedagogyMedical educationComputer scienceMedicine

Abstract

fetched live from OpenAlex

Quality is at the heart of any education and training system. It influences what trainees learn, how well they learn and what benefits they draw from their education and training. Whether a particular education and training system is of high or low quality can be judged in terms of input, output and process. Until recently, however, much discussion of educational quality has been centered on only system inputs in terms of the provision of teachers, educators, teaching materials and other facilities, and on output in terms of trainees’ achievement. Little or no attention is given to the teaching-learning process, the dimension which involves what really happens in the classroom. This study thus aims at finding out the extent to which innovative approach to teaching and learning are employed in the Ethiopian primary teacher training classrooms, to identify the factors that affect its implementation, and to recommend better ways and means for further improvement. In conclusion, the study found that traditional lecture methods, in which teachers talk and students listen, dominate most classrooms. The common obstacles to the employment of innovative methods of teaching as found out by this study are: the Ethiopian tradition of teaching, lack of institutional support, and learning resources, teachers’ lack of expertise, inappropriate curricular materials and student teachers’ lack of prior experience to actively participate in the teaching and learning process.

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)0.005
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.466
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.098
GPT teacher head0.459
Teacher spread0.361 · 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.

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

Citations3
Published2017
Admission routes1
Has abstractyes

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