融合行为和遗忘因素的贝叶斯知识追踪模型研究

贝叶斯知识追踪模型(Bayesian knowledge tracing,BKT)被用于智能教学系统中追踪学习者的知识状态并预测其掌握水平和未来表现.由于BKT容易忽视记忆遗忘现象,以及未考虑学习行为对表现结果产生的影响,导致模型预测结果与实际情况出现偏差.针对此问题,提出了一种融合学习者的行为和遗忘因素的贝叶斯知识追踪模型(behavior-forgetting Bayesian knowledge tracing,BF-BKT).首先,采用决策树算法处理学习行为数据,引入行为节点;然后初始化遗忘参数并赋值,更新学习者知识掌握水平的算法;最后,利用ASSISTMENTS提供的公开数据集对相关模型的预测精度进行对比.实验验证,BF-BKT能够达到更好的预测精度.     

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.     

基于更新样本智能识别算法的自适应集成建模

选择表征建模对象特性漂移的新样本对软测量模型进行自适应更新,能够降低模型复杂度和运行消耗,提高模型可解释性和预测精度.针对新样本近似线性依靠程度（Approximate linear dependence, ALD）和预测误差（Prediction error, PE）等指标只能片面反映建模对象的漂移程度,领域专家结合具体工业过程需要依据上述指标和自身积累经验进行更新样本的有效识别等问题,本文提出了基于更新样本智能识别算法的自适应集成建模策略.首先,基于历史数据离线建立基于改进随机向量泛函连接网络（Improved random vector functional-link networks, IRVFL）的选择性集成模型;然后,基于集成子模型对新样本进行预测输出后采用在线自适应加权算法（On-line adaptive weighting fusion, OLAWF）对集成子模型权重进行更新,实现在线测量阶段对建模对象特性变化的动态自适应;接着基于领域专家知识构建模糊推理模型对新样本相对ALD（Relative ALD, RALD）值和相对PE（Relative PE, RPE）值进行融合,实现更新样本智能识别,构建新的建模样本库;最后实现集成模型的在线自适应更新.采用合成数据仿真验证了所提算法的合理性和有效性.     

小世界网络的知识转移行为仿真分析

构建基于小世界网络模型的知识转移网络仿真模型,用平均路径长度和聚类系数来表征知识网络节点间的交流频率与聚集程度,分析知识网络节点的知识释放、吸收能力及信任程度对网络知识转移效率的影响。仿真结果表明,在具有小世界网络特征的知识转移网络中,提高网络节点的交流频率和集聚程度,增强节点知识转移的行为能力,能够保证网络组织知识转移效率达到较高水平。     

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。     

基于网格资源管理的协作在线学习研究

随着计算机技术的飞速发展,在线学习在世界各地迅速普及。在线学习就相当于学习者通过网络寻找学习资源,并选择适当的学习资源来学习,教师和教授的作用是学习的指导者,而不是传统知识的传播者。然而,尽管有众多的可用的在线资源,学习过程中的重要元素如参与与协作通过当前的网络技术并未实现。本文提出了一种基于网格的资源管理模型,该模型主要用于创建协作的在线学习环境。该模型提出一种有效的可交互操作的方式来帮助学习者发现,存储,提取,保护和管理在线学习资源。模型主要使用如下技术:Linux,Apache,MySQL。     

ITS中知识点的设计与教学策略推理

根据实际教学中知识内容之间的关系,将ITS中知识点构建为网状结构,并使用带有权值的邻接矩阵表示,同时将模糊数学中隶属函数引入到对学习者学习程度的判断中,在此基础上使用队列和递归算法实现教学策略的推理.     

基于Hebbian法则的P2P学习网络构建算法

针对远程教育环境中信息量过大,学习者分散、难于组织的问题,提出了一种E-Learning学习网络构建算法.该算法采用P2P的网络结构组织节点之间的通信,利用Hebbian学习法则来修改网络节点之间的信任权值,通过不断调整节点的联系人来实现学习网络的构建.学习网络能够对学习者关于学习资源的查询做出符合学习者学习兴趣的反馈,从而最大限度地满足学习者的需求,提高学习者的学习满意度.实验结果表明,算法相比于传统算法,具有较快的网络建设速度、较高的网络建设质量以及较高的鲁棒性,因而能够更好地适用于大规模的E-Learning学习这种开放的分布式环境.     

