论基于WebX.0的网络学习环境设计

WebX.0正由连接信息的简单网页发展为连接人类智慧的愈加复杂和智能的元网络（Meta Web）,这对传统的由“教”的一方驱动的网络学习环境设计提出了挑战.对环境整体而言,环境形成良性生态循环有利于维持其可持续发展;对学习者而言,当学习者面对多种网络组成的复杂的、具有高度不确定性的学习环境时,根据自身的需求构建个人学习环境（Personal Learning Environment,PIE）是促进其学习的最有效方式.因此,WebX.0时代的网络学习环境设计应当以创设网络学习生态和利于学习者构建PLE为目标,以学习者的需求为起点,提高“以学习者为中心”、“标准化”、“一体化”、“开放化”的程度,以更好地促进学习者在复杂网络学习环境中通过构建PLE进行的有效学习.     

Mashup聚合技术与网络学习

如何将Web2.0环境下的海量信息与服务整合到统一的网络学习环境中,有效地促进学习,是网络学习发展中逐渐引起思考的问题。基于SaaS的Mashup可以整合不同外部数据源的内容和功能,把松散的学习资源、要素、服务和功能进行集成,提供增值的、创新的服务和统一完整体验的Web应用系统,这将是未来网络学习系统重要的应用模式。基于Mashup的网络学习将有助于构建个人学习环境和学习网络,满足情境化学习需要,从而达到学习效果的最优化。     

基于EyeOs+Moodle的个人学习云环境的构建研究

个人学习环境以学习者为中心的个性化特点有益于学习者的个性化的自主学习,云计算技术是学习者构建个人学习环境的捷径。文章在个人学习环境和云计算的基础上提出了个人学习云环境,设计了其系统结构和服务模式,结合云操作系统EyeOs和课程管理系统Moodle的技术和应用特点,构建了个人学习云环境,并在教学实践中进行了应用。     

个性化语言学习系统的学习者需求分类

文章基于QFD(Quality Function Deployment,质量功能展开)和Kano模型,对个性化语言学习系统的学习者需求分类进行了研究——通过对学习者原始需求信息的获取、整理、转换和归类,得到学习者在学习过程、资源、内容和环境等四个方面的需求项及需求展开项;然后,对需求展开项的重要度进行调查和统计分析,将学习者需求展开项分为基本型、期望型和兴奋型三类,并构建了学习者需求项的Kano-金字塔模型。期待文章的相关研究成果,能有助于推动以学习者为中心的个性化语言学习系统的质量改进。     

基于教学设计的学习设计构想初探

伴随着信息时代知识的日益更新,信息时代的学习方式也在不断变化。信息尉代为学习者提供了内容丰富、形式多样的学习资源,学习者可以借助各种各样的信息设备获取这些资源,满足自己多样化的学习需求。然而并不是接触到资源就可以顺利将其化为自己的知识,学习者还必须对自己的学习进行系统的设计,在一个适合自己的环境里,按照有利于知识内化的方式进行学习,这样才能获得更好的学习效果。如何进行学习设计？     

基于关联主义的个人学习网络管理系统

随着学习理论的发展和网络通讯技术的进步,网络教学（学习）系统在经历了CMS、LMS、LCMS和LAMS的四个阶段之后,面对学习者新的学习需求,又将呈现新的功能特征。建设学习网络管理系统（LNMS）的理念,将学习者关系管理纳入学习管理的过程,对于构建一个支持学习者非正式学习和社会化学习的网络环境,具有重要意义。     

应用云计算构建师生协作平台

云计算是当前信息技术应用的热门领域。是下一代网络计算平台的核心技术。在教育领域,云计算是未来网络学习的基本环境与平台,将对学习者个人学习环境构建、学校资源建设和教育信息系统开发等带来积极影响。Google云计算服务,为教师和学习者搭建的师生协作平台,体现了以人为本和满足学习者个性化的需求。     

突破机构教育之囿:网络世界的个人学习

本文首先分析在传统教育机构基于模式和设计的学习环境下学习者的需求,并从收复学习管理权的角度出发阐述一种基于网络的、学习者自我管理学习的新教育系统.文章对运用模式设计学习系统或学习过程提出质疑,主张学习应该由学习者根据各自的具体情况来开展;他们通过在个人学习环境中的一个学习资源网络进行学习.这种模式的学习设计类似于八哥集群飞舞(murmuration),即以自我组织方式形成和重塑学习过程.作者建议学生通过"收复学习管理权"进行自我组织.换言之,发展自己的学习系统,将学习延伸到传统教育机构之外进行.     

