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Record W2907348195 · doi:10.5430/ijba.v10n1p49

Factors Affecting Students Attraction Towards Jordanian University

2018· article· en· W2907348195 on OpenAlexvenueno aff
Dima Musa Al-Dajani, Mahmood Jasim Alsamydai

Bibliographic record

VenueInternational Journal of Business Administration · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSocioeconomic Development in MENA
Canadian institutionsnot available
Fundersnot available
KeywordsAttractionSelection (genetic algorithm)StatisticDescriptive statisticsPsychologyScale (ratio)Computer scienceMarketingMathematics educationStatisticsMathematicsBusinessGeographyArtificial intelligence

Abstract

fetched live from OpenAlex

The aim of this study is to investigate the factors influencing the attraction of students’ selection of Jordanian universities. For this purpose a model has been designed to test these factors. The model is divided into seven dimensions including: university’s attributes, economic factors, Geographic factors, reference groups and marketing communication, Attraction and Selection University.Seven hypotheses were developed based on the dimensions of the study as well as the relevant literature to achieve this aim, the researchers used a convenience sampling technique. A total of (321) respondents completed the research questionnaire, which distributed in (6) private universities which located in Amman. The distributed questionnaire consists of (35) questions that measured using a 5 points liker scale.The collected data were analyzed by using spas for descriptive statistic and amours analysis to assess the structural and measurement model for the proposed study model.The findings indicated that is an impact of all factors on the attraction of students for selection universities.Also, the findings showed the complementary partial role of attraction between the study factors and students' selection for universities.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.072
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.055
GPT teacher head0.367
Teacher spread0.312 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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