Study - Investigating TPACK from a Subject-Specific Angle Using Test-Based Instruments

This project is a study which was part of my dissertation. It adresses the question of how Technological Pedaogigical Content Knowledge (i.e., TPCK) is related to Pedagogical Content Knowledge (i.e., PCK) and TK. Moreover, it investigates possible influencing variables on the relationship between different assessment strategies of TPCK. This page provides supplementary material for https://doi.org/10.1016/j.compedu.2024.10504

Development of public expenditures in light of the progress of political cycles in Czech Republic between years 2000 and 2015

The thesis is concerned with the analysis of the development of public expenditures in Czech Republic between the years 2000 and 2015 with regard to the progress of political cycles. The analysis of overall development of each budget chapter (especially the year on year comparison) is based on real outcomes of state finances collected from final state accounts. The purpose of the thesis is to discover potential influence of political cycles on the level and structure of public expenses. The author created his own theoretical assumptions and set an original methodological approach. Final findings mostly comply with the created hypothesis

Machine learning using the extreme gradient boosting (XGBoost) algorithm predicts 5-day delta of SOFA score at ICU admission in COVID-19 patients

Background: Accurate risk stratification of critically ill patients with coronavirus disease 2019 (COVID-19) is essential for optimizing resource allocation, delivering targeted interventions, and maximizing patient survival probability. Machine learning (ML) techniques are attracting increased interest for the development of prediction models as they excel in the analysis of complex signals in data-rich environments such as critical care. Methods: We retrieved data on patients with COVID-19 admitted to an intensive care unit (ICU) between March and October 2020 from the RIsk Stratification in COVID-19 patients in the Intensive Care Unit (RISC-19-ICU) registry. We applied the Extreme Gradient Boosting (XGBoost) algorithm to the data to predict as a binary out- come the increase or decrease in patients’ Sequential Organ Failure Assessment (SOFA) score on day 5 after ICU admission. The model was iteratively cross-validated in different subsets of the study cohort. Results: The final study population consisted of 675 patients. The XGBoost model correctly predicted a decrease in SOFA score in 320/385 (83%) critically ill COVID-19 patients, and an increase in the score in 210/290 (72%) patients. The area under the mean receiver operating characteristic curve for XGBoost was significantly higher than that for the logistic regression model (0.86 vs . 0.69, P < 0.01 [paired t -test with 95% confidence interval]). Conclusions: The XGBoost model predicted the change in SOFA score in critically ill COVID-19 patients admitted to the ICU and can guide clinical decision support systems (CDSSs) aimed at optimizing available resources