导向型自主学习的医学实验及临床技能教学

针对导向型自主学习理论、研究背景及内涵的深入研究,根据医学实验及临床技能教学的突出特点,构建导向型自主学习系统;学生通过系统及配套的网络实验室学习资源进行独立自主的有效学习,还可以向教师及学习者同伴寻求帮助,并积极参与、协作、互动、交流学习;教师利用系统能轻松的管理网络实验室学习资源,并能提供对学生引导和帮助,使学习者在导向型自主学习系统环境中进行有效的自主学习、协作学习和个性化学习;根据网络实验室学习资源的属性及师生的需求,设计了系统实例和系统的功能模型,确保学习者对实验室研究性学习资源进行有效的学习。     

基于语义网和本体构建适应性学习资源信息系统

为了满足学习者自主学习、协作学习以及个性化适应学习的需求,结合语义网、本体的概念,整合相关领域内大量学习资源,构建适应性学习资源信息系统。依据信息系统远期发展的需求,设计了适应性学习资源信息系统模型和信息系统安全管理模型,成功地实现了系统实例,达到了在合适时间,合适空间,用合适的方式将适应的学习资源呈现给学习者,为学习者提供了本体适应性和个性化学习服务。     

基于Mashup聚合技术对网络学习的有效支持探微

如何将网络中海量学习资源高效、快捷地组织到统一的网络环境中,有效地服务并促进网络学习,是当前网路学习发展中所面临的问题。Mashup作为一种新型聚合技术能将多个外部数据源进行整合,提供一种创新服务,成为未来网络学习的重要应用模式。针对网络学习中存在的问题,探讨了Mashup聚合技术对网络学习的有效支持,以期提高网络学习绩效。     

虚实融合学习环境:概念、特征与应用

随着现代信息技术的发展和教育教学理念的转变,基于网络和多媒体的"虚拟"学习环境已经被人们熟悉并部分取代基于课堂的"现实"学习环境。虚拟和现实的学习环境各有利弊,前者有利于学习者使用丰富的数字化资源开展跨地域的协作学习、拓宽知识视野,后者有利于学习者的临场体验和实际操作能力培养。在分析相关文献和现有学习环境特征研究的基础上,分析了"虚实融合学习环境"的内涵与特征,对虚拟、现实以及虚实融合的学习环境进行比较,并就若干典型的虚实融合学习环境及其教学应用情况进行了分析,其中包括:校园环境信息观测与研究性学习、GLOBE全球性学习与观察、虚实融合的机器人教育、数字化探究实验室。     

教学效率数学模型建构的教育原理及意义

如何提高教学效率,是各层次各类教学研究的重要问题。在教学理论和实践基础上首次提出的“教学效率的数学模型”,可以直观地理解教学效率的三个限制性原理,即在学校教育中不能仅仅强调学生自主学习,不能仅仅强调教师单向传授知识,教与学是相互“耦合”的。其原理表明三个不可能：只要求学生自主学习,不可能高效;只要求教师灌输,不可能高效;不将教师的教学转化为学生的自学,不可能高效。以此模型推论,对于不同学科、不同水平的教师和学生,两条“学历一效率”曲线的交点0是不同的。这就为因材施教地研究教学效率提供了新思路。在系统研究教学模型基础上建构的“教学模型的数学模型”,是“模式的模式”,即元模式。元模式的研究具有方法论价值,为教师在教学中选择合适的教学模型提供了实用的模式“网络结构”。这些数学模型之于远程教育的意义是：远程教学十分需要新的突破。     

中国远程学习者学习风格特征的三维模型

测量远程学习的学习风格，可以帮助学生选择适合自身学习风格的学习策略，进而提高远程学习的质量。该的研究目的在于建构符合我国远程学习学习风格特征的三维模型，以此作为测量和鉴别我国远程学习学习风格特征的理论基础。该研究以学习的信息加工理论、科尔布的经验学习理论、荣格的人格特征类型理论等三大学习风格理论模型为基础，综合了生理、社会、心理三层面上的学习风格特征。研究结果形成了中国远程学习学习风格的三维模型。该模型分三个维度：1)生理维度：从视觉、听觉、动觉三个角度描述远程学习在感觉通道偏好上的学习风格特征；2)经验维度：描述远程学习与其社会性有关的四种学习方式偏好。即原理型、经验型、实践型、思考型；3)心理维度：主要涵盖了与远程学习风格相关的人格心理特征类型，即内向与外向、现实与幻想、理性与感性、计划与随意。作希望该模型能全面概括我国远程学习的学习风格类型特点，并能作为我国远程学习学习风格测量工具开发的理论基础。     

