ABSTRACT
Machine learning (ML) provides a powerful framework for the analysis of high-dimensional datasets by modelling complex relationships, often encountered in modern data with many variables, cases and potentially non-linear effects. The impact of ML methods on research and practical applications in the educational sciences is still limited, but continuously grows, as larger and more complex datasets become available through massive open online courses (MOOCs) and large-scale investigations. The educational sciences are at a crucial pivot point, because of the anticipated impact ML methods hold for the field. To provide educational researchers with an elaborate introduction to the topic, we provide an instructional summary of the opportunities and challenges of ML for the educational sciences, show how a look at related disciplines can help learning from their experiences, and argue for a philosophical shift in model evaluation. We demonstrate how the overall quality of data analysis in educational research can benefit from these methods and show how ML can play a decisive role in the validation of empirical models. Specifically, we (1) provide an overview of the types of data suitable for ML and (2) give practical advice for the application of ML methods. In each section, we provide analytical examples and reproducible R code. Also, we provide an extensive Appendix on ML-based applications for education. This instructional summary will help educational scientists and practitioners to prepare for the promises and threats that come with the shift towards digitisation and large-scale assessment in education. Context and implications Rationale for this study In 2020, the worldwide SARS-COV-2 pandemic forced the educational sciences to perform a rapid paradigm shift with classrooms going online around the world—a hardly novel but now strongly catalysed development. In the context of data-driven education, this paper demonstrates that the widespread adoption of machine learning techniques is central for the educational sciences and shows how these methods will become crucial tools in the collection and analysis of data and in concrete educational applications. Helping to leverage the opportunities and to avoid the common pitfalls of machine learning, this paper provides educators with the theoretical, conceptual and practical essentials. Why the new findings matter The process of teaching and learning is complex, multifaceted and dynamic. This paper contributes a seminal resource to highlight the digitisation of the educational sciences by demonstrating how new machine learning methods can be effectively and reliably used in research, education and practical application. Implications for educational researchers and policy makers The progressing digitisation of societies around the globe and the impact of the SARS-COV-2 pandemic have highlighted the vulnerabilities and shortcomings of educational systems. These developments have shown the necessity to provide effective educational processes that can support sometimes overwhelmed teachers to digitally impart knowledge on the plan of many governments and policy makers. Educational scientists, corporate partners and stakeholders can make use of machine learning techniques to develop advanced, scalable educational processes that account for individual needs of learners and that can complement and support existing learning infrastructure. The proper use of machine learning methods can contribute essential applications to the educational sciences, such as (semi-)automated assessments, algorithmic-grading, personalised feedback and adaptive learning approaches. However, these promises are strongly tied to an at least basic understanding of the concepts of machine learning and a degree of data literacy, which has to become the standard in education and the educational sciences. Demonstrating both the promises and the challenges that are inherent to the collection and the analysis of large educational data with machine learning, this paper covers the essential topics that their app ic tion requires and provides easy-to-follow resources and code to facilitate the process of adoption. © 2021 The Authors. Review of Education published by John Wiley & Sons Ltd on behalf of British Educational Research Association.
ABSTRACT
Purpose: Monoclonal antibody (mAB) infusion (bamlanivimab or casirivimab/ imdevimab) for symptomatic, non-hypoxemic, high-risk outpatients with COVID-19 infection, is an available early intervention for COVID-19+ SOT recipients. We aimed to assess efficiency in time from diagnosis to treatment, and outcomes in a retrospective cohort of SOT recipients with COVID-19 who received mAB. Methods: We developed a Nurse Coordinator-led initiative to screen, refer, and facilitate mAB infusion for COVID-19+ SOT recipients within 10 days of symptom onset. SOT recipients received electronic messaging to promptly report potential COVID-19 symptoms to the transplant team. Data were collected on time from symptom onset to diagnosis, mAB infusion, and follow-up > 21 days, and hospital admissions, disease severity, mortality, and rejection. Results: 34 out of 36 referred SOT recipients with symptomatic COVID-19 disease without hypoxia received mAB therapy (3 heart, 8 lung, 16 kidney, 2 Liver-Kidney, 2 Pancreas-Kidney, 3 Kidney-Heart). Median time from symptom onset to diagnosis was 2 days and from date of diagnosis to mAB infusion was 4 days. Of those 34, 88% did not require hospitalization and recovered uneventfully. 12% required hospitalization for COVID disease progression, two on the same day as mAB infusion, and the other 2, more than 26 days post infusion. Of these, 2 patients had mild-moderate hypoxia, and 2 had critical disease. Only 1 patient died from COVID-19 complications and no episodes of rejection or graft loss were observed. Conclusions: The Nurse Coordinator-led initiative efficiently facilitated mAB therapy for COVID-19+ SOT recipients and was associated with excellent outcomes. Compared to prior published COVID-19 outcomes in SOT recipients, patients who received mAB may have reduce hospitalization and low mortality. As mAB therapy may be underutilized in the general population, these results support efforts to educate transplant centers to implement efficient interventions for the screening and referral of COVID+ SOT recipients for mAB therapy.