The Graduate Record Examination and Success in an Engineering Management Program: A Case Study

Holt, Daniel T.; Bleckmann, Charles A.; Zitzmann, Charles C.

doi:10.1080/10429247.2006.11431679

Cited by 10 publications

(11 citation statements)

References 14 publications

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“…These results are in line with those of previous studies found in literature (Holt et al 2006;van Rooyen et al, 2006). These studies have shown that prior academic achievement (i.e.…”

Section: Discussion Of Findingssupporting

confidence: 93%

“…This is because grades obtained in TD could be used as a metric for assessing the ability of a student to draw objects. The results of the current study support the findings of previous research (see Holt et al 2006;van Rooyen et al, 2006) which showed that prior academic achievement is positively related to students' academic performance. This outcome is contrary to the findings reported in previous studies which reported that weak relationship exist between prior academic achievement and academic performance (Abisuga et al, 2015;Adewale and Adhuze, 2014;Wao et al, 2016).…”

Section: Discussion Of Findingssupporting

confidence: 91%

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Towards reliable prediction of academic performance of architecture students using data mining techniques

Aluko

Daniel

Oshodi

et al. 2018

JEDT

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PurposeIn recent years, there has been a tremendous increase in the number of applicants seeking placement in the undergraduate architecture programme. It is important to identify new intakes who possess the capability to succeed during the selection phase of admission at universities.Admission variable (i.e. prior academic achievement) is one of the most important criteria considered during selection process. The present study investigates the efficacy of using data mining techniques to predict academic performance of architecture student based on information contained in prior academic achievement. Design/methodology/approachThe input variables, i.e. prior academic achievement, were extracted from students' academic records. Logistic regression and support vector machine (SVM) are the data mining techniques adopted in this study. The collected data was divided into two parts. The first part was used for training the model, while the other part was used to evaluate the predictive accuracy of the developed models. FindingsThe results revealed that SVM model outperformed the logistic regression model in terms of accuracy. Taken together, it is evident that prior academic achievement are good predictors of academic performance of architecture students. Research limitationsAlthough the factors affecting academic performance of students are numerous, the present study focuses on the effect of prior academic achievement on academic performance of architecture students. Originality/valueThe developed SVM model can be used a decision-making tool for selecting new intakes into the architecture program at Nigerian universities.

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“…These results are in line with those of previous studies found in literature (Holt et al 2006;van Rooyen et al, 2006). These studies have shown that prior academic achievement (i.e.…”

Section: Discussion Of Findingssupporting

confidence: 93%

Section: Discussion Of Findingssupporting

confidence: 91%

Towards reliable prediction of academic performance of architecture students using data mining techniques

Aluko

Daniel

Oshodi

et al. 2018

JEDT

View full text Add to dashboard Cite

show abstract

“…Given the similarity in knowledge and skills required for students in graduate mathematics/physical science programs compared to that for students in veterinary medicine, this finding is consistent with results from Kuncel et al (). Holt, Bleckmann, and Zitzmann () evaluated the validity of the GRE for students in an engineering management program at the Air Force Institute of Technology. GRE‐V correlated with first‐year GGPA at .37, GRE‐Q at .20, and GRE‐AW at .18, but UGPA did not correlate significantly with first‐year GGPA ( r = .12).…”

Section: Research On the Validity Of Gre Scores For Predicting Graduamentioning

confidence: 99%

The Validity of Scores from the GRE^® revised General Test for Forecasting Performance in Business Schools: Phase One

Young

Klieger

Bochenek

et al. 2014

ETS Research Report Series

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Scores from the GRE® revised General Test provide important information regarding the verbal and quantitative reasoning abilities and analytical writing skills of applicants to graduate programs. The validity and utility of these scores depend upon the degree to which the scores predict success in graduate and business school in specific contexts. To assess the predictive validity of the GRE test for graduate business programs, we collaborated with a number of universities and obtained data from each university's admissions office and registrar. We focused specifically on part‐time and full‐time students in master's of business administration (MBA) degree programs. Given the nested structure of the data, we used a 2‐level (representing students and institutions) hierarchical linear model (HLM) to estimate regression models with first‐semester MBA grade point average (GPA) or cumulative MBA GPA as the dependent variable and GRE scores and undergraduate GPA (UGPA) as independent variables. For predicting cumulative MBA GPA, the pseudo R‐squared (R2) value was .04 using UGPA, cohort year, and program type as the only predictors; this value increased substantially to .19 with the addition of GRE scores. The HLM results show that both GRE‐Quantitative and GRE‐Verbal scores were statistically significant predictors of both first‐semester MBA GPA and cumulative MBA GPA.

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“…The selection of students by such committees is usually based on prescribed criteria for each programme at the different universities. A robust selection process should ensure that the best students are selected for each programme (Holt et al, 2006;Neame et al, 1992;Young, 1989). Furthermore, the previous academic performance of applicants is crucial information needed to aid the selection process.…”

Section: Introductionmentioning

confidence: 99%

Predicting the academic success of architecture students by pre-enrolment requirement: using machine-learning techniques

Aluko

Adenuga

Kukoyi

et al. 2016

CEB

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In recent years, there has been an increase in the number of applicants seeking admission into architecture programmes. As expected, prior academic performance (also referred to as pre-enrolment requirement) is a major factor considered during the process of selecting applicants. In the present study, machine learning models were used to predict academic success of architecture students based on information provided in prior academic performance. Two modeling techniques, namely K-nearest neighbour (k-NN) and linear discriminant analysis were applied in the study. It was found that K-nearest neighbour (k-NN) outperforms the linear discriminant analysis model in terms of accuracy. In addition, grades obtained in mathematics (at ordinary level examinations) had a significant impact on the academic success of undergraduate architecture students. This paper makes a modest contribution to the ongoing discussion on the relationship between prior academic performance and academic success of undergraduate students by evaluating this proposition. One of the issues that emerges from these findings is that prior academic performance can be used as a predictor of academic success in undergraduate architecture programmes. Overall, the developed k-NN model can serve as a valuable tool during the process of selecting new intakes into undergraduate architecture programmes in Nigeria.

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The Graduate Record Examination and Success in an Engineering Management Program: A Case Study

Cited by 10 publications

References 14 publications

Towards reliable prediction of academic performance of architecture students using data mining techniques

Towards reliable prediction of academic performance of architecture students using data mining techniques

The Validity of Scores from the GRE^® revised General Test for Forecasting Performance in Business Schools: Phase One

Predicting the academic success of architecture students by pre-enrolment requirement: using machine-learning techniques

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The Graduate Record Examination and Success in an Engineering Management Program: A Case Study

Cited by 10 publications

References 14 publications

Towards reliable prediction of academic performance of architecture students using data mining techniques

Towards reliable prediction of academic performance of architecture students using data mining techniques

The Validity of Scores from the GRE® revised General Test for Forecasting Performance in Business Schools: Phase One

Predicting the academic success of architecture students by pre-enrolment requirement: using machine-learning techniques

Contact Info

Product

Resources

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The Validity of Scores from the GRE^® revised General Test for Forecasting Performance in Business Schools: Phase One