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Record W1967551837 · doi:10.5539/ass.v10n12p184

The Ideal Psychology Teacher: Qualitative Analysis of Views from Brunei GCE A-Level Students and Trainee Psychology Teachers

2014· article· en· W1967551837 on OpenAlexvenueno aff
Nurul Azureen Omar, Sri Ridhwanah Matarsat, Nur Hafizah Azmin, Veronica Chung Ai Wei, Mohd Mu izzuddin Mohd Nasir, Ummi Kalthum Syahirah Sahari, Masitah Shahrıll, Lawrence Mundia

Bibliographic record

VenueAsian Social Science · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIdeal (ethics)PsychologyEducational psychologyContext (archaeology)CertificateMathematics educationCognitionSocial psychologyPedagogy

Abstract

fetched live from OpenAlex

We qualitatively explored the notion of the ideal teacher from the context of pre-university Brunei General Certificate of Education Advanced Level (GCE A-Level) psychology students and trainee psychology teachers. Both previous research and our own analyses on this concept revealed that the so-called ideal teacher was neither a perfect nor a super teacher but rather an effective instructor who was firm, fair, and a good communicator. Psychology students of various ability levels (high achievers, average students, and low scorers) gave slightly different descriptive characteristics for the ideal teacher. More-able students preferred a cognitive-oriented teacher while less-able students emphasized the affective-oriented instructor. Students in the middle range of the ability scale endorsed both cognitive and affective traits in the ideal teacher traits. Trainee psychology teachers closely resembled the higher achieving GCE A-Level psychology students in their descriptions of the ideal teacher. The findings have implications for teaching and assessing psychology students that we discuss. Further mixed-methods research was recommended to generate more insightful outcomes.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.577
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.006
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.252
GPT teacher head0.553
Teacher spread0.301 · 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; both teacher heads agree on what is shown here.

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

Citations19
Published2014
Admission routes1
Has abstractyes

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