Rethinking the admission criteria to nursing school

Shulruf, Boaz; Wang, Grace; Zhao, Yipin; Baker, Heather

doi:10.1016/j.nedt.2010.11.024

Cited by 43 publications

(55 citation statements)

References 24 publications

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“…Signal detection theory is applicable wherever criteria are applied in decision-making regardless of the specific context and goals, for example, in validating criteria of multiple mini interviews (Patterson et al 2016). Following up on Shulruf, Hattie, and Tumen (2008), Shulruf et al (2011) andSteenman, Bakker, andvan Tartwijk (2014), future application of signal detection theory might be informative on comparisons of selection outcomes given different operationalisations of SSGPA and academic achievement. The relatively small proportion of unsuccessful students does mean that all interpretations are preliminary and awaiting replication with a larger sample.…”

Section: Implementation Of Signal Detection Theorymentioning

confidence: 99%

“…Given that it is often considered the best available predictor of future academic achievement, this is logical (Richardson, Abraham, and Bond 2012), yet 'the best available' in university admissions does not equal a prediction of academic achievement that can be called accurate (Sternberg et al 2012;Sawyer 2013). Although Shulruf, Hattie, and Tumen (2008) (see also Shulruf et al 2011) found correlations >0.60 of SSGPA with university first-year GPA using a specific and new calculation of SSGPA, it is more commonly reported as only a reasonable predictor of university GPA, with r ≈ 0.40 (Richardson, Abraham, and Bond 2012;Edwards, Friedman, and Pearce 2013). Although their predictive power in most studies is arguably much smaller than desired, past academic achievement (de Koning et al 2012;van der Heijden, Hessen, and Wubbels 2012), cognitive admission tests (visser et al 2012) and non-cognitive measures (Huang 2012;Richardson, Abraham, and Bond 2012;Sternberg et al 2012) have all been shown to be valid predictors of academic achievement to a certain extent (Robbins et al 2004;O'Neill et al 2011;Sinha et al 2011;Poole et al 2012;Schmitt 2012;Stemler 2012;Kreiter and Axelson 2013;Preiss et al 2013;Sawyer 2013;Christersson et al 2014;Sartania et al 2014;Simpson et al 2014;Gaertner and Larsen McClarty 2015).…”

Section: Introductionmentioning

confidence: 99%

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Signal detection theory as a tool for successful student selection

Linden

Smagt

Woertman

et al. 2016

Assessment & Evaluation in Higher Education

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Prediction accuracy of academic achievement for admission purposes requires adequate sensitivity and specificity of admission tools, yet the available information on the validity and predictive power of admission tools is largely based on studies using correlational and regression statistics. The goal of this study was to explore signal detection theory as a tool to extend the available information; signal detection theory allows for comparisons of selection outcomes on both group and individual levels and the development of tailor-made criteria for specific programmes and admission goals. We investigated who would or would not have been admitted applying specific criteria for each of three common admission tools, how many admitted students would fail and how many applicants who would have been successful would be rejected. Both comparisons at an individual level and the receiver operating characteristic curves at a group level revealed that scores obtained in a programme-specific matching programme and non-cognitive factors appear more valuable than regression statistics suggest when it comes to admitting applicants who will become successful students. Signal detection theory allows not only for admission-goal-specific and programme-specific fine-tuning of the content of admission tools, it also informs about the effects of criteria and thus allows the setting of criteria.

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Section: Implementation Of Signal Detection Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Signal detection theory as a tool for successful student selection

Linden

Smagt

Woertman

et al. 2016

Assessment & Evaluation in Higher Education

View full text Add to dashboard Cite

show abstract

“…Nesta relação, verifica-se a nota final do ensino pré-universitário (Shulruf et al, 2011), a prática de exercícios físicos (Bellar et al, 2014) e a qualidade do sono (Ferreira, Martino, 2012).…”

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“…Ainda no contexto da enfermagem e do desempenho acadêmico, em pesquisa realizada com 134 estudantes de graduação na Austrália, foi identificado que quanto melhor a nota alcançada no ensino pré-universitário, melhor é o desempenho do estudante no curso de enfermagem, sendo considerada a nota no ensino pré-universitário preditora para o melhor desempenho acadêmico (Shulruf et al, 2011).…”

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“…As evidências dos estudos apresentados anteriormente mostram que diversos fatores têm sido investigados e associados ao desempenho acadêmico, sobretudo em relação às características sociodemográficas dos estudantes (idade, sexo, minoria étnica) (Timer, Clauson, 2011), às acadêmicas e pré-universitárias (nota geral do semestre e nota no ensino pré-universitário) (Shulruf et al, 2011), às do ambiente institucional (relação professor-aluno) (Al-Hussami et al, 2011) e às psicoemocionais (ansiedade, apoio social, fatores de estresse, sintomas depressivos, resiliência) (Pitt et al, 2012, Elias, Ping, Abudallah, 2011Xu et al, 2014;Pitt et al, 2014 A Universidade, como um contexto facilitador do desenvolvimento dos estudantes e suporte no percurso durante o ensino superior, aponta em seu relatório de autoavaliação que vem desenvolvendo estratégias para melhoria do desempenho e garantia de sucesso do estudante (UFAM, 2012(UFAM, , 2013. Todavia, as estratégias adotadas pela universidade se dirigem para melhoria dos fatores socioeconômicos, como moradia, bolsas de incentivo à permanência e atividades acadêmicas (monitoria, pesquisa e extensão), e são dispensadas poucas estratégias que fortaleçam os aspectos psicoemocionais do estudante (UFAM, 2012(UFAM, , 2013.…”

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Fatores de estresse, sintomas depressivos, suporte social e resiliência associados ao desempenho acadêmico em estudantes de enfermagem

Fonseca¹

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Second‐Year Nursing Students’ Retention of Gross Anatomical Knowledge

Narnaware

Neumeier

2019

Anatomical Sciences Ed

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Human anatomy is a foundational course in nursing education, however, there is growing concern that students do not retain enough anatomical knowledge to successfully apply it in clinical settings. The aim of this study was to determine retention level of anatomy knowledge among second‐year nursing students from their first‐year anatomy class, and to determine if there is a difference in level of retention based on organ system. For each system, second‐year students were asked to answer 9 to 11 multiple‐choice questions (MCQs), and the scores from these quizzes were compared to matched test items from their first‐year anatomy examinations. There was a significant decrease in the overall mean score from 83.05 ± 8.34 (±SD) in first year to 54.36 ±12.9 in second year (P = 0.0001). Retention levels were system specific. System‐specific knowledge retention was highest for the gastrointestinal system (89.7%), respiratory system (88.5%), and genitourinary system (83.6%). This was followed by the integumentary system (80.1%), special senses (79.4%), nervous system (74.9%), and musculoskeletal system (69.3%). Retention was lowest for the lymphatic system (64.3%), cranial nerves (58.8%), vascular system (53.9%), and head and neck (42.6%). The present study shows that nursing students’ anatomy knowledge retention was comparatively higher than rates reported by others in medical and allied‐health students. The researchers are now investigating knowledge retention in third‐ and fourth‐year nursing students. Further investigation into why retention is higher for specific systems and intervention strategies to improve knowledge acquisition and retention in nursing students is recommended.

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Rethinking the admission criteria to nursing school

Cited by 43 publications

References 24 publications

Signal detection theory as a tool for successful student selection

Signal detection theory as a tool for successful student selection

Fatores de estresse, sintomas depressivos, suporte social e resiliência associados ao desempenho acadêmico em estudantes de enfermagem

Second‐Year Nursing Students’ Retention of Gross Anatomical Knowledge

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