Improving students’ higher order thinking skills in learning health systems using mobile-based instructional approach

Aims Learning health systems are healthcare systems in which awareness formation processes are inserted into daily practice to provide constant development in care. Many students have difficulty completing the health systems courses due to a lack of Higher-Order Thinking Skills (HOTS). Therefore, this study aims to improve students’ HOTS using a mobile-based instructional approach. Materials & Methods The enhanced HOTS is measured using indicators of critical and creative thinking processes known as Bloom’s taxonomy concept. Furthermore, this is experimental research with a pre-test-post-test random control group pattern and ADDIE technique to develop the mobile-based instructional Approach. The study involved 120 students who were evenly divided into the experimental class and the control class. Respondents were selected from 650 SMK students in Central Java using the random cluster sampling method. Findings Based on the results, this is evidenced by the ability to answer challenging questions associated with critical and creative reasons. Conclusion Therefore, using a mobile-based instructional Approach supports independent learning

Numerical Computation, Data Analysis and Software in Mathematics and Engineering

The present book contains 14 articles that were accepted for publication in the Special Issue “Numerical Computation, Data Analysis and Software in Mathematics and Engineering” of the MDPI journal Mathematics. The topics of these articles include the aspects of the meshless method, numerical simulation, mathematical models, deep learning and data analysis. Meshless methods, such as the improved element-free Galerkin method, the dimension-splitting, interpolating, moving, least-squares method, the dimension-splitting, generalized, interpolating, element-free Galerkin method and the improved interpolating, complex variable, element-free Galerkin method, are presented. Some complicated problems, such as tge cold roll-forming process, ceramsite compound insulation block, crack propagation and heavy-haul railway tunnel with defects, are numerically analyzed. Mathematical models, such as the lattice hydrodynamic model, extended car-following model and smart helmet-based PLS-BPNN error compensation model, are proposed. The use of the deep learning approach to predict the mechanical properties of single-network hydrogel is presented, and data analysis for land leasing is discussed. This book will be interesting and useful for those working in the meshless method, numerical simulation, mathematical model, deep learning and data analysis fields

Daten- und termingesteuerte Entscheidungsmethodik der Fabrikplanung unter Berücksichtigung der Produktentstehung

Die Fabrikplanung ist heute mit diversen Trends und Herausforderungen konfrontiert, wie beispielsweise der Planung nachhaltiger Fabriken oder der Integration von Produkt und Fabrik. Hierzu leistet die vorliegende Arbeit einen Beitrag, virtuelle Methoden termingesteuert in einen Planungsprozess zu implementieren. Anhand einer generischen Vorgehensweise wird die Integration des Planungsprozesses in die Produktentstehung definiert, was durch zwei Industriebeispiele validiert wird