基于学习者个性特征的协同学习任务生成方法

现有协同学习系统无法自动生成学习任务,满足学习者个性化学习,针对此问题,提出了基于学习者个性特征的协同学习任务生成方法。该方法以学习者的个性特征为输入,用改进的贪婪算法进行分组,根据学习单元及所需角色的耦合度确定协同学习模式,结合贝叶斯网络技术生成符合学习者个性特征的学习任务,实验结果表明,基于该任务进行学习,显著提高了学习者的学习效率和学习深度。     

基于CSP的学习智能导航服务模型与算法研究

研究了在网络学习平台中如何为自主学习者提供学习活动智能导航服务的问题。目前,为了进一步满足虚拟学习环境中学习者的个性化需求,笔者所在课题组开发的网络学习系统中采用了基于知识点的学习路径生成算法和资源组合算法,从而能为不同学习者从资源库中提取出个性化的学习内容包。然而,在实际使用中笔者发现,学习者虽然获得了符合需求的学习内容,其学习效果仍然缺乏有效的保障。原因在于学习过程实质是由学习活动而非静态的学习内容组成的,在学习过程中学习者应该根据教学规律和自身特点,有步骤地完成一系列学习子任务,才能保证最后的学习效果。为此,在以往的知识点关系层之上建立了学习活动,并将学习活动的调度问题转化为了csP问题模型,并提出了相应的求解方法。实验表明,该方法能够生成符合预期目标的学习任务调度,从而实现学习活动的智能导航服务。     

自主学习任务的导航和自动创建方法研究

网络环境下自主学习是基于计算机网络的自主学习。学习要素由原来基于课堂教学的学习者、内容、技术、教师而重组为学习者、资源、网络学习环境、教师。基本要素的变化使学习支持服务系统的范围更加宽泛。基于此,所谓网络环境下的自主学习是指学习者利用计算机网络提供的学习支持服务系统,根据教师设计的教学要求,自主性地选择学习时间、确定学习目标和学习内容、通过可选择的交互方式主动探究学习过程,实现有意义知识建构的学习方式。为了促进教师在教学过程中的经验交流,增强学习任务的复用,在自主学习系统中添加了知识点体系的概念,并且基于此概念,针对学生的个性化情况,设计了以学习评价为依据的导航算法。系统中还加入了由系统自动生成的学习任务,能够根据学生对知识点的掌握程度,智慧地从题库中抽取题目,自动化地生成任务。     

基于网络的个性化学习系统中学习者个性特征的提取算法

研究了基于网络的个性化学习系统如何根据学习者的个性特征主动推送个性化学习信息服务,并就如何提高基于网络的个性化学习系统的运行效率,提出了一种有效提取学习者个性特征的算法。该算法主要分为三步:(1)捕获学习者个性特征;(2)统计分析学习者所在学习群体的群体特征和趋势;(3)根据学习者与他所在学习群体有显著差异的特征项和群体特征,推送个性化学习信息。     

基于认知诊断的协同过滤试题推荐

针对协同过滤忽略了学习者的知识点掌握情况（学习状态）,对个性化教育试题推荐中运用的协同过滤算法进行了一定改进研究,该推荐算法分为三个步骤：（1）结合认知诊断模型,对学习者所练习题目中反映的知识点掌握情况进行建模分析;（2）利用协同过滤算法,结合学习者的知识点掌握情况,来对学习者的表现情况进行相似度分析;（3）根据相似用户的历史行为数据和目标用户的知识点掌握状态,针对学习者的近邻用户进行试题推荐.该推荐办法借鉴了群体相似学习者的共性,也考虑到了个体学习者的独特性,结合二者来对学习者进行个性化试题推荐,保证了试题推荐的准确性和性能,在个性化教育系统中,结合认知诊断改进了原有的协同过滤算法来对试题做出推荐.     

网络环境下自适应学习系统研究

针对网络环境下自适应学习系统发展中遇到的新问题,如学习者个性特征的挖掘及自适应学习策略的生成等,提出了一种新的自适应学习系统,即在建立个性化学习者模型的基础上,进一步研究相应的数据挖掘算法在自适应学习中的应用,得到学习者的个性特征与学习行为之间的关联规则,并最终生成自适应的学习策略提供给学习者;仿真研究表明,本系统设计先进,较好地实现了学习过程的自适应性。     

基于Web2.0和本体检索的个性化E-learning系统研究与开发

针对当前E-learning系统中存在着堆砌教学资料和学习内容单一、个性化不足等问题,设计一个基于Web2.0和本体检索技术的个性化E-learning系统,通过应用Ajax和RSS聚合技术以及Ontology本体技术,使得该系统能根据学习者的知识结构、学习目标、学习风格、偏好等特征信息提供适应学习者的教学方法和学习资源,营造个性化的网络学习环境。实验结果表明该系统能有效促进学生网络学习的效率,满足学生个性化学习的需求。     

