基于分布式系统的锁服务器的容错机制的设计与实现

随着需求的不断增长,分布式文件系统的重要性和易用性也渐渐的展现了出来。大凡分布式文件系统都有容错机制来实现数据的恢复以及服务器之间的冗余备份或者动态的调整某个服务器的负载,本文将会讨论一个分布式系统的锁服务器的容错机制。     

Excel在公路检测中的应用

VBA的强大功能可以帮你使繁琐的工作变得简单.故在简单介绍一下VBA的基础上,着重讲解平整度的分析计算与固定格式套用的方法,并在VBA的编辑器中实现此过程,从而使数据处理变得简单快捷,为我们的工作步入自动化(半自动化)打下坚实的基础.     

Psychometric characteristics of a sexuality education survey for teachers of secondary school students with learning disabilities in Malaysia

Delivery of sexuality education to secondary school students in Malaysia started since 1989. However, this area of education was neglected for secondary students with learning disabilities. Therefore, in order to explore their needs for sexuality education, society's perceptions especially teachers’ towards this matter should be considered. To achieve this objective, it is important to conduct a needs analysis on teachers’ perceptions towards this topic. An instrument with good psychometric properties is needed to obtain consistent and reliable information. This paper describes the development and psychometric testing of the Sexuality Education Survey for Teachers of Secondary School Students with Learning Disabilities (SES). SES showed acceptable content validity, construct validity, and achieved satisfactory reliability. The instrument also has good stability (intraclass correlation coefficient = 0.95) to be adopted as an instrument for analysing the health and sexuality education needs of secondary students with learning disabilities in Malaysia.     

高校图书馆价值与功能的内隐观调查与分析

采用内隐观调查方法,对三所大学615名大学生进行高校图书馆价值与功能的调查研究。结果表明,大学生读者所认为的高校图书馆价值功能可归为6类：获取信息、提升素养、提供资源、提供学习环境与氛围、提供服务与管理、传播传承文化。同时,讨论内隐观研究结果的层次性特点及对当前高校图书馆核心价值研究的启迪意义。     

汉诗英译双重比较的及物性视角研究——以唐诗《春晓》的多译本分析为例

国内外少数学者用Halliday的系统功能语法对汉诗英译的不同译文进行了比较分析,但既对汉诗及译本进行英汉对比,又对其不同译本进行对比的双重比较并不多见。以唐诗《春晓》的原文与译文及不同英译本的及物性分析为例,尝试从系统功能的及物性角度进行汉诗原文与译文以及不同译本之间的双重比较,来验证系统功能语言学在汉诗英译中进行双重比较的可操作性和可应用性。     

The mediating effects of needs satisfaction on the relationships between prior knowledge and self-regulated learning through artificial intelligence chatbot

The anthropomorphic characteristics of artificial intelligence (AI) can provide a positive environment for self-regulated learning (SRL). The factors affecting adolescents' SRL through AI technologies remain unclear. Limited AI and disciplinary knowledge may affect the students' motivations, as explained by self-determination theory (SDT). In this study, we examine the mediating effects of needs satisfaction in SDT on the relationship between students' previous technical (AI) and disciplinary (English) knowledge and SRL, using an AI conversational chatbot. Data were collected from 323 9th Grade students through a questionnaire and a test. The students completed an AI basic unit and then learned English with a conversational chatbot for 5 days. Confidence intervals were calculated to investigate the mediating effects. We found that students' previous knowledge of English but not their AI knowledge directly affected their SRL with the chatbot, and that satisfying the need for autonomy and competence mediated the relationships between both knowledge (AI and English) and SRL, but relatedness did not. The self-directed nature of SRL requires heavy cognitive learning and satisfying the need for autonomy and competence may more effectively engage young children in this type of learning. The findings also revealed that current chatbot technologies may not benefit students with relatively lower levels of English proficiency. We suggest that teachers can use conversational chatbots for knowledge consolidation purposes, but not in SRL explorations.

Practitioner notes

What is already known about this topic

• Artificial intelligence (AI) technologies can potentially support students' self-regulated learning (SRL) of disciplinary knowledge through chatbots.
• Needs satisfaction in Self-determination theory (SDT) can explain the directive process required for SRL.
• Technical and disciplinary knowledge would affect SRL with technologies.

What this paper adds

• This study examines the mediating effects of needs satisfaction in SDT on the relationship between students' previous AI (technical) and English (disciplinary) knowledge and SRL, using an AI conversational chatbot.
• Students' previous knowledge of English but not their AI knowledge directly affected their SRL with the chatbot.
• Autonomy and competence were mediators, but relatedness was not.

Implications for practice and/or policy

• Teachers should use chatbots for knowledge consolidation rather than exploration.
• Teachers should support students' competence and autonomy, as these were found to be the factors that directly predicted SRL.
• School leaders and teacher educators should include the mediating effects of needs satisfaction in professional development programmes for digital education.