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Enregistrement W214290705

Predicting Performance of MBA Students: Comparing the Part-Time MBA Program and the One-Year Program.

2009· article· en· W214290705 sur OpenAlexaboutno aff
Lynn A. Fish, F. Scott Wilson

Notice bibliographique

RevueCollege student journal · 2009
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueManagement and Marketing Education
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPsychologyCurriculumAccreditationMedical educationMathematics educationGraduation (instrument)PedagogyMedicineMathematics
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

While predictor variables for success in MBA programs vary between schools, are they different within the same business school? At an AACSB-accredited school, although the curriculum and professors are essentially the same between the One-Year MBA and Part-Time MBA programs, the significant factors to predict success in each program are not. Results indicate significant factors to predict graduate performance for a One-Year MBA program include the GMAT-Verbal, undergraduate grade point average, and a Canadian factor. While the part-time program significant factors include GMAT-Verbal and undergraduate grade point average, they also include GMAT-Quantitative and age. These results favor using slightly different entrance criteria for each program, and the suggestion for faculty to consider the educational process differences between the two programs. ********** Graduate business programs continue to seek admission criteria that predict academic success. Studies indicate the need for each MBA program to individually determine the relationship among predictor variables and graduate level performance in its program [Wright and Palmer, 1997]; however, does the same curriculum delivered in a different framework require different predictor variables? Are there significant differences at the same school between a full-time MBA program and a part-time, evening program to warrant different entrance criteria? If the curriculum content and delivery process are the same, should the same incoming factors be considered for admission into each respective program or are there potentially other process differences that exist? In general, if both programs are delivered by the same professors that use similar materials and testing to deliver courses, do graduates achieve the same outcome level? These questions form the basis for our study: comparison of predictability for two MBA programs in the same school--a One-Year MBA program and a traditional, Part-Time, evening MBA program. Literature Review Business admissions use different processes ranging from review of undergraduate grade point average (GPA); transcript analysis that reviews the 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 Admission Test (GMAT). Relevant admission factors to executive, full and Part-Time MBA programs around the world have been researched; however, the only conclusion that can be agreed upon is that GMAT and undergraduate GPA are significant factors to predicting MBA performance as measured by the graduate GPA [Wright & Palmer, 1994; Braunstein, 2002; Hecht et al., 1989; McClure et al., 1986; Paolillo, 1982; Wright and Palmer, 1997; Sireci & Talento-Miller, 2006]. Predictability, with only GMAT and undergraduate GPA as factors, is typically less than 19% of the graduate GPA [Wilson and Hardgrave, 1995], but when additional factors are considered, predictability as high as 36% for an Executive MBA program at Tulane in New Orleans, Louisiana has been reported [Arnold, Chakravarty and Balakrishnan, 1996]. Some studies favor GMAT as the stronger predictor over undergraduate GPA [Carver and King, 1994], while others favor undergraduate GPA as the stronger predictor over the GMAT [Yang and Lu, 2001]. In yet another study, the Graduate Records Exam (GRE) is a better predictor of performance than GMAT [Nilsson, 1995]. Other predictor variables are significant in some studies, but the results are not always replicated in others. The majority of studies focus on predicting exiting graduate GPA, although some attempt to model the first year performance. While GMAT and undergraduate GPA are always included in the models, other factors, such as GMAT--Verbal percentage, GMAT-Quantitative percentage, Junior/Senior GPA, length of time out of school, gender, age, undergraduate major, undergraduate institution, undergraduate major, gender, and work experience, have been tested and yield varying results as discussed below [Braunstein, 2002; Carver, Jr. …

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,016
Version: metacan-v3-hybrid-931329e0061cStatut 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 enseignants0,018
Score d'incertitude au seuil0,036

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,016
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,000
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,001

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.

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

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations15
Publié2009
Routes d'admission1
Résumé présentoui

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