基于学习者画像的数据库课程教学改革研究

针对现有数据库基础课程教学无法满足学生多样化需求为背景,本文引入问卷调查获取学生个性化特征,根据学习者的基本信息、行为数据、态度数据以及学习结果对其进行画像,将具有相同学习行为的学生形成群体,按学生个人能力和需求给予个性化教学,并及时对学生学习过程进行干预,设计形成以人为本的差异性和多元化数据库课程教学新模式。     

基于学习者行为特征的MOOCs学习伙伴推荐

大规模开放在线课堂的兴起给高等教育的全球化提供了契机。与大规模参与用户形成鲜明对比的是,目前课程普遍存在完成率不足的问题。造成学生退出的原因之一是学习者缺乏合适的学习伙伴相互交流以及时解决学习中遇到的问题。分析了开放课程中论坛用户的身份特征和学生用户在论坛讨论过程中的行为模式,建立了学习者行为特征模型和学生在讨论过程中形成的关系网络。根据课程内容建立关键词词典,并以此为主题词,提出了一种具有固定主题词的主题模型,进而推断关系网络潜在的主题分布,最终根据主题分布结果为学习者推荐学习伙伴。通过分析Coursera课程平台的真实数据,证明了该学习伙伴推荐方法能有效地挖掘出主题相关的学习者,为学习者相互推荐学习伙伴,在一定程度上将有助于提高学习者的积极性。     

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。     

MOOC课程论坛中学习者论坛交互网络结构特征分析

分析学习者在课程论坛中的交流互动行为记录,根据不同的行为特征对学习者进行分类并进一步构建一个学习者之间的交流互动关系网络,利用社会网络分析方法阐述节点用户的网络结构特征。以Coursera平台的课程论坛数据进行实验分析,指出当前MOOC平台的课程论坛中,用户参与度的提高对促进学习者更好地完成课程学习具有积极的作用,但当前MOOC课程论坛的用户参与度还有待进一步提高。     

基于多任务自编码器的MOOC课程推荐模型

为解决在线学习当中,学习者行为的数量远少于在线课程的样本总数所产生的数据稀疏问题,提出一种基于多任务自编码器的课程推荐模型（multi-task autoencoder course recommendation model,MAEM）。通过分析学习者的学习行为,将总体任务分为两个子任务：任务一是学习者浏览课程章节列表行为,任务二是完成课程50%的学习行为,通过共享网络底部的隐藏层提高泛化能力。模型总体划分为共享嵌入、自编码器与分解预测、任务组合3个模块,3个模块协同工作,旨在突破训练数据稀疏问题。将MAEM与7种常用的推荐算法比较,实验结果表明,MAEM算法优于7种热门的推荐算法,验证了其在课程推荐中的有效性。     

基于大数据分析的计算机网络课程自适应学习系统构建

大数据时代数字化的学习资源将呈现爆炸式的增长,面对海量的学习资源,学习者如何选择,或者如何向学习者推送合适的学习资源将成为未来面临的主要问题。针对计算机网络课程授课内容多、知识更新快的问题,通过收集教育过程中的学习行为数据,利用大数据的学习分析技术构建了一个计算机网络自适应学习系统,该系统包含自适应、预测、干预等六大模块,可以针对性地推送学习内容,及时反馈学习者的学习效果,并推荐下一步的学习策略,从而达到因材施教和培养学生自主学习能力的效果。     

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。     

数据驱动的SQLTutor的设计与实现

本研究以当前SQL语言教学中存在的问题与突破点为出发点,以学习者学习行为数据获取为目标设计、开发了具有测评与知识点学习等功能的SQLTutor平台(SQL查询语言智能学习系统),并通过相关课程进行验证与分析,以期利用数据驱动的SQLTutor可以有效地提升学生学习效率。     

大规模网络课程中学习者学习行为分析

针对MOOC平台上学习者学习状况不易把握的现状,阐述在MOOC平台中对学习者进行学习行为分析的重要性,依托辽宁省精品资源共享课程平台,提出学习者学习行为属性模型,以模型是展开对学习者学习行为分析的展开研究,便于任课教师掌握学习者的学习情况,并以此为依据调整课程组织和教学方式,从而达到最优的教学效果。     

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。     

大数据思维下的网络教学平台的数据驱动研究

网络教学平台的应用可为学习者提供丰富的资源,但是对于教师或学习者从现有网络教学平台获得的数据使 用是静态的,平台缺乏个性化学习指导。通过将大数据思维引入网络教学平台,可以更好地理解学习者的多样性和每个学习 者的真实情况。本文分析了现有网络教学平台的一些缺点,并讨论了如何利用“数据驱动”为学习者提供优质,个性化的教育 资源。     

