护理本科生深度学习能力与学习动力的相关性研究
Chinese Evidence-based Nursing(2022)
福建中医药大学
Abstract
目的:分析护理本科生深度学习能力与学习动力的相关性,为提升护理本科生深度学习能力提供依据.方法:采用整群抽样法,利用大学生深度学习量表和护理专业学生学习动力评定量表对460 名二年级、三年级护理本科生进行调查.结果:护理本科生深度学习得分为(101.68±12.45)分、学习动力得分为(121.71±16.53)分,两者总分呈正相关(r=0.642,P<0.05).多元逐步回归分析显示,学习动力和学习自信心是护理本科生深度学习的主要影响因素(调整R2=0.43,P<0.001).结论:护理本科生深度学习能力处于中等水平,帮助护理本科生设定学习目标及执行计划,充分激发其学习动力,树立学习自信,有助于其深度学习能力的提高.
More求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined