在线学习资源推荐综述

近年来越来越多的学校广泛使用网络在线授课，然而互联网中海量的学习资源令学习者难以抉择。因此，研究在线学习资源推荐并为学习者进行个性化推荐非常重要，这可以帮助学习者快速获取其所需的优质学习资源。针对在线学习资源推荐的研究现状，从以下5个方面进行分析总结。首先，总结了目前国内外在线教育平台在学习资源推荐方面的工作；其次，分析和探讨了以知识点习题、学习路径、学习视频和学习课程为学习资源推荐目标的4种算法；接着，分别从学习者和学习资源的角度出发，以具体的算法为例，详述了常用的基于学习者画像、基于学习者行为和基于学习资源本体的3种学习资源推荐算法；此外，总结了公开的在线学习资源数据集；最后，分析了学习资源推荐系统目前存在的问题和未来的发展方向。     

基于知识图谱和图嵌入的个性化学习资源推荐

面对海量的在线学习资源,学习者往往面临“信息过载”和“信息迷航”等问题,帮助学习者高效准确地获取适合自己的学习资源来提升学习效果,已成为研究热点.针对现有方法存在的可解释性差、推荐效率和准确度不足等问题,提出了一种基于知识图谱和图嵌入的个性化学习资源推荐方法,它基于在线学习通用本体模型构建在线学习环境知识图谱,利用图嵌入算法对知识图谱进行训练,以优化学习资源推荐中的图计算效率.基于学习者的学习风格特征进行聚类来优化学习者的资源兴趣度,以获得排序后的学习资源推荐结果.实验结果表明,相对于现有方法,所提方法能在大规模图数据场景下显著提升计算效率和个性化学习资源推荐的准确度.     

在线学习资源的个性化智能推荐研究探析

由于在线学习学习者的认知能力的不确定性、学习兴趣的变化性、用户偏好的多样性等,在线学习资源的个性化智能推荐面临新挑战。文章根据学习者认知能力的模糊综合诊断和学习者多重特征信息融合等,对在线学习资源的个性化智能推荐进行了研究,以期为相关研究者提供参考和启发。     

面向个性化学习的慕课资源推荐系统开发

近年来,在线学习得到了大规模普及,互联网上已发布了海量的慕课学习资源.针对广大学习者进行在线慕课学习时面临的"信息迷航"和"信息过载"等问题,以混合推荐算法为基础,通过多角度挖掘用户的个性化信息,设计并实现了一个面向个性化学习的慕课资源推荐系统,以此为广大学习者提供自主学习的辅助支持.该系统能充分挖掘学习者的显式和隐式偏好,为其推荐满意的慕课资源,具有良好的应用价值.     

干部在线学习中推荐系统冷启动问题研究

干部在线学习过程中,利用个性化推荐机制能够使参学干部从海量的学习资源中快速、精准地找到满足需求的学习资源,但是平台新注册的学员和最新上传的学习资源由于缺少大量的历史行为数据而面临推荐系统冷启动问题.为了解决该问题,使参学干部在碎片的学习时间内学习更有成效,收获更有价值,在阐述了干部在线学习中冷启动问题的形成原因和研究意义的基础上,提出了非个性化和半个性化推荐、提取参学干部注册信息、快速试探并实时反馈、抓取学习资源特征信息和专家多维度特征标注等冷启动解决方案.     

融合学习者时序行为和认知水平的个性化学习资源推荐算法

个性化服务是构建智慧学习环境的内在要求和建设要点.为学习环境中的主体（学习者）推送个性化学习资源可以提高学习资源的利用概率,解决在线学习容易产生的迷航问题.通过本体知识库的统一性语义建立学习者和学习资源内部结构特征,设计出一个有效计算两者相关性的推荐算法.算法中引入时间衰减函数来描述学习者学习时的时序特征,导入计算学习者认知水平与学习资源难度的匹配算子以体现学习的循序渐进原则.实验结果表明：所构建的时间函数和匹配算子达到了预期目标,更好地提升了所推荐学习资源的质量和适应性,且算法的时间复杂度能满足实时计算要求.     

