Notice bibliographique
Résumé
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.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».