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Record W2107545228 · doi:10.5539/ijms.v7n1p78

Taking or Not Taking a Class: Students’ Perceived Physiognomies Associated with Syllabi

2015· article· en· W2107545228 on OpenAlexvenueno aff
Reza Motameni, William C. Rice, Patricia LaRosa

Bibliographic record

VenueInternational Journal of Marketing Studies · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsnot available
Fundersnot available
KeywordsSyllabusConceptualizationPerceptionClass (philosophy)Presentation (obstetrics)PsychologyMathematics educationComputer scienceMedicineArtificial intelligence

Abstract

fetched live from OpenAlex

This paper has tried to reconcile and synthesize the existing literature on course syllabi and create an interconnected and cohesive conceptual framework capable of bringing together the findings of previous conceptualization. The spin-off objectives involved conducting a survey to establish the students’ overall perceptions regarding the course syllabi in general and the most recent syllabi they have been exposed to in particular. More importantly, we wanted to find out if the students’ current perceptions were significantly different from an ideal syllabus perception and whether students’ gender, ethnicity, academic standing, option, time of class, and class structure (i.e., regular vs. totally online) influenced students’ perception of a syllabus. This study identified four factors contributing to an ideal syllabus. This study extended the previous research on syllabi because no other study has focused on syllabus format and its visual presentation. The findings suggest that alternative strategies should be used to communicate syllabus information.

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)0.019
metaresearch head score (Gemma)0.118
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.173
Threshold uncertainty score0.889

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.118
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.276
GPT teacher head0.501
Teacher spread0.226 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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