基于多目标优化策略的在线学习资源推荐方法

目前在线学习资源推荐较多采用单目标转化方法,推荐过程中对学习者偏好考虑相对不足,影响学习资源推荐精度.针对上述问题,文中提出基于多目标优化策略的在线学习资源推荐模型(MOSRAM),在学习者规划时间内,以同时获得学习者对学习资源类型偏好度最大和难度水平适应度最佳为优化目标,设计具有向邻居均值学习能力和探索新区域能力的多目标粒子群优化算法(NEMOPSO),提出以MOSRAM为核心的在线学习资源推荐方法(NEMOPSO-RA).不同问题规模下融合经典多目标优化算法的推荐方法对比实验表明,NEMOPSO-RA可以有效提高在线学习资源的推荐精度和推荐性能.     

基于JADE的学习网络构建系统研究与实现

针对远程教育环境中,学习者分散、缺乏个性化学习指导等问题,构建了一个基于JADE的学习网络与个性化学习系统.系统为每个学习者创建一个JADE代理,用以动态监控学习行为并实现感兴趣资源的共享、推荐和评估,同时基于其他学习者代理对不同资源的感兴趣程度,通过发现相似性、更新信任权值和调整潜在邻居等方法,动态调整学习者之间的信任关系,构建学习网络,为远程学习者提供更准确地学习资源推荐.实验结果表明系统可以非常迅速的将具有相同兴趣的学习者聚合在一起,并很好的满足他们的查询、推荐需求.     

个性化在线学习系统研究

该文针对目前在线学习系统不能为学习者提供个性化学习资源的不足,该文设计了一个利用用户行为数据表征其个性化特征的个性化在线学习系统。该系统能为根据不同学习者的不同需求为学习者提供个性化学习资源。     

基于行为序列分析的学习资源推荐算法研究

利用数据挖掘技术分析网络学习行为数据可以挖掘出其隐含的行为规律特征,为学习者提供个性化的学习资源服务。针对现有的数据挖掘算法在对网络学习行为数据进行分析时普遍存在模型适用性不高的问题,提出了一种基于行为序列分析的学习资源推荐算法。首先,提出行为序列及其相关概念的定义,并提出行为序列相似度计算方法;然后提出基于行为序列相似度的协同过滤推荐算法,计算学习者相似度并为待推荐学习者生成学习资源推荐列表;接着给出基于学习风格的推荐方法,将学习者学习风格特征融入推荐过程;最后,给出基于行为序列分析的学习资源推荐算法的模型。提出的算法没有对行为序列的模式进行限制,具有较高的适用性,对深入研究网络学习行为序列数据为学习者提供个性化学习服务具有一定的借鉴作用。     

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’.     

一种面向学习路径推荐的知识网络构建方法

随着在线教育的迅速发展,互联网上的教学资源数量也呈现出快速增长的趋势。针对当前在线学习平台普遍存在着教学资源内容重复、人们难以辨别与选择,导致学习者很难应用这些资源构建适合自己的学习路径的问题,提出一种面向学习路径推荐的领域知识网络构建方法。通过对每个学习对象的预备知识与目标知识进行社会标注,构建相应的领域知识网络,然后,运用弗洛伊德算法计算领域知识网络里任意两个知识点间的最短路径,为学习路径推荐提供基础。     

Collaborative recommendation of e‐learning resources: an experimental investigation

Repositories with educational resources can support the formation of online learning communities by providing a platform for collaboration. Users (e.g. teachers, tutors and learners) access repositories, search for interesting resources to access and use, and in many cases, also exchange experiences and opinions. A particular class of online services that take advantage of the collected knowledge and experience of users are collaborative filtering ones. The successful operation of such services in the context of real‐life applications requires careful testing and parameterization before their actual deployment. In this paper, the case of developing a learning resources' collaborative filtering service for an online community of teachers in Europe was examined. More specifically, a data set of evaluations of learning resources was collected from the teachers that use the European Schoolnet's learning resource portal. These evaluations were then used to support the experimental investigation of design choices for an online collaborative filtering service for the portal's learning resources. A candidate multi‐attribute utility collaborative filtering algorithm was appropriately parameterized and tested for this purpose. Results indicated that the development of such systems should be taking place considering the particularities of the actual communities that are to be served.     