ITS中知识点的设计与教策略推理

根据实际教学中知识内容之间的关系。将ITS中知识点构建为网状结构，并使用带有权值的邻接矩阵表示．同时将模糊数学中隶属函数引入到对学习者学习程度的判断中。在此基础上使用队列和递归算法实现教学策略的推理。     

基于宠物游戏的智能教学系统

通过利用游戏宠物扮演各种教育角色,扩展智能教学系统(intelligent tutoring system,ITS)中的学生模型,从而完成宠物游戏与ITS的有机结合.设计了一个基于宠物游戏的游戏化智能教学系统架构,架构中的宠物模块能记录学习者的学习活动、分析学习者的学习进程.基于该软件架构,实现了一款游戏化智能教学系统--宠物星际旅行,将宠物游戏的游戏化特性引入到了小学分数知识的学习过程中.测试结果表明,学生对该游戏越专注,就能越快地理解和运用所学知识.     

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.     

I-Quiz: An Intelligent Assessment Tool for Non-Verbal Behaviour Detection

Electronic learning (e-learning) has become one of the widely used modes of pedagogy in higher education today due to the convenience and flexibility offered in comparison to traditional learning activities. Advancements in Information and Communication Technology have eased learner connectivity online and enabled access to an extensive range of learning materials on the World Wide Web. Post covid-19 pandemic, online learning has become the most essential and inevitable medium of learning in primary, secondary and higher education. In recent times, Massive Open Online Courses (MOOCs) have transformed the current education strategy by offering a technology-rich and flexible form of online learning. A key component to assess the learner’s progress and effectiveness of online teaching is the Multiple Choice Question (MCQ) assessment in most of the MOOC courses. Uncertainty exists on the reliability and validity of the assessment component as it raises a qualm whether the real knowledge acquisition level reflects upon the assessment score. This is due to the possibility of random and smart guesses, learners can attempt, as MCQ assessments are more vulnerable than essay type assessments. This paper presents the architecture, development, evaluation of the I-Quiz system, an intelligent assessment tool, which captures and analyses both the implicit and explicit non-verbal behaviour of learner and provides insights about the learner’s real knowledge acquisition level. The I-Quiz system uses an innovative way to analyse the learner non-verbal behaviour and trains the agent using machine learning techniques. The intelligent agent in the system evaluates and predicts the real knowledge acquisition level of learners. A total of 500 undergraduate engineering students were asked to attend an on-Screen MCQ assessment test using the I-Quiz system comprising 20 multiple choice questions related to advanced C programming. The non-verbal behaviour of the learner is recorded using a front-facing camera during the entire assessment period. The resultant dataset of non-verbal behaviour and question-answer scores is used to train the random forest classifier model to predict the real knowledge acquisition level of the learner. The trained model after hyperparameter tuning and cross validation achieved a normalized prediction accuracy of 85.68%.     

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.     

基于自注意力机制和双向GRU神经网络的深度知识追踪优化模型

针对现有深度知识追踪模型存在输入习题间复杂关系捕获能力弱、无法有效处理长序列输入数据等问题,提出了基于自注意力机制和双向GRU神经网络的深度知识追踪优化模型(KTSA-BiGRU)。首先,将学习者的历史学习交互序列数据映射为实值向量序列;其次,以实值向量序列作为输入训练双向GRU神经网络,利用双向GRU神经网络建模学习者的学习过程;最后,使用自注意力机制捕获练习题之间的关系,根据双向GRU神经网络输出的隐向量和注意力权重计算学习者正确回答下一问题的概率。实验在三个公共数据集上的性能分析优于现有的知识追踪模型,能提高深度知识追踪的预测精度。     

Enhancing e-learning systems with personalized recommendation based on collaborative tagging techniques

Personalization of the e-learning systems according to the learner’s needs and knowledge level presents the key element in a learning process. E-learning systems with personalized recommendations should adapt the learning experience according to the goals of the individual learner. Aiming to facilitate personalization of a learning content, various kinds of techniques can be applied. Collaborative and social tagging techniques could be useful for enhancing recommendation of learning resources. In this paper, we analyze the suitability of different techniques for applying tag-based recommendations in e-learning environments. The most appropriate model ranking, based on tensor factorization technique, has been modified to gain the most efficient recommendation results. We propose reducing tag space with clustering technique based on learning style model, in order to improve execution time and decrease memory requirements, while preserving the quality of the recommendations. Such reduced model for providing tag-based recommendations has been used and evaluated in a programming tutoring system.     

