A note on the penalty parameter in Nitsche's method for unfitted boundary value problems

Nitsche's method is a popular approach to implement Dirichlet-type boundary conditions in situations where a strong imposition is either inconvenient or simply not feasible. The method is widely applied in the context of unfitted finite element methods. From the classical (symmetric) Nitsche's method it is well-known that the stabilization parameter in the method has to be chosen sufficiently large to obtain unique solvability of discrete systems. In this short note we discuss an often used strategy to set the stabilization parameter and describe a possible problem that can arise from this. We show that in specific situations error bounds can deteriorate and give examples of computations where Nitsche's method yields large and even diverging discretization errors

Effects of Early Warning Emails on Student Performance

We use learning data of an e-assessment platform for an introductory mathematical statistics course to predict the probability of passing the final exam for each student. Subsequently, we send warning emails to students with a low predicted probability to pass the exam. We detect a positive but imprecisely estimated effect of this treatment, suggesting the effectiveness of such interventions only when administered more intensively.Comment: arXiv admin note: text overlap with arXiv:1906.0986

Effects of state-wide implementation of the Los Angeles Motor Scale for triage of stroke patients in clinical practice

Background: The prehospital identification of stroke patients with large-vessel occlusion (LVO), that should be immediately transported to a thrombectomy capable centre is an unsolved problem. Our aim was to determine whether implementation of a state-wide standard operating procedure (SOP) using the Los Angeles Motor Scale (LAMS) is feasible and enables correct triage of stroke patients to hospitals offering (comprehensive stroke centres, CSCs) or not offering (primary stroke centres, PSCs) thrombectomy.Methods: Prospective study involving all patients with suspected acute stroke treated in a 4-month period in a state-wide network of all stroke-treating hospitals (eight PSCs and two CSCs). Primary endpoint was accuracy of the triage SOP in correctly transferring patients to CSCs or PSCs. Additional endpoints included the number of secondary transfers, the accuracy of the LAMS for detection of LVO, apart from stroke management metrics.Results: In 1123 patients, use of a triage SOP based on the LAMS allowed triage decisions according to LVO status with a sensitivity of 69.2% (95% confidence interval (95%-CI): 59.0-79.5%) and a specificity of 84.9% (95%-CI: 82.6-87.3%). This was more favourable than the conventional approach of transferring every patient to the nearest stroke-treating hospital, as determined by geocoding for each patient (sensitivity, 17.9% (95%-CI: 9.4-26.5%); specificity, 100% (95%-CI: 100-100%)). Secondary transfers were required for 14 of the 78 (17.9%) LVO patients. Regarding the score itself, LAMS detected LVO with a sensitivity of 67.5% (95%-CI: 57.1-78.0%) and a specificity of 83.5% (95%-CI: 81.0-86.0%).Conclusions: State-wide implementation of a triage SOP requesting use of the LAMS tool is feasible and improves triage decision-making in acute stroke regarding the most appropriate target hospital.</p

E-Assessment Using Variable-Content Exercises in Mathematical Statistics

Computer-assisted assessment (CAA) is widely used in modern university courses in many different fields. It can be used both in formative and summative assessments and with different emphasis on (self-)training, grading, and feedback generation. This article reports on experiences from using the CAA tool “JACK” to support a university lecture on mathematical statistics for exercises, tests, and exams. We show and discuss, among others, a positive relationship between usage intensity of JACK and final grades. Moreover, students generally report to invest more time into studying when offered a CAA, and to be satisfied with such a setup

When is the Best Time to Learn? – Evidence from an Introductory Statistics Course

We analyze learning data of an e-assessment platform for an introductory mathematical statistics course, more specifically the time of the day when students learn and the time they spend with exercises. We propose statistical models to predict students’ success and to describe their behavior with a special focus on the following aspects. First, we find that learning during daytime and not at nighttime is a relevant variable for predicting success in final exams. Second, we observe that good and very good students tend to learn in the afternoon, while some students who failed our course were more likely to study at night but not successfully so. Third, we discuss the average time spent on exercises. Regarding this, students who participated in an exam spent more time doing exercises than students who dropped the course before