网络教学环境下学生学习行为分析

本文针对在线教学平台的实际使用情况进行研究,通过查阅国内外网络学习行为的相关文献,明确网络学习行为的概念、网络学习行为模型构建的方法和技术。以徐州工程学院教学平台《计算机组成原理》为例,搜集网络学习环境下学习者学习过程中的各种学习行为数据,通过实证研究展示大学生网络学习的真实情况,从中发现大学生网络学习行为存在的问题,并针对这些问题提出相应的对策建议。     

智慧教育推动教学创新变革的研究

随着互联网+时代的到来,数字化教育将会向智慧教育的方向发展,智慧教育将会变革和创新以往的学习方 式、教学方式和评价方式,智慧教育为学习者提供了个性化的混合式教育模式。本文介绍了我院“计算机网络”课程在思科网 院、百度云课堂、Blackboard 系统中开展在线智慧教学的相关实践,特别指出了借助智能化的学习分析技术来收集、处理教学 数据,分析评估学习者的学习行为并进行必要的干涉,以便及时提高和改进教学效果。随着系统中教学数据的不断丰富,可借 助大数据分析平台进行进一步地分析以促进智慧学习。     

Assessing metacognitive knowledge in web-based CALL: a neural network approach

The assessment of learners’ metacognitive knowledge level is crucial when developing computer-assisted language learning systems. Currently, many systems assess learners’ metacognitive knowledge level with pre-instructional questionnaires or metacognitive interviews. However, learners with limited language proficiency may be at a disadvantage in responding to verbal-report interview or questionnaire probes. The goal of this study is to present a neural network model that assesses automatically the learner’s metacognitive knowledge level by observing his/her online browsing behavior. The model is implemented through a multi-layer feed forward neural network. An experiment was conducted to examine the suitability of this model in different Web page structures. One hundred and forty-six college students were categorized into three groups according to three Web page structures: networked, hierarchical, and linear. The experiment results verified the suitability of the proposed model, and the MSEs of assessment of the three groups showed no significant differences with respect to the Web page structures.     

Towards an Online Continuous Adaptation Mechanism (OCAM) for Enhanced Engagement: An EEG Study

ABSTRACT

Individual preferences for learning environments can be linked to a specific behavior. The tendency of such behavior can somehow be associated with an individual’s ability to cognitively engage in the learning process without being distracted by other stimuli. An online continuous adaptation mechanism (OCAM) of learning contents was developed in order to regulate the presentation of learning contents based on changes in the learner’s aptitude level. This was claimed to stimulate a better cognitive and emotional response among learners, thus stimulating their engagement. A total of 41 students (36 male and 5 female; age 20–25 years) participated in this study. The results revealed that learners’ levels of concentration and cognitive load were positively influenced by the OCAM, which significantly increased their engagement. Our findings can be used to inform designers and developers of online learning systems about the importance of regulating the presentation of learning contents according to the aptitude level of individual learners. The proposed OCAM can improve learners’ ability to process specific information meaningfully and make the inferences necessary for understanding the learning content.     

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.     

Integrating cluster and sequential analysis to explore learners’ flow and behavioral patterns in a simulation game with situated-learning context for science courses: A video-based process exploration

Much emphasis has been placed on the research on applying digital games in science education. Among the studies, the advantages and limitations of role-playing simulation games deserve further exploration. However, existing analyses of the behavioral patterns of role-playing simulation games in science education remain substantially lacking, particularly the integration of diverse behavioral pattern analysis methods. This study thus seeks to analyze the videotaped learning process of 86 college students in game-based learning activities that utilize a role-playing simulation game. This study used the integrated method of sequential analysis and cluster analysis and explored the learners’ flow state and learning behavioral patterns. The results show that the use of integrated behavioral pattern analysis helps to explore the traits and limitations of role-playing simulation games in science education as well as learners’ reflective behavior patterns.This study identifies a wide variety of learning behavior patterns from three potential clusters of learners and then discusses the learning process of each cluster. The different levels of flow experienced by the learners affected their learning behavior patterns; learners with higher levels of flow demonstrated a more in-depth reflective process. The study further discusses the results of these analyses and makes relevant recommendations for the systems development of the games, its educational applications, and evaluation methods.     

基于网络学习平台的学习行为监控研究

本文在对网络学习行为信息的采集分析的基础上,提出了学习者群体及个体网络学习行为的实时监控的方法并进行实践,以期对网络教育教学提供必要依据。