Bibliographic record
Abstract
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. …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".