Personalized E-learning system with self-regulated learning assisted mechanisms for promoting learning performance

With the rapid development of Internet technologies, the conventional computer-assisted learning (CAL) is gradually moving toward to web-based learning. Additionally, instructors typically base their teaching methods to simultaneously interact with all learners in a class based on their professional disciplines in the traditional classroom learning. However, the requirements of individual learners are frequently ignored in the traditional classroom learning. Compared to the conventional classroom learning, individual learners are the focus in web-based learning environments and many web-based learning systems provide personalized learning mechanisms for individual learners. One key problem is that learners have to frequently interact with web-based learning systems even though they lack instructors to monitor their learning attitudes and behavior during learning processes. Hence, a learner’s ability to self-regulated learning is clearly an important factor affecting learning performance in a web-based learning environment. Self-regulated learning is a goal-oriented learning strategy that is very suited to self-managed learning to promote learning performance of individual learners in a web-based learning environment. However, how to assist learners in cultivating self-regulated learning abilities efficiently is an important research issue in the self-regulated learning field. This study presents a novel personalized e-learning system with self-regulated learning assisted mechanisms that help learners enhance their self-regulated learning abilities. The proposed self-regulated learning mechanisms assist learners in becoming lifelong learners who have autonomous self-regulated learning abilities. Additionally, four self-regulated learning types, based on a self-regulated learning competence index and self-regulated learning performance index, are also proposed. Experimental results demonstrate that the proposed self-regulated learning assisted mechanisms aid learners by speeding up their acquisition of self-regulated learning abilities in a personalized e-learning system, and help their learning performance.     

基于移动学习的学科主题学习资源设计研究

移动学习作为一种新型的学习方式正成为研究热点,而基于移动学习的学科主题学习资源相对缺乏。本文阐述了移动学习的概念及特点、主题学习、学科主题学习资源的理论基础,分析了基于移动学习的学科主题学习资源设计的基本原则,最后构建了基于移动学习的学科主题学习资源的设计框架。     

A theoretical comparison of batch-mode, on-line, cyclic, andalmost-cyclic learning

We study and compare different neural network learning strategies: batch-mode learning, online learning, cyclic learning, and almost-cyclic learning. Incremental learning strategies require less storage capacity than batch-mode learning. However, due to the arbitrariness in the presentation order of the training patterns, incremental learning is a stochastic process; whereas batch-mode learning is deterministic. In zeroth order, i.e., as the learning parameter eta tends to zero, all learning strategies approximate the same ordinary differential equation for convenience referred to as the "ideal behavior". Using stochastic methods valid for small learning parameters eta, we derive differential equations describing the evolution of the lowest-order deviations from this ideal behavior. We compute how the asymptotic misadjustment, measuring the average asymptotic distance from a stable fixed point of the ideal behavior, scales as a function of the learning parameter and the number of training patterns. Knowing the asymptotic misadjustment, we calculate the typical number of learning steps necessary to generate a weight within order epsilon of this fixed point, both with fixed and time-dependent learning parameters. We conclude that almost-cyclic learning (learning with random cycles) is a better alternative for batch-mode learning than cyclic learning (learning with a fixed cycle).     

The relationship of learning traits,motivation and performance-learning response dynamics

This paper proposes a model of learning dynamics and learning energy, one that analyzes learning systems scientifically. This model makes response to the learner action by means of some equations relating to learning dynamics, learning energy, learning speed, learning force, and learning acceleration, which is analogous to the notion of Newtonian mechanics in some way; therefore, this model is named Learning Response Dynamics. First, in this paper, the relationship between learning dynamics and learning speed has been investigated in a learning system, and then the changes of learning energy are inferred from the relationships obtained. The learning effect is estimated according to the changes of the learning energy. Based on the learning portfolios of the learners, the model is designed to investigate the changes of learning speed over time. Various dynamics will influence the learning speed. These dynamics include the traits of the learners, the traits of the learning materials, and the stimulation of the learning activities. How to use different dynamics to motivate the learners is crucial to the success of learning. This model converts the factors in a learning system to quantified and comprehensible data, deducing the relationships between those factors. It makes the study of the learning system more efficient and scientific. With the experience of the two-year ongoing experiments on distance learning, and with the learning information discovered from the web-based-distance-class learners' learning portfolios by means of data mining techniques, the learning model mentioned above is inferred, tested and verified.     

基于非联合型学习机制的学习神经元模型

针对生物神经细胞所具有的非联合型学习机制,设计了具有非联合型学习机制的新型神经元模型——学习神经元。首先,研究了非联合型学习机制中习惯化学习机制和去习惯化学习机制的简化描述;其次,建立了习惯化和去习惯化学习机制的数学模型;最后,基于经典的M-P（McCulloch-Pitts）神经元模型,提出了具有习惯化和去习惯化学习能力的新型神经元模型——学习神经元。经仿真实验验证,学习神经元具有典型的习惯化和去习惯化学习能力,为构建新型神经网络提供良好的基础。     

Mobile formative assessment tool based on data mining techniques for supporting web-based learning

Current trends clearly indicate that online learning has become an important learning mode. However, no effective assessment mechanism for learning performance yet exists for e-learning systems. Learning performance assessment aims to evaluate what learners learned during the learning process. Traditional summative evaluation only considers final learning outcomes, without concerning the learning processes of learners. With the evolution of learning technology, the use of learning portfolios in a web-based learning environment can be beneficially adopted to record the procedure of the learning, which evaluates the learning performances of learners and produces feedback information to learners in ways that enhance their learning. Accordingly, this study presents a mobile formative assessment tool using data mining, which involves six computational intelligence theories, i.e. statistic correlation analysis, fuzzy clustering analysis, grey relational analysis, K-means clustering, fuzzy association rule mining and fuzzy inference, in order to identify the key formative assessment rules according to the web-based learning portfolios of an individual learner for the performance promotion of web-based learning. Restated, the proposed method can help teachers to precisely assess the learning performance of individual learner utilizing only the learning portfolios in a web-based learning environment. Hence, teachers can devote themselves to teaching and designing courseware, since they save a lot of time in measuring learning performance. More importantly, teachers can understand the main factors influencing learning performance in a web-based learning environment based on the interpretable learning performance assessment rules obtained. Experimental results indicate that the evaluation results of the proposed scheme are very close to those of summative assessment results and the factor analysis provides simple and clear learning performance assessment rules. Furthermore, the proposed learning feedback with formative assessment can clearly promote the learning performances and interests of learners.     

