Student wastage at Edinburgh University II. A prediction scale

Kapur, R. L.

doi:10.1111/j.1468-2273.1972.tb01921.x

Cited by 2 publications

(2 citation statements)

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“…All take a number of predictor variables and combine them in some way; an ‘at‐risk’ threshold is then set such that failure is predicted if performance falls below this level. The predictors are most commonly class tests and/or university selection data but some studies have used social and personality variables (Kapur 1972). There is little consistency in the way predictors arc combined; important differences in ‘background factors’(difficulty of subject matter, support for students, etc.)…”

Section: Introductionmentioning

confidence: 99%

Can past academic criteria predict students at risk of future failure?

1988

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A significant minority of medical and dental students fail their undergraduate courses. Early warning systems (EWSs) have been developed in some areas of higher education to predict 'at-risk' students at an early remedial stage. An attempt is made to develop an EWS to predict failure in the bacteriology component of the Batchelor of Dental Surgery course at Manchester Dental School. A system based on class tests and previous end-of-year performance is derived which is used to predict those students likely to fail or fall in the bottom 20-25% in their finals examination. The predictors are combined by a simple equal weights method, which is found to have the same predictive power as using multiple regression. Failure was correctly predicted in 60% of cases, at the expense of 71% false alarms. The high number of false alarms reflects the low failure rate rather than the lack of predictive information. The need for effective cross-validation of EWSs is discussed; many previous studies have not been tested on independent data.

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Section: Introductionmentioning

confidence: 99%

Can past academic criteria predict students at risk of future failure?

1988

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“…Several studies have developed and evaluated prediction schemes, using academic and other criteria combined in a variety of ways (Nisbett & Welsh 1966; Kapur 1972; McGuire 1977; Sherwin & Child 1976; Herman & Veloski 1981). According to Wainer & Thissen (1976), systems which assume equal contributions by all the explanatory variables are simpler to devise and also yield results comparable in predictive validity to those achieved by more complex regression models.…”

Section: Introductionmentioning

confidence: 99%

Predicting performance in anatomy using antecedent academic criteria

Nnodim

1994

Medical Education

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The performance of two multivariate prediction models (equal and differential weights) at forecasting the outcome of the comprehensive examination in preclinical anatomy is reported. The models were devised by regression analysis, using scores in two antecedent examinations which correlated highly with the criterion variable, namely the science aggregate at the Joint Matriculation Examination and the premedical science aggregate. Five years of data were considered and the scores for each year were computed using the formula derived from scores of the preceding year. The average failure rate for the period covered by the study was 31.28%. The correlation between actual and predicted scores was positive and moderately high (Pearson r = 0.47 and 0.49 for the equal- and differential-weights models respectively). On average, the equal-weights model correctly predicted 33.9% of failures, with a false alarm rate of 28.4%, compared with 44.3% and 38.9% respectively for the differential-weights model. In predicting candidates whose scores would fall in the bottom half and bottom third of the class, no statistically significant difference was noted between the hit rates achieved by both models. The equal-weights model is simple to formulate and efficient in operation. Although its hit rate at the pass/fail boundary was lower than that of the differential-weights model, it yielded a significantly lower false alarm rate (28.4% vs 38.9%). The relevance and application of performance prediction to the planning of remedial instruction are discussed.

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Student wastage at Edinburgh University II. A prediction scale

Cited by 2 publications

References 4 publications

Can past academic criteria predict students at risk of future failure?

Can past academic criteria predict students at risk of future failure?

Predicting performance in anatomy using antecedent academic criteria

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