Dataset Information

A Systematic Approach to Teaching Case Studies and Solving Novel Problems.

ABSTRACT: Both research and practical experience in education support the use of case studies in the classroom to engage students and develop critical thinking skills. In particular, working through case studies in scientific disciplines encourages students to incorporate knowledge from a variety of backgrounds and apply a breadth of information. While it is recognized that critical thinking is important for student success in professional school and future careers, a specific strategy to tackle a novel problem is lacking in student training. We have developed a four-step systematic approach to solving case studies that improves student confidence and provides them with a definitive road map that is useful when solving any novel problem, both in and out of the classroom. This approach encourages students to define unfamiliar terms, create a timeline, describe the systems involved, and identify any unique features. This method allows students to solve complex problems by organizing and applying information in a logical progression. We have incorporated case studies in anatomy and neuroanatomy courses and are confident that this systematic approach will translate well to courses in various scientific disciplines.

SUBMITTER: Meyer CA

PROVIDER: S-EPMC6203626 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

A Systematic Approach to Teaching Case Studies and Solving Novel Problems.

Meyer Carolyn A CA Hall Heather H Heise Natascha N Kaminski Karen K Ivie Kenneth R KR Clapp Tod R TR

Journal of microbiology & biology education 20181031 3

Both research and practical experience in education support the use of case studies in the classroom to engage students and develop critical thinking skills. In particular, working through case studies in scientific disciplines encourages students to incorporate knowledge from a variety of backgrounds and apply a breadth of information. While it is recognized that critical thinking is important for student success in professional school and future careers, a specific strategy to tackle a novel p ...[more]

PMID: 30377470

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Project description:This research paper develops a novel hybrid approach, called hybrid particle swarm optimization-teaching-learning-based optimization (hPSO-TLBO), by combining two metaheuristic algorithms to solve optimization problems. The main idea in hPSO-TLBO design is to integrate the exploitation ability of PSO with the exploration ability of TLBO. The meaning of "exploitation capabilities of PSO" is the ability of PSO to manage local search with the aim of obtaining possible better solutions near the obtained solutions and promising areas of the problem-solving space. Also, "exploration abilities of TLBO" means the ability of TLBO to manage the global search with the aim of preventing the algorithm from getting stuck in inappropriate local optima. hPSO-TLBO design methodology is such that in the first step, the teacher phase in TLBO is combined with the speed equation in PSO. Then, in the second step, the learning phase of TLBO is improved based on each student learning from a selected better student that has a better value for the objective function against the corresponding student. The algorithm is presented in detail, accompanied by a comprehensive mathematical model. A group of benchmarks is used to evaluate the effectiveness of hPSO-TLBO, covering various types such as unimodal, high-dimensional multimodal, and fixed-dimensional multimodal. In addition, CEC 2017 benchmark problems are also utilized for evaluation purposes. The optimization results clearly demonstrate that hPSO-TLBO performs remarkably well in addressing the benchmark functions. It exhibits a remarkable ability to explore and exploit the search space while maintaining a balanced approach throughout the optimization process. Furthermore, a comparative analysis is conducted to evaluate the performance of hPSO-TLBO against twelve widely recognized metaheuristic algorithms. The evaluation of the experimental findings illustrates that hPSO-TLBO consistently outperforms the competing algorithms across various benchmark functions, showcasing its superior performance. The successful deployment of hPSO-TLBO in addressing four engineering challenges highlights its effectiveness in tackling real-world applications.

Dataset Information

A Systematic Approach to Teaching Case Studies and Solving Novel Problems.

Publications

A Systematic Approach to Teaching Case Studies and Solving Novel Problems.

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