某护理学院护理本科生电子学习评价

1 Pub Date : 2023-07-18 DOI:10.46632/cllrm/4/1/8
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E-learning has gained popularity as an adaptable and readily available learning method, but its performance must be assessed to ensure optimal results. The purpose of this introduction is to emphasize the need of evaluating e-learning programmers to identify their influence on student happi-ness, acquisition of knowledge, and skill development. It also highlights the need of a standardized assessment framework in facilitating accurate and trustworthy evaluations. Educators and policymakers may make more informed judgements to increase the efficacy of e-learning projects if they understand the evaluation process. Research significance: The assessment of e-learning research is critical in the realm of education. For starters, it allows educators to assess the efficacy of e-learning pro-grammers in reaching targeted learning objectives. Second, it sheds light on how e-learning affects student satisfaction and engagement. Third, this research identifies opportunities for improvement in the design and delivery of e-learning. It also con-tributes to the establishment of a standardized assessment system, which promotes comparability and dependability across various e-learning programmers. Finally, the significance of this research resides in its capacity to increase the quality and effi-cacy of e-learning practices, resulting in better educational experiences for students. Method: The Grey Relationship Analysis (GRA) approach is a decision-making tool designed to analyses and assess connections between variables in circumstances when information is ambiguous or inadequate. It is especially beneficial when working with systems with insufficient data or dynamic and complicated interactions. GRA use grey numbers to show the data's uncertainty and incompleteness, enabling for more exact analysis. By generating grey relational grade, the approach assists decision makers to arrive at informed choices by identifying the most significant elements or factors in a system. GRA can be used in a variety of industries, including banking, technology, and management, where there is ambiguous or partial information. Alternative parameters: Analysis, Design, De-velopment, Implementation, Evaluation. Evaluation parameters: E-Learning Environment, Webpage Connection, Learning Rec-ords, Instruction Materials. Result: Analysis in 3rd rank, Design in 4th rank, development in 2nd rank, implementation in 5th rank, evaluation 1st rank. Conclusion: evaluation of e learning is progressed. 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引用次数: 0

摘要

这个短片调查了在线教育的评估及其作为一种学习工具的实用性。这项研究着眼于许多评估方法,这些方法被用来衡量电子学习程序的结果和效果。它调查了学生满意度、知识获取和技能发展在决定电子学习有效性方面的作用。摘要讨论了衡量电子学习的困难和限制,以及标准化评估框架的必要性。最后,本研究的目的是深入了解评估过程,从而协助教育工作者和政策制定者在将电子学习纳入教育机构方面做出明智的判断。导言:概述了e-learning评估及其在当前教育中的意义。电子学习作为一种适应性强且容易获得的学习方法已经受到欢迎,但必须对其性能进行评估以确保最佳结果。本引言的目的是强调评估电子学习程序的必要性,以确定它们对学生幸福感、知识获取和技能发展的影响。它还强调需要一个标准化的评估框架,以促进准确和值得信赖的评价。如果教育工作者和政策制定者了解评估过程,他们可能会做出更明智的判断,以提高电子学习项目的效率。研究意义:电子学习研究的评估是教育领域的关键问题。首先,它允许教育工作者评估电子学习程序在达到目标学习目标方面的有效性。其次,它揭示了电子学习如何影响学生的满意度和参与度。第三,本研究确定了改进电子学习设计和交付的机会。它还有助于建立标准化的评估系统,从而促进各种电子学习程序之间的可比性和可靠性。最后,本研究的意义在于它能够提高电子学习实践的质量和效率,从而为学生带来更好的教育体验。方法:灰色关联分析(GRA)方法是一种决策工具,用于分析和评估在信息模糊或不充分的情况下变量之间的联系。在处理数据不足或动态和复杂交互的系统时,它特别有用。GRA使用灰色数字来显示数据的不确定性和不完整性,从而可以进行更精确的分析。通过生成灰色关联等级,该方法通过识别系统中最重要的元素或因素来帮助决策者做出明智的选择。抓住可用于多种行业,包括银行、技术、和管理,模糊或部分信息。可选参数:分析、设计、开发、实施、评估。评估参数:网络学习环境、网页连接、学习记录、教学材料。结果:分析为第3级,设计为第4级,开发为第2级,实施为第5级,评价为第1级。结论:电子学习的评价取得了进展。以下是可选参数的排名——分析排名第三,设计排名第四,开发排名第二,实施排名第五,评估排名第一。
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Evaluation of E- Learning Undergraduate Nursing Students in a Faculty of Nursing
This short investigates the assessment of online education and its usefulness as a learning instrument. The research looks on the many assessment approaches that are used to measure the results and effect of e-learning programmers. It investigates the role of student satisfaction, knowledge acquisition, and skill development in determining the effectiveness of e-learning. The abstract discusses the difficulties and constraints in measuring e-learning, as well as the necessity for standardized evaluation frameworks. Finally, the goal of this research is to give insights into the assessment process, so assisting educators and poli-cymakers in making informed judgements about integrating e-learning into educational institutions. Introduction: The introduc-tion gives an outline of e-learning assessment and its significance in current education. E-learning has gained popularity as an adaptable and readily available learning method, but its performance must be assessed to ensure optimal results. The purpose of this introduction is to emphasize the need of evaluating e-learning programmers to identify their influence on student happi-ness, acquisition of knowledge, and skill development. It also highlights the need of a standardized assessment framework in facilitating accurate and trustworthy evaluations. Educators and policymakers may make more informed judgements to increase the efficacy of e-learning projects if they understand the evaluation process. Research significance: The assessment of e-learning research is critical in the realm of education. For starters, it allows educators to assess the efficacy of e-learning pro-grammers in reaching targeted learning objectives. Second, it sheds light on how e-learning affects student satisfaction and engagement. Third, this research identifies opportunities for improvement in the design and delivery of e-learning. It also con-tributes to the establishment of a standardized assessment system, which promotes comparability and dependability across various e-learning programmers. Finally, the significance of this research resides in its capacity to increase the quality and effi-cacy of e-learning practices, resulting in better educational experiences for students. Method: The Grey Relationship Analysis (GRA) approach is a decision-making tool designed to analyses and assess connections between variables in circumstances when information is ambiguous or inadequate. It is especially beneficial when working with systems with insufficient data or dynamic and complicated interactions. GRA use grey numbers to show the data's uncertainty and incompleteness, enabling for more exact analysis. By generating grey relational grade, the approach assists decision makers to arrive at informed choices by identifying the most significant elements or factors in a system. GRA can be used in a variety of industries, including banking, technology, and management, where there is ambiguous or partial information. Alternative parameters: Analysis, Design, De-velopment, Implementation, Evaluation. Evaluation parameters: E-Learning Environment, Webpage Connection, Learning Rec-ords, Instruction Materials. Result: Analysis in 3rd rank, Design in 4th rank, development in 2nd rank, implementation in 5th rank, evaluation 1st rank. Conclusion: evaluation of e learning is progressed. Here is the rank for alternative parameters Analy-sis in 3rd rank, Design in 4th rank, development in 2nd rank, implementation in 5th rank, evaluation 1st rank.
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