Intelligent web-based learning system with personalized learning path guidance

Personalized curriculum sequencing is an important research issue for web-based learning systems because no fixed learning paths will be appropriate for all learners. Therefore, many researchers focused on developing e-learning systems with personalized learning mechanisms to assist on-line web-based learning and adaptively provide learning paths in order to promote the learning performance of individual learners. However, most personalized e-learning systems usually neglect to consider if learner ability and the difficulty level of the recommended courseware are matched to each other while performing personalized learning services. Moreover, the problem of concept continuity of learning paths also needs to be considered while implementing personalized curriculum sequencing because smooth learning paths enhance the linked strength between learning concepts. Generally, inappropriate courseware leads to learner cognitive overload or disorientation during learning processes, thus reducing learning performance. Therefore, compared to the freely browsing learning mode without any personalized learning path guidance used in most web-based learning systems, this paper assesses whether the proposed genetic-based personalized e-learning system, which can generate appropriate learning paths according to the incorrect testing responses of an individual learner in a pre-test, provides benefits in terms of learning performance promotion while learning. Based on the results of pre-test, the proposed genetic-based personalized e-learning system can conduct personalized curriculum sequencing through simultaneously considering courseware difficulty level and the concept continuity of learning paths to support web-based learning. Experimental results indicated that applying the proposed genetic-based personalized e-learning system for web-based learning is superior to the freely browsing learning mode because of high quality and concise learning path for individual learners.     

A recommender agent based on learning styles for better virtual collaborative learning experiences

Almost unlimited access to educational information plethora came with a drawback: finding meaningful material is not a straightforward task anymore. Based on a survey related to how students find additional bibliographical resources for university courses, we concluded there is a strong need for recommended learning materials, for specialized online search and for personalized learning tools. As a result, we developed an educational collaborative filtering recommender agent, with an integrated learning style finder. The agent produces two types of recommendations: suggestions and shortcuts for learning materials and learning tools, helping the learner to better navigate through educational resources. Shortcuts are created taking into account only the user’s profile, while suggestions are created using the choices made by the learners with similar learning styles. The learning style finder assigns to each user a profile model, taking into account an index of learning styles, as well as patterns discovered in the virtual behavior of the user. The current study presents the agent itself, as well as its integration to a virtual collaborative learning environment and its success and limitations, based on users’ feedback.     

基于多维信息特征映射模型的在线学习路径优化方法

针对目前在线学习路径优化方法存在学习路径与学习者匹配度不高的问题,首先构建在线学习路径的多维信息特征映射模型(MIFMM),该模型根据学习者与学习资源的多维信息特征建立,融合了kolb学习风格和学习资源类型信息;然后设计双映射二进制粒子群优化算法(DMBPSO),DMBPSO算法根据进化因子ef将学习路径推荐过程分为收敛和跳出局部最优两种进化状态,采用与进化状态特征相匹配的映射函数选择策略,并对惯性权重进行动态非线性调整,提高学习路径推荐性能;接着将MIFMM模型与DMBPSO算法相融合提出基于多维信息特征映射模型的在线学习路径优化方法(MIFMM-POA);最后将MIFMM-POA方法与其他4种粒子群算法为核心的学习路径优化方法相比较,从寻优精度、寻优过程与寻优时间3个角度进行分析,实验表明MIFMM-POA方法是优化学习路径的有效方法.     

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。     

利用学习向量化样本分类的在线学习成绩预测

对网络在线学习者产生的数据进行记录和分析,并为其提供精准化的个性化服务是在线教育发展的重要方面.本文以学习者在平台上产生的日常学习数据为样本,综合其最具代表性的五种影响因子,通过学习向量化神经网络对样本进行分类,得到基于BP神经网络的在线学习成绩预测数据.在模型中采用遗传算法有效优化BP神经网络的权重和阈值,在提高预测精度的同时加快模型的收敛速度.最后与其他两种模型进行对比分析,结果表明：该模型进行预测的结果与真实的成绩分布基本一致,具有很高的可信度,能够为有效的预测学习状态提供决策依据,具有一定的工程应用价值.     

可重用、个性化网络课程的设计与实现

学习对象技术是关于可重用网络课程内容开发的相关理论,SCORM标准给出了其技术实现框架,在此基础上,本丈结合用户模型的建立,设计了一个以可重用学习单元为核心构成适应学习者个人需求的网络课程内容定制系统,根据用户要求和已经具备的背景知识,系统能够检索到适应用户学习水平的学习单元,形成既可重用、又具有个性化的网络课程学习内容。