基于模糊C均值的在线协作学习混合分组研究

在构建了学习者多维特征模型的基础上,设计了基于模糊C均值的在线协作学习混合分组算法。提取学习者多维特征分量,通过模糊C均值算法以学习风格、知识水平、学习目标和兴趣爱好为主要特征进行同质聚类,根据活跃度和性别特征进行异质聚类以实现混合性质分组。该算法将异质和同质分组相结合,既保证了学习风格、知识水平、学习目标和兴趣爱好具有相似性的学习者划分到同一组,同时考虑到了活跃度和性别差异对学习效果的影响,使得小组划分更加合理。实验表明,该算法优于传统分组方法,学习者的学习效果和学习满意度都有较大提升。     

基于改进蚁群算法的协作学习分组研究

协作学习中根据学习者的特征进行有效分组对于提高学习者的学习效率具有重要的作用。基于学习者的学习能力、兴趣爱好和理解水平,在基于蚁群算法的协作学习分组中,以学习者特征相似度值作为启发信息,并针对蚁群算法可能出现的早熟收敛和停滞现象,分别在初期加入判断回退机制和在中后期对启发因子及期望因子进行动态调节以保证分组结果的准确性。模拟实验结果表明该算法在分组性能及准确性上均优于传统算法。     

基于改进细菌觅食的协作学习分组算法

针对协作学习中基于学习者特征的分组方式对学习过程的影响,设计一种基于改进细菌觅食的协作学习分组算法。在实现协作学习分组过程中,引入分组调节因子和特征权值,满足不同教学活动对学习者多个特征及分组的要求。为构成有效的分组空间,在细菌种群初始化中,细菌群体以实数编码,并加入随机扰动以增加细菌种群的多样性;在算法后期加入二次变异操作,以避免细菌觅食算法可能出现的早熟收敛现象。仿真实验结果表明,该算法在不同分组形式下,与传统算法相比,具有较优的分组性能和较高的准确率,并且对于不同数据集规模具有良好的稳定性。     

基于Wiki平台的协作小组分组策略研究

借助Wiki系统,对Wiki协作学习平台进行二次开发,以实现协作小组自动划分.采用聚类算法,根据学习者的关键特征类型对协作小组进行划分,以使协作学习绩效达到最优.     

基于在线学习行为分析的个性化学习推荐

随着在线课程和线上学习的普及,大量的在线学习行为数据被积累。如何利用数据挖掘技术分析积累的大数据,从而为教学决策和学习优化提供服务,已经成为新的研究重点。文中分析了在线学习的行为特征,挖掘学习者的性格特征与学习效率的关系,实现个性化学习方法推荐。首先,提取在线学习行为特征,并提出了一种基于BP神经网络的学习成绩预测方法,通过分析在线学习行为特征,预测其相应的线下学习成绩;其次,为了进一步分析学习者的在线学习行为与成绩的关系,提出了基于实际熵的在线学习行为规律性分析,通过分析学习者的在线学习行为,定义并计算相应的实际熵值来评估个体的学习行为规律性,从而分析规律性与最终成绩的关系;再次,基于Felder-Silverman性格分类法获得学习者的性格特征,对学习者实现基于K-means的聚类分析获得相似学习者的类别,将学习成绩较优的学习者的在线学习习惯推荐给同一类别的其他学习者,从而提高学习者的在线学习效率;最终,以某在线课程平台的实际数据为实验对象,分别实现在线学习行为特征提取、线下成绩预测、学习规律性分析和个性化学习推荐,从而验证了所提方法的有效性和应用价值。     

多目标优化视角下在线学习群体形成方法

形成既能满足教师教学实施需求,又能得到学习者认可的在线学习群体是影响在线协作学习效率的重要因素.多目标粒子群算法和遗传算法应用于在线学习群体形成领域是目前的研究热点.然而,利用多目标粒子群算法解决在线学习群体形成问题时存在多样性差,容易陷入局部最优等问题;运用遗传算法解决在线学习群体形成问题时,则需要以耗费大量时间为代...     

语义增强的在线学习行为预测研究

当前学习者的在线学习行为预测研究未充分利用短文本中的语义数据,导致对学习者的学习状态刻画不够全面,严重影响了行为预测的准确性.针对此问题,本文提出了语义增强的在线学习行为预测方法.首先,利用双向长短时记忆网络得到到短文本的语义向量表示;其次,基于学习者的统计、行为和短文本数据得到学习者的特征表征,并利用长短时记忆网络模型构建其学习状态表征;最后,利用学习状态表征预测学习者的学习行为.在11门真实在线课程数据集上的实验表明,本文方法能过有效提升在线学习行为预测的精确度.     