Hybrid E‐Learning Acceptance Model: Learner Perceptions

E‐learning tools and technologies have been used to supplement conventional courses in higher education institutions creating a “hybrid” e‐learning module that aims to enhance the learning experiences of students. Few studies have addressed the acceptance of hybrid e‐learning by learners and the factors affecting the learners’ satisfaction with these tools. This study assesses hybrid e‐learning acceptance by learners using three critical success factors: instructor characteristics, information technology infrastructure, and organizational and technical support. Structural equation modeling was applied to examine and validate the hypothesized relationships among the three factors and their effects on learners’ acceptance of hybrid e‐learning. A total of 538 usable responses from university students were used to validate the proposed research model. The influence of the three factors on learners’ decision to accept hybrid e‐learning was empirically examined. The results show that all three factors significantly and directly impacted the learners’ acceptance of hybrid e‐learning courses. Information technology infrastructure and organizational support were proven to be key determinants of the instructor characteristics as a critical success factor of hybrid e‐learning acceptance by learners. Implications of this work for higher education institutions, researchers, and instructors are described.     

Adoption model for a hybrid SEM-neural network approach to education as a service

Education and Information Technologies - Learning systems are widely adopted as educational tools. The success of a learning system depends on the level of acceptance by instructors and learners....     

Design and implementation of a SMART Learning environment for the Upskilling of Cybersecurity professionals in Mauritius

Teaching and Learning confined to within the four walls of a classroom or even online Learning through Massive Online Courses (MOOCs) and other Learning Content Management Systems (LCMS) are no longer seen as the optimal approach for competency and skills development, especially for working professionals. Each of these busy learners have their own training needs and prior knowledge. Adopting the one-size-fits-all teaching approach is definitely not effective, motivating and encouraging. For some learners, the content might be too easy and for others, it might be too difficult. This is why this research presents the use of SMART Learning Environment that makes use of Intelligent Techniques to personalise the learning materials for each learner. This mismatch in skills is becoming a pressing issue and is having a direct impact on the ICT Sector, which is one of the pillars of the Mauritian Economy. This research, therefore, besides proposing a novel approach to learning, also attempts to address an issue of national importance. Data was collected during three phases, namely an Expert Reference Group Discussion, a pre-test questionnaire and a survey questionnaire. The Expert Reference Group Discussion was carried out to further understand the training needs and expectations of Cybersecurity professionals in Mauritius. A SMART Learning Environment making use of Artificial Neural Networks and Backpropagation Algorithm to personalise learning materials was eventually designed and implemented. The major findings of this research were that personalisation of learning materials through the use of a SMART Learning Environment can be used to address the training needs of Cybersecurity professionals in Mauritius.

     

The effect of curricular activities on learner autonomy: the perspective of undergraduate mechanical engineering students

This study researches how first-year engineering students perceived the influence of curricular activities on their own learning autonomy, measured with an adaptation of the Personal Responsibility Orientation to Self-direction in Learning Scale (PRO-SDLS). Participants were questioned to assess the influence of the teacher's role. The results indicate that learners’ characteristics (motivation and self-efficacy) contribute more to learner autonomy (LA) than the teaching–learning transaction (control and initiative), as in the original PRO-SDLS validation. The most autonomous learners presented higher values in all LA components and dimensions, but the differences were greater in motivation and initiative. The participants with higher LA were not as dependent on the teacher, regarding assessment, the completion of classroom tasks and deadlines. Regardless of the degree of autonomy in learning, all participants viewed teachers as the main source of information. Therefore, LA plays an important role in teaching activities planning. Suggestions for adjustments and more flexible learning scenarios are formulated.     

‘Learning Style’: frameworks and instruments

In both education and training an important aspect of the design, development and delivery of learning is the role of individual differences between learners in terms of their ‘learning styles’. One may identify four broad categories of what have been termed ‘learning style’: (i) ‘cognitive personality elements’ (e.g. Witkin et al. 1977; Riding, 1991); (ii) ‘information‐processing style’ (e.g. Kolb, 1984; Honey & Mumford, 1992); (iii) ‘approaches to studying’ (e.g. Entwistle & Tait, 1994); (iv) ‘instructional preferences’ (e.g. Riechmann & Grasha, 1974). A study of 245 university undergraduates in business studies aimed to: (i) describe the range of individual differences present within the sample; (ii) investigate the relationship between learners’ cognitive styles, learning styles, approaches to studying and learning preferences; (iii) consider the implications of ‘learning style’ for teaching and learning in higher education. The present study suggested some overlap between the dimensions measured by the Learning Styles Questionnaire (Honey & Mumford, 1986; 1992) and the Revised Approaches to Studying Inventory (Entwistle & Tait, 1994). No statistically significant correlations were found between cognitive style, as measured by the Cognitive Styles Analysis (Riding, 1991) and any of the other ‘style’ constructs used. Further research is required to investigate these relationships, as is a large‐scale factor analytical study of the Honey and Mumford and Kolb instruments. The notions of whole brain functioning, integra‐tive approaches to studying and degree of learning activity are discussed.