Who Succeeds at University? Factors predicting academic performance in first year Australian university students

McKenzie, Kirsten; Schweitzer, Robert

doi:10.1080/07924360120043621

Cited by 503 publications

(392 citation statements)

References 15 publications

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“…Comparable to previous studies about personal reasons leading to student attrition (Baik et al, 2017;McKenzie & Schweitzer, 2001), this study found that physical, mental and affective issues influenced decisions to withdraw from STEPS. Thus, it would seem imperative that a sustained focus be placed on greater promotion of the university's support services, complemented by the appointment of a dedicated counsellor with the workload flexibility and capacity to provide timely pastoral care to students who have indicated the need for additional support.…”

Section: Recommendationsmentioning

confidence: 56%

“…The relationship between health and attrition has been reported on in previous studies of undergraduate student attrition (McKenzie & Schweitzer, 2001) where it was found that undergraduate students experiencing more depressive episodes had higher withdrawal rates than students experiencing fewer depressive episodes. Baik et al, (2017) found emotional health to be the second most commonly reported reason for first year attrition amongst students with low ATAR scores.…”

Section: Discussionmentioning

confidence: 89%

“…Nelson, Duncan and Clarke (2009) suggest there is no single factor identified to explain student withdrawal prior to program completion, but rather, there are multiple factors responsible for attrition. The literature cites such factors as financial hardship; feelings of isolation and nonacceptance at the institution; competing family responsibilities and extra-curricular commitments; health issues; unmet personal expectations; poor attendance patterns; gaining employment whilst studying; poor academic results; and, inadequate academic induction, social orientation and lack of quality instruction (Bennett, Kottasz & Nocciolino 2007;Hinton 2007;Kift & Nelson, 2005;McKenzie & Schweitzer, 2001;Pascerella & Terenzini 2005;Tinto 1993;Trotter & Roberts, 2006). Due caution must be exercised, however because of the broad range of higher education systems worldwide, the flexibility, degree of difficulty of programs, and the variance in admission strategies and assessment regimes that each adopt (Department of Education and Training, 2017a).…”

Section: Introductionmentioning

confidence: 99%

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“Why did we lose them and what could we have done”?

Willans

Seary

2018

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Section: Recommendationsmentioning

confidence: 56%

Section: Discussionmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

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“Why did we lose them and what could we have done”?

Willans

Seary

2018

SUCCESS

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“…Educational research has shown that prior achievement is the best predictor of future achievement (Goberna, López, & Pastor, 1987;House, Hurst, & Keely, 1996;Mathiasen, 1984;McKenzie & Schweitzer, 2001;Wilson & Hardgrave, 1995;Zeegers, 2004).…”

Section: Palabras Clave: Regresión Logística Versus Regresión Linealmentioning

confidence: 99%

“…Using achievement in middle school and/or scores in standardized university entrance tests as the predictor variables, linear models explain less than 20% percent of the variance in some cases (García et al, 2000), rising to moderate, around 40%, in other cases (McKenzie & Schweitzer, 2001;Pike & Saupe, 2002). The inclusion of variables from the educational institution (institution effect), as well as the use of hierarchical linear models, increases the variance accounted for but not beyond 6-7% (Pike & Saupe).…”

Section: Linear Models Versus Logistic Modelsmentioning

confidence: 99%

Prediction of University Students' Academic Achievement by Linear and Logistic Models

Ayán

García

2008

Span. J. Psychol.

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University students' academic achievement measured by means of academic progress is modeled through linear and logistic regression, employing prior achievement and demographic factors as predictors. The main aim of the present paper is to compare results yielded by both statistical procedures, in order to identify the most suitable approach in terms of goodness of fit and predictive power. Grades awarded in basic scientific courses and demographic variables were entered into the models at the first step. Two hypotheses are proposed: (a) Grades in basic courses as well as demographic factors are directly related to academic progress, and (b) Logistic regression is more appropriate than linear regression due to its higher predictive power. Results partially confirm the first prediction, as grades are positively related to progress. However, not all demographic factors considered proved to be good predictors. With regard to the second hypothesis, logistic regression was shown to be a better approach than linear regression, yielding more stable estimates with regard to the presence of ill-fitting patterns. Keywords: logistic versus linear regression, prediction, credits, academic achievement, advance in careerSe estudia el efecto de dos tipos de factores sobre el rendimiento de estudiantes universitarios: variables académicas de rendimiento previo y variables demográficas, mediante modelos lineales y logísticos. El principal objetivo del trabajo es comparar los resultados obtenidos con ambas técnicas estadísticas, para determinar cuál de ellos es más adecuado en términos de ajuste y capacidad predictiva cuando se pretende explicar y predecir el rendimiento académico, en función de variables de rendimiento previo y factores sociodemográficos. Como medida del rendimiento a predecir se empleó el avance en la carrera. Las hipótesis planteadas son: 1) El avance está directamente relacionado con las calificaciones en materias básicas de primer año y con variables demográficas y 2) Los modelos logísticos son más adecuados que los modelos lineales, ya que presentan mayor capacidad predictiva. Los resultados permiten confirmar la primera hipótesis en su primera parte, ya que el rendimiento previo está directa y significativamente asociado al avance en la carrera. Pero se cumple de forma parcial por lo que se refiere al efecto factores demográficos. Con respecto a la segunda hipótesis, la regresión logística mostró ser más adecuada que la lineal, pues arroja estimaciones más estables en relación con la presencia de patrones de mal ajuste. Palabras clave: regresión logística versus regresión lineal, predicción, créditos, rendimiento académico, avance en la carrera

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A new academic performance indicator for the first term of first‐year science degrees students at La Laguna University: a predictive model

et al. 2019

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Academic performance during the first year of university is correlated with future academic success, and is considered to be a determining factor in the reduction of dropouts. In the present study, we describe a new academic performance indicator for the first term of first‐year science degrees students at La Laguna University in Spain. We are interested in identifying the most important previous academic factors for predicting the success of first‐year students at university. Specifically, multiple linear regression models were used to identify such predictors of academic success. We report that, for all of the analyzed science degrees, the best predictor of academic success is high school grade point average. In addition, we obtained predictive models for estimating the value of the new academic performance indicator. Using these models, we can predict future academic success, which may help identify students at risk of failure at the beginning of the course. This in turn would ensure early implementation of educational interventions or strategies to increase academic achievement of such students.

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Who Succeeds at University? Factors predicting academic performance in first year Australian university students

Cited by 503 publications

References 15 publications

“Why did we lose them and what could we have done”?

“Why did we lose them and what could we have done”?

Prediction of University Students' Academic Achievement by Linear and Logistic Models

A new academic performance indicator for the first term of first‐year science degrees students at La Laguna University: a predictive model

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