利用预测分析技术在巴基斯坦开展为期一年的急诊医学认证项目,为高风险学习者提供支持。

IF 3.3 2区 教育学 Q1 EDUCATION, SCIENTIFIC DISCIPLINES
Saima Ali, Syed Ghazanfar Saleem, Priya Arumuganathan, Sama Mukhtar, Adeel Khatri, Megan Rybarczyk
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引用次数: 0

摘要

导读:预测分析和机器学习(PAML)在卫生专业教育(HPE)中越来越受欢迎。它们的使用包括但不限于指导学生入学,识别有风险的学习者,加强教育决策,并通过数据驱动的见解分配适当的资源。本研究探索了PAML在巴基斯坦印度河医院和卫生网络(IHHN)为期一年的急诊医学认证项目(CPEM)中识别高危学习者的使用,目的是为改善结果提供有针对性的教育支持。方法:通过利用先前CPEM队列(2018-2022,n = 91)的数据,比较回归树和线性回归机器学习模型来预测CPEM 2023学习者队列(n = 26)的期末考试表现。这些模型被前瞻性地用于识别有风险的学习者(n = 14/26)。额外的学习支持(ELS)作为一种包容性的措施提供给每个人,而不仅仅是那些被模型标记并被十个学习者接受的人。分析了模型的准确性和教育干预的影响。结果:两种模型均具有较高的准确率(回归树:受试者工作特征曲线下面积(Area Under Receiver Operating Characteristic Curve, AUC)= 0.89;线性回归:AUC= 0.88),但回归树模型的敏感性和特异性略好。这些模型总共预测了14名计划参加2023年期末考试的学生的表现不理想。经过有针对性的干预,8名学习者的最终成绩有所提高。回归树模型预测效果较好;然而,这两种模式都有其局限性。结论:本研究证明了使用PAML识别风险学习者和定制支持策略以提高低资源环境下教育成果的可行性和实用性。这种额外的支持可以增强专家的判断,并确保公平的教育实践。然而,在高风险评估中,必须积极解决模型限制和伦理问题,如算法偏差、过度拟合和数据失衡。[方框:见文本]。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Harnessing predictive analytics to support high-risk learners in a one-year certification program in emergency medicine (CPEM) in Pakistan.

Introduction: Predictive analytics and Machine Learning (PAML) are gaining traction in health professions education (HPE). Their utilization includes, but is not limited to, guiding student enrollment, identifying at-risk learners, enhancing educational decisions, and allocating proper resources through data-driven insights. This study explored the use of PAML to identify at-risk learners in a one-year Certification Program in Emergency Medicine (CPEM) at the Indus Hospital and Health Network (IHHN), Pakistan with the aim of providing targeted educational support for improved outcome.

Methodology: By leveraging data from prior CPEM cohorts (2018-2022, n = 91), regression tree and linear regression machine learning models were compared to predict the final examination performance of the CPEM 2023 learner cohort (n = 26). The models were prospectively applied to identify at-risk learners (n = 14/26). Extra learning support (ELS) was offered as an inclusive measure to everyone, not just the ones flagged by the models and was accepted by ten learners. Data were analyzed for model accuracy and the impact of the educational intervention.

Results: Both models showed high accuracy (regression tree: Area Under the Receiver Operating Characteristic (ROC) Curve (AUC)= 0.89; linear regression: AUC= 0.88), though the regression tree model demonstrated slightly better sensitivity and specificity. The models altogether predicted unsatisfactory performance for 14 learners scheduled to sit for the 2023 final examination. Following targeted intervention, eight learners showed improvement in their final scores. Regression tree model was comparatively better in making predictions; however, both models had their limitation.

Conclusion: The study demonstrated the feasibility and utility of using PAML to identify at-risk learners and tailor support strategies for enhancing educational outcome in low-resource settings. This additional support can augment expert judgement and ensure equitable educational practices. However, model limitations and ethical concerns, such as algorithmic bias, overfitting, and data imbalance, must be actively addressed in high-stakes assessments.[Box: see text].

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来源期刊
Medical Teacher
Medical Teacher 医学-卫生保健
CiteScore
7.80
自引率
8.50%
发文量
396
审稿时长
3-6 weeks
期刊介绍: Medical Teacher provides accounts of new teaching methods, guidance on structuring courses and assessing achievement, and serves as a forum for communication between medical teachers and those involved in general education. In particular, the journal recognizes the problems teachers have in keeping up-to-date with the developments in educational methods that lead to more effective teaching and learning at a time when the content of the curriculum—from medical procedures to policy changes in health care provision—is also changing. The journal features reports of innovation and research in medical education, case studies, survey articles, practical guidelines, reviews of current literature and book reviews. All articles are peer reviewed.
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