Student Behavior Modeling for an E-Learning System Offering Personalized Learning Experiences

With the advent of computing and communication technologies, it has become possible for a learner to expand his or her knowledge irrespective of the place and time. Web-based learning promotes active and independent learning. Large scale e-learning platforms revolutionized the concept of studying and it also paved the way for innovative and effective teaching-learning process. This digital learning improves the quality of teaching and also promotes educational equity. However, the challenges in e-learning platforms include dissimilarities in learner’s ability and needs, lack of student motivation towards learning activities and provision for adaptive learning environment. The quality of learning can be enhanced by analyzing the online learner’s behavioral characteristics and their application of intelligent instructional strategy. It is not possible to identify the difficulties faced during the process through evaluation after the completion of e-learning course. It is thus essential for an e-learning system to include component offering adaptive control of learning and maintain user’s interest level. In this research work, a framework is proposed to analyze the behavior of online learners and motivate the students towards the learning process accordingly so as to increase the rate of learner’s objective attainment. Catering to the demands of e-learner, an intelligent model is presented in this study for e-learning system that apply supervised machine learning algorithm. An adaptive e-learning system suits every category of learner, improves the learner’s performance and paves way for offering personalized learning experiences.     

基于稀疏表示剪枝集成建模的烧结终点位置智能预测

烧结终点位置(BTP)是烧结过程至关重要的参数, 直接决定着最终烧结矿的质量. 由于BTP难以直接在线 检测, 因此, 通过智能学习建模来实现BTP的在线预测并在此基础上进行操作参数调节对提高烧结矿质量具有重要 意义. 针对这一实际工程问题, 首先提出一种基于遗传优化的Wrapper特征选择方法, 可选取使后续预测建模性能最 优的特征组合; 在此基础上, 为了解决单一学习器容易过拟合的问题, 提出了基于随机权神经网络(RVFLNs)的稀疏 表示剪枝(SRP)集成建模算法, 即SRP-ERVFLNs算法. 所提算法采用建模速度快、泛化性能好的RVFLNs 作为个体 基学习器, 采用对基学习器基函数与隐层节点数等参数进行扰动的方式来增加集成学习子模型间的差异性; 同时, 为了进一步提高集成模型的泛化性能与计算效率, 引入稀疏表示剪枝算法, 实现对集成模型的高效剪枝; 最后, 将所 提算法用于烧结过程BTP的预测建模. 工业数据实验表明, 所提方法相比于其他方法具有更好的预测精度、泛化性 能和计算效率.     

基于表示学习的在线学习交互质量评价方法

当今的教育模式发生着非常重大的变革,教育正在向泛在化、智能化、个性化的方向发展。以Massive Open Online Courses(MOOCs)为代表的在线教育逐渐进入大众视野,在线教育中的交互性成为了决定在线学习质量的关键。研究表明,学习过程中的交互为学习者提供了有效且高效的帮助和支持,对学习过程的评价反馈可以有效地提高学习效果。在教育领域,对学习者和学习资源之间的交互进行建模至关重要,表示学习技术为学习者和学习资源之间的顺序交互建模提供了具体方案。文中首先建立在线学习的交互网络模型,然后使用两个循环神经网络将网络中的学习者和学习资源节点嵌入到一个欧氏空间中,并提出交互质量评价指标,以判断学习者的学习效果是否达到预期。在实际数据集上的实验证明了所提方法的有效性。     

Evaluating the integration of fuzzy logic into the student model of a web-based learning environment

In this paper, we evaluate the effectiveness and accuracy of the student model of a web-based educational environment for teaching computer programming. Our student model represents the learner’s knowledge through an overlay model and uses a fuzzy logic technique in order to define and update the student’s knowledge level of each domain concept, each time that s/he interacts with the e-learning system. Evaluation of the student model of an Intelligent Tutoring System (ITS) is an aspect for which there are not clear guidelines to be provided by literature. Therefore, we choose to use two well-known evaluation methods for the evaluation of our fuzzy student model, in order to design an accurate and correct evaluation methodology. These evaluation models are: the Kirkpatrick’s model and the layered evaluation method. Our system was used by the students of a postgraduate program in the field of Informatics in the University of Piraeus, in order to learn how to program in the programming language C. The results of the evaluation were very encouraging.