协作学习中基于协同过滤的学习资源推荐研究

符合学习者特征的学习资源对于提高协作学习效率具有重要的影响。但是传统的学习资源推荐,没有充分考虑学习者、学习资源的特征和高效的推荐算法。针对上述问题,提出了基于协同过滤的学习资源推荐算法,根据学习者学习特征、学习资源特征和学习者对学习资源历史评价信息,采用协同过滤推荐算法,实现学习资源推荐。首先,通过学习者特征和学习资源的评分,寻找相似学习者并计算学习资源预测评分,然后根据该评分值和学习资源与学习者匹配度推荐学习资源,从而为学习者推荐符合自己兴趣爱好最合适的学习资源。实验结果表明该算法在个性化学习资源推荐的准确性上优于传统算法。     

个性化网络学习系统设计研究

本文以个性化学习理论为指导,探索出能够适应学习者个性化学习的网络学习系统的基本模块及其功能,并通过综合运用数据库技术、数据挖掘技术以及人工智能技术来完善系统的功能。该系统将网络学习的特点与学习者的认知水平、认知风格和兴趣爱好等个性化因素紧密结合起来,为学习者营造一个个性化的网络学习环境。     

个性化网络学习系统设计研究

本文以个性化学习理论为指导,探索出能够适应学习者个性化学习的网络学习系统的基本模块及其功能,并通过综合运用数据库技术、数据挖掘技术以及人工智能技术来完善系统的功能。该系统将网络学习的特点与学习者的认知水平、认知风格和兴趣爱好等个性化因素紧密结合起来,为学习者营造一个个性化的网络学习环境。     

The impact of goal focus, task type and group size on synchronous net-based collaborative learning discourses

Net-based collaborative learning discourses often suffer from deficiencies such as lack of coherence and coordination. It is suggested that the provision of two functionalities, referencing and typing, which learners may optionally use to ground their contributions during a chat-based discourse, can improve collaborative learning. In particular, we examined if goal focus, type of task and group size affect learning outcomes and the use of these functionalities. A chat-based system, called a learning protocol, implements these functionalities and serves as a net-based collaborative learning environment. Results suggest that a learning protocol is more beneficial for knowledge-acquisition tasks than for problem-solving tasks, and that the use of supporting functionalities increases when goal focus is on the group rather than on the individual. Also, there is a tendency that learning outcomes improve as group size increases. We propose that learning protocols provide potentially valuable design features that can promote net-based collaborative learning.     

基于动态任务空间的协同学习管理模型与实现

在协同学习支撑环境以及协同学习系统中,学生和老师都需要一些协同和控制手段去协调共同的学习活动。为此,提出了一种新的协同学习环境下的协同学习管理模型。它通过对协同学习任务的分解和学习资源的合理化配置搭建共享的任务空间。文中分析了动态任务空间的组成元素,给出了动态任务空间模型的形式化定义,介绍了模型管理工具的设计与实现,总结了模型在协同学习系统中应用的特点。     

A Web 2.0-based collaborative annotation system for enhancing knowledge sharing in collaborative learning environments

A limitation of current Web-based collaborative learning is the restricted ability of students to create and share individual annotations with annotated documents. Applying Web 2.0 collaborative annotation systems and analyzing students’ annotation behavior has attracted attention to improve collaborative learning. This study designed a personalized annotation management system 2.0 (PAMS 2.0) for managing, sharing, and reusing individual and collaborative annotations as well as providing a shared mechanism for discussion about shared annotations among multiple users.     

Exploring regulatory processes during a computer-supported collaborative learning task using process mining

The purpose of this study was to explore sequences of social regulatory processes during a computer-supported collaborative learning task and their relationship to group performance. Analogous to self-regulation during individual learning, we conceptualized social regulation both as individual and as collaborative activities of analyzing, planning, monitoring and evaluating cognitive and motivational aspects during collaborative learning. We analyzed the data of 42 participants working together in dyads. They had 90 min to develop a common handout on a statistical topic while communicating only via chat and common editor. The log files of chat and editor were coded regarding activities of social regulation. Results show that participants in dyads with higher group performance (N = 20) did not differ from participants with lower group performance (N = 22) in the frequencies of regulatory activities. In an exploratory way, we used process mining to identify process patterns for high versus low group performance dyads. The resulting models show clear parallels between high and low achieving dyads in a double loop of working on the task, monitoring, and coordinating. Moreover, there are no major differences in the process of high versus low achieving dyads. Both results are discussed with regard to theoretical and empirical issues. Furthermore, the method of process mining is discussed.     

