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Enregistrement W234265372

Predicting Performance of One-Year MBA Students

2007· article· en· W234265372 sur OpenAlex

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aboutLe titre ou le résumé porte un signal canadien du lexique géographique.
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Notice bibliographique

RevueCollege student journal · 2007
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueManagement and Marketing Education
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPsychologyMathematics educationAccreditationPredictabilityMedical educationStatistics
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Although several studies have been performed, Graduate Admissions programs are still encountering difficulties uncovering criteria that will predict academic success in their programs. Researchers have analyzed Executive, full and part-time MBA programs and can only conclude that undergraduate grade point average and the GMAT are significant factors to predicting success; however, predictability with these factors is less than 19%. Similar to other studies, regression analysis is used to analyze potential factors to predict success in a highly-controlled OneYear MBA program at an AACSB-accredited American college on the United States-Canadian border. Model predictability increases over previous studies as the Canadian-factor, GMAT-Verbal and undergraduate grade point average are significant factors. These results raise questions regarding the significance of the GMAT-Verbal versus the GMAT-Quantitative and differences between American and Canadian school systems. LITERATURE REVIEW Since admissions decisions are critical at educational institutions, various studies have reviewed the incoming factors that may assist in predicting MBA student performance. Researchers point to the necessity for each MBA program to individually determine the relationship among predictor variables and graduate level performance in its program [Wright and Palmer, 1997]. Various programs have different admissions processes ranging from review of undergraduate record (grade point average), type of courses taken, trends and progress over time, level of analytical and quantitative skill required in current and past professions, recommendations, and the Graduate Management Aptitude Test (GMAT). Noteworthy points to this study include analysis of prediction factors for a One-Year, one-classroom MBA cohort program; the Canadian, GMAT-Verbal and undergraduate grade point average (GPA) are significant factors; and an improvement in predictability over similar studies. Previous studies to predict MBA performance focus on predicting overall MBA quality point average (QPA). Factors tested to predict performance include, but are not limited to: total GMAT, GMAT-Verbal score, GMAT-Quantitative score, undergraduate grade point average (GPA), junior/senior GPA, length of time out of school, sex, age, undergraduate major, undergraduate institution, undergraduate major, gender, and work experience [Braunstein, 2002; Carver, Jr. and King, 1994; Deckro and Woudenberg, 1973, Fisher and Resnick, 1990; Graham, 1991; Hecht et al., 1989; McClure, 1986; Paolillo, 1982; Remus and Wong, 1982; Sobol, 1984; Wilson and Hardgrave, 1995; Wright and Palmer, 1997]. Researchers vary in their handling of students dismissed or who left the program, and current students versus graduates. Over twenty-years of similar studies, results demonstrate total GMAT and undergraduate GPA are always significant factors [Braunstein, 2002; Hecht et al., 1989; McClure, 1986; Paolillo, 1982; Wright and Palmer, 1997] with prediction equations explaining 19% or less of the graduate GPA [Wilson and Hardgrave, 1995]. Total GMAT has been shown to be statistically significant in differentiating high performers versus other students [Wright and Palmer, 1997; Braunstein, 2002]. Only one exception to this predictability has been uncovered--an Executive MBA program at Tulane in New Orleans, Louisiana, where the coefficient of determination was .36 [Arnold, Chakravarty and Balakrishnan, 1996]. In this Executive MBA program, GMAT remains the best single indicator, but qualitative factors, such as work experience, motivation and business success, enhance the predictive ability of the model [Arnold, Chakravarty and Balakrishnan, 1996]. In another study, the GMAT-Verbal score, but not the GMAT-Quantitative score, is a significant factor to differentiate between high performers and other students [Wright and Palmer, 1997]. The authors acknowledge that the GMAT-Verbal may be a factor of curriculum content and may not be significant for every program. …

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,069
Score d'incertitude au seuil0,552

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,014
Tête enseignante GPT0,271
Écart entre enseignants0,257 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle