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Record W2297442623 · doi:10.5539/ies.v9n2p66

An Analysis of Grades, Class Level and Faculty Evaluation Scores in the United Arab Emirates

2016· article· en· W2297442623 on OpenAlexvenueno aff
Lee Waller

Bibliographic record

VenueInternational Education Studies · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyMathematics educationCourse evaluationHigher educationClass (philosophy)Medical educationMedicine

Abstract

fetched live from OpenAlex

<p class="apa">This study examined the results of a student evaluation of faculty against the grades awarded and the level of the course for a higher education institution in the United Arab Emirates. The purpose of the study was to determine if the grades awarded in the course and/or level of the course impacted the evaluation scores awarded to the faculty member. The study utilized a 25-question student perception survey coupling course results with the overall course grade point average (GPA) and the course level. All courses were undergraduate. Descriptives for the responses were obtained prior to conducting a factor analysis for the purposes of dimension reduction. The analysis included 184 course pairings. The data set was examined to verify satisfaction of assumptions appropriate for factor analysis. Reliability analysis yielded a Chronbach’s alpha of 0.974. The factor analysis identified three underlying factors accounting for 80.07% of the variance. These three factors were identified as (1) overall perception of instruction, (2) the relationship of the grade and course level and (3) course management. Results of the study did not indicate that the grades given in a class nor the level of the course significantly affected the evaluations provided by the students. Grades and the level of the course were found to align. Student achievement in the course was also found to relate to the student’s perception of fair treatment by the faculty member.</p>

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)0.004
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score0.807

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.493
GPT teacher head0.593
Teacher spread0.100 · 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 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

Citations0
Published2016
Admission routes1
Has abstractyes

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