Student Experiences of High-stakes Testing for Progression in One Undergraduate Nursing Program

Understanding underperformance in a high-stakes clinical-based simulation assessment in physiotherapy: a descriptive analysis

BACKGROUND

High-stakes assessments are often used as a 'gate-keeper' activity for entry into the health professions by ensuring that the minimum core competency thresholds of the profession are met. The aim of the study was to explore if common areas of underperformance existed in international candidates assessed with a high-stakes clinical-based simulation assessment for entry into the physiotherapy profession in Australia.

METHODS

A retrospective mixed methods analysis of the clinical assessments completed by international candidates over a one-month period in 2021 that were deemed as not meeting competency. The clinical assessments were completed in one of the three practice areas: cardiorespiratory, musculoskeletal, or neurological rehabilitation. Each assessment was scored by two independent assessors, who discussed the performance and then completed a moderated assessment form. The assessment form used to score competency included seven domains such as initial assessment, effective treatment, communication skills, and risk management.

RESULTS

Fifty-one clinical assessments graded as not competent were analysed. Across the practice areas, a high failure rate was found in domains related to interpreting assessment findings and developing a treatment plan. This trend was also observed in the qualitative data, suggesting candidates struggled to meet competency in areas of planning and prioritisation, interpretation and implementation of the information gathered, and selection and evaluation of effective treatment.

CONCLUSION

These findings align with published data on the underperformance of Australian physiotherapy students in clinical placement settings, suggesting these issues are not specific to high stakes assessment of overseas physiotherapists, and that education needs to focus on improving these skills within the profession at all levels. With the identified areas of underperformance aligning with the ability to use higher order thinking and skills integral to clinical reasoning, improvements in the education and implementation of clinical reasoning may be a place to start.

Prediction of medical sciences students' performance on high-stakes examinations using machine learning models: a protocol for a systematic review

INTRODUCTION

Predicting medical science students' performance on high-stakes examinations has received considerable attention. Machine learning (ML) models are well-known approaches to enhance the accuracy of determining the students' performance. Accordingly, we aim to provide a comprehensive framework and systematic review protocol for applying ML in predicting medical science students' performance on high-stakes examinations. Improving the current understanding of the input and output features, preprocessing methods, setting of ML models and required evaluation metrics seems essential.

METHODS AND ANALYSIS

A systematic review will be conducted by searching the electronic bibliographic databases of MEDLINE/PubMed, EMBASE, SCOPUS and Web of Science. The search will be limited to studies published from January 2013 to June 2023. Studies explicitly predicting student performance in high-stakes examinations and referencing their learning outcomes and use of ML models will be included. Two team members will first screen literature meeting the inclusion criteria at the title, abstract and full-text levels. Second, the Best Evidence Medical Education quality framework rates the included literature. Later, two team members will extract data, including the studies' general data and the ML approach's details. Finally, the information consensus will be reached and submitted for analysis. The synthesised evidence from this review provides helpful information for medical education policy-makers, stakeholders and other researchers in adopting the ML models to evaluate medical science students' performance in high-stakes exams.

ETHICS AND DISSEMINATION

This systematic review protocol summarises findings of existing publications rather than primary data and does not require an ethics review. The results will be disseminated in publications of peer-reviewed journals.