基于短文本情感增强的在线学习者成绩预测方法

当前利用短文本情感信息进行在线学习成绩预测的研究存在以下问题: 1)当前情感分类模型无法有效适应在线学习社区的短文本特征, 分类效果较差; 2)利用短文本情感信息定量预测在线学习成绩的研究在准确性上还有较大的提升空间. 针对以上问题, 本文提出了一种短文本情感增强的成绩预测方法. 首先, 从单词和句子层面建模短文本语义, 并提出基于学习者特征的注意力机制以识别不同学习者的语言表达特点, 得到情感概率分布向量; 其次, 将情感信息与统计、学习行为信息相融合, 并基于长短时记忆网络建模学习者的学习状态; 最后, 基于学习状态预测学习者成绩. 在三种不同类别课程组成的真实数据集上进行了实验, 结果表明本文方法能有效对学习社区短文本进行情感分类, 且能够提升在线学习者成绩预测的准确性. 同时, 结合实例分析说明了情感信息、学习状态与成绩之间的关联.     

在线课程平台融入社交性对学习者影响的实证研究

目前在线课程平台学习者参与交互的动力不足,交互数据的缺乏使得难以对在线课程平台学习者进行准确地分析与量化,进而使平台中的相关服务受到限制.社交功能能否在一定程度上弥补学习者交互数据稀疏问题,并为学习者的在线课程学习起到促进作用.针对该问题,以学者网课程平台中一门活跃课程的学习者作为研究对象,通过假设检验和描述统计的方法,对学习者在课程平台中的在线社交数据和学习者的线下真实成绩数据进行对比分析,研究结果表明在线学习者社交活跃度总体不高,交互活动对学习成绩具有正相关影响,社交关系则通过促进交互活动的方式间接地影响学习效果.最后围绕研究结果,分别从课程平台、教师和学习者三个角度提出促进学习者更好地使用在线课程平台进行学习的策略建议.     

结合社会网络分析和多维特征聚类的学习小组划分方法

通过引入社会网络分析方法,建立了综合学习能力、学习状态和协作能力多维指标的学习者特征模型,应用模糊相似和模糊等价演算,以多层次的模糊动态聚类方法识别候选学习领袖,通过融合多类型学习者实现学习小组的迭代划分。实验分析表明,该方法可提高虚拟学习社区中学习小组划分结果的准确度,具有良好的自适应性。     

Community-organizing agent: An artificial intelligent system for building learning communities among large numbers of learners

Web-based (or online) learning provides an unprecedented flexibility and convenience to both learners and instructors. However, large online classes relying on instructor-centered presentations could tend to isolate many learners. The size of these classes and the wide dispersion of the learners make it challenging for instructors to interact with individual learners or to facilitate learner collaborations. Since extensive literature has confirmed that the substantial impact of learner interaction on learning outcomes, it is pedagogically critical to help distributed learners engage in community-based collaborative learning and to help individual learners improve their self-regulation. The E-learning lab of Shanghai Jiaotong University created an artificial intelligence system to help guide learners with similar interests into reasonably sized learning communities. The system uses a multi-agent mechanism to organize and reorganize supportive communities based on learners’ learning interests, experiences, and behaviors. Through effective award and exchange algorithms, learners with similar interests and experiences will form a community to support each others’ learning. Simulated experimental results indicate that these algorithms can improve the speed and efficiency in identifying and grouping homogeneous learners. Here, we will describe this system in detail and present its mechanism for organizing learning communities. We will conduct human experimentations in the near future to further perfect the system.     

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。     

CC–LR: providing interactive,challenging and attractive Collaborative Complex Learning Resources

Many researchers argue that students must be meaningfully engaged in the learning resources for effective learning to occur. However, current online learners still report a problematic lack of attractive and challenging learning resources that engage them in the learning process. This endemic problem is even more evident in online collaborative learning approaches whose resources lack of authentic interactivity, user empowerment, social identity and challenge, thus having a negative effect on learners' self‐motivation and engagement. To overcome these and other limitations and deficiencies, in this paper, a new type of learning resource named Collaborative Complex Learning Resources (CC–LR) is presented based on the virtualization of collaborative learning with the aim of leveraging knowledge elicited during live sessions. During the CC–LR execution, the collaborative sessions are animated so learners can observe how avatars discuss and collaborate, how discussion threads grow and how knowledge is been constructed, refined and consolidated. In addition, complex aspects of the learning process can be incorporated in the CC–LRs during their creation, such as cognitive assessment and emotional awareness. The system produced from this research is tested to evaluate the CC‐LR enriched with complex information and analyze its effects in the discussion process. The research reported in this paper was undertaken within the Seventh Framework Programme (FP7) European project called ‘Adaptive Learning via Intuitive/Interactive, Collaborative and Emotional systems’.     

