Changsoo Song, Resa Helikar, Wendy M Smith, Tomáš Helikar
{"title":"影响教师采用和持续使用计算机科学技术的因素:细胞集体背景下的案例研究。","authors":"Changsoo Song, Resa Helikar, Wendy M Smith, Tomáš Helikar","doi":"10.1187/cbe.22-11-0239","DOIUrl":null,"url":null,"abstract":"Acquiring computational modeling and simulation skills has become ever more critical for students in life sciences courses at the secondary and tertiary levels. Many modeling and simulation tools have been created to help instructors nurture those skills in their classrooms. Understanding the factors that may motivate instructors to use such tools is crucial to improve students’ learning, especially for having authentic modeling and simulation learning experiences. This study designed and tested a decomposed technology acceptance model in which the perceived usefulness and perceived ease of use constructs are split between the teaching and learning sides of the technology to examine their relative weight in a single model. Using data from instructors using the Cell Collective modeling and simulation software, this study found that the relationship between perceived usefulness–teaching and attitude toward behavior was insignificant. Similarly, all relationships between perceived ease of use–teaching and the other variables (i.e., perceived usefulness–teaching and attitude toward behavior) became insignificant. In contrast, we found the relationships between perceived ease of use–learning and the other variables (i.e., perceived usefulness–teaching, perceived usefulness–learning, and attitude toward behavior) significant. 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Factors Influencing Instructors' Adoption and Continued Use of Computing Science Technologies: A Case Study in the Context of Cell Collective.
Acquiring computational modeling and simulation skills has become ever more critical for students in life sciences courses at the secondary and tertiary levels. Many modeling and simulation tools have been created to help instructors nurture those skills in their classrooms. Understanding the factors that may motivate instructors to use such tools is crucial to improve students’ learning, especially for having authentic modeling and simulation learning experiences. This study designed and tested a decomposed technology acceptance model in which the perceived usefulness and perceived ease of use constructs are split between the teaching and learning sides of the technology to examine their relative weight in a single model. Using data from instructors using the Cell Collective modeling and simulation software, this study found that the relationship between perceived usefulness–teaching and attitude toward behavior was insignificant. Similarly, all relationships between perceived ease of use–teaching and the other variables (i.e., perceived usefulness–teaching and attitude toward behavior) became insignificant. In contrast, we found the relationships between perceived ease of use–learning and the other variables (i.e., perceived usefulness–teaching, perceived usefulness–learning, and attitude toward behavior) significant. These results suggest that priority should be given to the development of features improving learning over features facilitating teaching.
期刊介绍:
CBE—Life Sciences Education (LSE), a free, online quarterly journal, is published by the American Society for Cell Biology (ASCB). The journal was launched in spring 2002 as Cell Biology Education—A Journal of Life Science Education. The ASCB changed the name of the journal in spring 2006 to better reflect the breadth of its readership and the scope of its submissions.
LSE publishes peer-reviewed articles on life science education at the K–12, undergraduate, and graduate levels. The ASCB believes that learning in biology encompasses diverse fields, including math, chemistry, physics, engineering, computer science, and the interdisciplinary intersections of biology with these fields. Within biology, LSE focuses on how students are introduced to the study of life sciences, as well as approaches in cell biology, developmental biology, neuroscience, biochemistry, molecular biology, genetics, genomics, bioinformatics, and proteomics.