Reviewing the need for gaming in education to accommodate the net generation

There is a growing interest in the use of simulations and games in Dutch higher education. This development is based on the perception that students belong to the ‘gamer generation’ or ‘net generation’: a generation that has grown up with computer games and other technology affecting their preferred learning styles, social interaction patterns and technology use generally. It is often argued that in education this generation prefers active, collaborative and technology-rich learning, i.e. learning methods that involve extensive computer use and collaboration among students. Gaming is then proposed as a new teaching method which addresses these requirements. This article presents the results of a survey which studied whether this discourse is also applicable to higher education students from the Netherlands and whether games, considered as active, collaborative and technology-rich learning experiences, are of greater importance in the formal education of today’s students. Of 1432 respondents from eight Dutch institutes of higher education surveyed between 2005 and 2009, about 25% fit our criteria of being a clear representative of the net generation. Furthermore, our analysis shows that there is little difference, and no statistically significant difference, in active, collaborative and technology-rich learning preferences between the representatives and non-representatives of the net generation. Furthermore, no large or statistically significant differences were found between representatives and non-representatives of the net generation with respect to the value they accorded to gaming in education. Overall our dataset did not fit the expectations raised by the net generation theory, with the percentage of students who fit the criteria being much lower than expected. However, regardless of whether they represented the net generation or not, in general our respondents preferred collaborative and technology-rich learning and deemed games a valuable teaching method.     

大规模在线协作学习分组方法及应用研究

学习孤独感问题是造成MOOC课程学习完成率低的原因之一,构建与学习者学习特征相适应的协作学习小组,可以有效解决学习孤独感问题。利用自编码神经网络提取在线学习者的关键特征,根据同质分组原则,利用模糊C均值算法对在线学习者进行迭代分组,使在线学习者从独自学习转变为以团队的形式进行协作学习,从而改善在线学习者的学习体验,降低学习孤独感。以edX平台上选择计算机科学与编程入门课程的19?846名在线学习者为实验对象,进行在线协作学习分组。实验结果表明,基于该分组方法,每个小组内学习者都有较高的同质性,可以很好地解决学习孤独感问题。     

Effects of Collaborative Web Based Vocational Education and Training (VET) on Learning Outcomes

Considering the increase in need of skilled human capital, the vocational training and education through e-learning has widely been adopted all over the world. It serves as a major alternative for training and teaching professionals, technicians, skilled workers, amateurs and students who cannot manage regular college education due to time, cost and distance factors. Prior research focuses on learning enhancement from several perspectives like computer mediated methods, learning games, personalized learning environments and web based instructional design. The proposed work contributes to this debate by investigating the role of collaboration i.e. student–student and student–instructor interaction, support material, instructor's feedback and evaluating their effects on learning outcome and students' learning experience. The investigation is performed through a case study presenting four vocational courses offered in three consecutive sessions. The findings revealed that collaborative practices i.e. group work, team effort, in time instructor's feedback and consolidated support material enhances learning experience of student and contributes positively to the learning outcome.     

Online collaboration: Collaborative behavior patterns and factors affecting globally distributed team performance

Studying the collaborative behavior of online learning teams and how this behavior is related to communication mode and task type is a complex process. Research about small group learning suggests that a higher percentage of social interactions occur in synchronous rather than asynchronous mode, and that students spend more time in task-oriented interaction in asynchronous discussions than in synchronous mode. This study analyzed the collaborative interaction patterns of global software development learning teams composed of students from Turkey, US, and Panama. Data collected from students’ chat histories and forum discussions from three global software development projects were collected and compared. Both qualitative and quantitative analysis methods were used to determine the differences between a group’s communication patterns in asynchronous versus synchronous communication mode. K-means clustering with the Ward method was used to investigate the patterns of behaviors in distributed teams. The results show that communication patterns are related to communication mode, the nature of the task, and the experience level of the leader. The paper also includes recommendations for building effective online collaborative teams and describes future research possibilities.     

融入学习者模型在线学习资源协同过滤推荐方法

在线教育存在“信息迷航”问题,而传统的信息推荐方法往往忽视教育的主体—学习者的特征。本文依据教育教学理论,根据在线教育平台中的学习者相关数据,研究构建了适用于在线学习资源个性化推荐的学习者模型。以协同过滤推荐方法为切入点,融合学习者模型中的静态特征和动态特征对协同过滤方法进行改进,建立融入学习者模型的在线学习资源协同过滤推荐方法。以2020年3~7月时间段的东北石油大学“C程序设计”课程学生的真实学习数据和行为数据为数据集,对本文提出的方法进行验证和对比,最后证明本文提出的方法在性能上均优于对比方法。