一种面向个性化协同学习的任务生成方法

现有协同学习应用无法很好地支持学习任务的生成以及学习者的个性化学习.针对此问题,提出了一种面向个性化协同学习的学习任务生成方法.该方法在学习任务形式化描述的基础上,通过学习者分组、确定学习资源、分解学习单元、分配学习模式以及生成事件序列等步骤,生成既符合学习者群体认知水平,又符合个体学习者个性特征的协同学习任务.根据此任务,可以较好地实现网络环境下群体学习者的个性化协同学习.目前,该方法已在Smart-Realcalss网络教学系统中得到应用.     

Usability of a cloud-based collaborative learning framework to improve learners’ experience

Computer-Supported Collaborative Learning (CSCL) is concerned with how Information and Communication Technology (ICT) might facilitate learning in groups which can be co-located or distributed over a network of computers such as Internet. CSCL supports effective learning by means of communication of ideas and information among learners, collaborative access of essential documents, and feedback from instructors and peers on learning activities. As the cloud technologies are increasingly becoming popular and collaborative learning is evolving, new directions for development of collaborative learning tools deployed on cloud are proposed. Development of such learning tools requires access to substantial data stored in the cloud. Ensuring efficient access to such data is hindered by the high latencies of wide-area networks underlying the cloud infrastructures. To improve learners’ experience by accelerating data access, important files can be replicated so a group of learners can access data from nearby locations. Since a cloud environment is highly dynamic, resource availability, network latency, and learner requests may change. In this paper, we present the advantages of collaborative learning and focus on the importance of data replication in the design of such a dynamic cloud-based system that a collaborative learning portal uses. To this end, we introduce a highly distributed replication technique that determines optimal data locations to improve access performance by minimizing replication overhead (access and update). The problem is formulated using dynamic programming. Experimental results demonstrate the usefulness of the proposed collaborative learning system used by institutions in geographically distributed locations.     

e‐Learning recommender system for a group of learners based on the unified learner profile approach

In the age of information explosion, e‐learning recommender systems (eL_RSs) have emerged as effective information filtering techniques that attempt to provide the most appropriate learning resources for learners while using e‐learning systems. These learners are differentiated on the basis of their learning styles, goals, knowledge levels and others. Several attempts have been made in the past to design eL_RSs to recommend resources to individuals; however, an investigation of recommendations to a group of learners in e‐learning is still in its infancy. In this paper, we focus on the problem of recommending resources to a group of learners rather than to an individual. The major challenge in group recommendation is how to merge the individual preferences of different learners that form a group and extract a pseudo unified learner profile (ULP) that closely reflects the preferences of all learners. Firstly, we propose a profile merging scheme for the ULP by utilizing learning styles, knowledge levels and ratings of learners in a group. Thereafter, a collaborative approach is proposed based on the ULP for effective group recommendations. Experimental results are presented to demonstrate the effectiveness of the proposed group recommendation strategy for e‐learning.     

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。     

Guidelines for facilitating the development of learning communities in online courses

Online learning has grown exponentially in recent years; however, dropout problem remains challenging for some online programmes. The dropout problem can be attributed to a number of reasons, with a lack of interaction between learners and the instructor constituting one of the main reasons. The lack of interaction also leads to learners' feeling of isolation. Learning communities can provide learners with an environment conducive to increased interactions and alleviate their feeling of isolation. Unfortunately, there are no clear rules that instructors can follow to help learners create learning communities. In this paper, we propose guidelines for online instructors to facilitate the development of learning communities in online courses. We first review the definition of a learning community, importance of a learning community and factors affecting the development of a learning community. Afterwards, based on a review of the existing guidelines and other relevant literature, we propose guidelines for facilitating the development of learning communities in online courses.     

基于SNS的Web协作学习系统设计

随着Web2.0的快速发展,具有社会特征的SNS软件不断涌现,基于SNS平台的Web协作学习逐渐成为一种新型学习模式。提出了一种基于SNS的Web协作学习模式,并结合国内主流的SNS平台进行二次开发,设计了一个面向SNS的Web协作学习系统,主要实现了个人社区、协作学习社区、文件共享和问答系统等模块。实践证明该系统能够实现高效的Web协作和资源共享,能够帮助学习者更好地交流学习心得和分享学习成果,从而增强学习者的学习兴趣和解决问题的能力。