Technology-assisted learning: a longitudinal field study of knowledge category, learning effectiveness and satisfaction in language learning

Abstract   A field experiment compares the effectiveness and satisfaction associated with technology-assisted learning with that of face-to-face learning. The empirical evidence suggests that technology-assisted learning effectiveness depends on the target knowledge category. Building on Kolb's experiential learning model, we show that technology-assisted learning improves students' acquisition of knowledge that demands abstract conceptualization and reflective observation but adversely affects their ability to obtain knowledge that requires concrete experience. Technology-assisted learning better supports vocabulary learning than face-to-face learning but is comparatively less effective in developing listening comprehension skills. In addition, according to empirical tests, perceived ease of learning and learning community support significantly predict both perceived learning effectiveness and learning satisfaction. Overall, the results support our hypotheses and research model and suggest instructors should consider the target knowledge when considering technology-assisted learning options or designing a Web-based course. In addition, a supportive learning community can make technology-assisted learning easier for students and increase their learning satisfaction.     

The design and implementation of a competency-based intelligent mobile learning system

Mobile learning provides a ubiquitous learning context for the learners to select appropriate learning paths and learning objects. Adaptive learning methods and correct learning path planning can help to achieve the goal of learning anytime and anywhere. Moreover, the display ability of mobile learning devices has become a key factor affecting the interest and acquisition time of learners. Achieving the desired functionality is currently an important topic in the field of mobile learning. This paper uses competency-based learning as the basis to evaluate the knowledge deficiency that the learner must overcome. We then use carrier selection, fuzzy interpolation computation, and ant-genetic algorithm techniques to select the appropriate learning paths and objects. Finally, we use NFC’s point-to-point technology to transfer the learning content in the learning device to a larger screen with NFC capability in the user’s environment to display the same content, thus providing a complete learning system.     

浅谈机器学习方法简

本文以什么是机器学习、机器学习的发展历史和机器学习的主要策略这一线索,对机器学习进行系统性的描述。接着,着重介绍了流形学习、李群机器学习和核机器学习三种新型的机器学习方法,为更好的研究机器学习提供了新的思路。     

迁移学习在医学图像分析中的应用研究综述

迁移学习是机器学习中一种新的学习范式,它可以克服深度学习需要大量样本的缺陷,能够解决医学图像分析中数据集较小导致模型不准确的问题,因而成为继深度学习之后在医学图像分析领域的研究热点。对迁移学习进行概要阐述,按照目前医学图像分析中应用的主要迁移学习方法,即基于数据的迁移学习、基于模型的迁移学习、对抗式迁移学习和混合迁移学习,对医学图像分析领域的重要文献进行整理和归纳,分析每种迁移学习的机制、适用范围、应用情况和优缺点,再对这几种迁移学习方法进行总结、分析及比较。针对研究现状的不足指出该领域的研究发展趋势,为迁移学习在该领域的深入研究提供参考。     

学习控制技术·方法应用的发展新动向

分析和概述了当前学习控制系统所采用的技术、学习方法及应用的发展新动向 .从所采用的技术来看 ,学习控制正在从采用单一的技术向采用混合技术的方向发展 ;从学习方法和应用来看 ,学习控制正在从采用较为简单的参数学习向采用较为复杂的结构学习、环境学习和复杂对象学习的方向发展     

半监督集成学习综述

半监督学习和集成学习是目前机器学习领域中两个非常重要的研究方向,半监督学习注重利用有标记样本与无标记样本来获得高性能分类器,而集成学习旨在利用多个学习器进行集成以提升弱学习器的精度。半监督集成学习是将半监督学习和集成学习进行组合来提升分类器泛化性能的机器学习新方法。首先,在分析半监督集成学习发展过程的基础上,发现半监督集成学习起源于基于分歧的半监督学习方法;然后,综合分析现有半监督集成学习方法,将其分为基于半监督的集成学习与基于集成的半监督学习两大类,并对主要的半监督集成方法进行了介绍;最后,对现有研究进了总结,并讨论了未来值得研究的问题。     

本体在E learning系统中的应用研究*

探讨了Elearning及本体,提出了基于本体的Elearning系统层次结构模型,并重点研究了本体在其中的应用：用于描述学习材料语义的内容本体,用于定义学习材料上下文的上下文本体以及用于在学习课程中组织学习材料的结构本体。     

Personalized mobile English vocabulary learning system based on item response theory and learning memory cycle

Since learning English is very popular in non-English speaking countries, developing modern assisted-learning tools that support effective English learning is a critical issue in the English-language education field. Learning English involves memorization and practice of a large number of vocabulary words and numerous grammatical structures. Vocabulary learning is a principal issue for English learning because vocabulary comprises the basic building blocks of English sentences. Therefore, many studies have attempted to improve the efficiency and performance when learning English vocabulary. With the accelerated growth in wireless and mobile technologies, mobile learning using mobile devices such as PDAs, tablet PCs, and cell phones has gradually become considered effective because it inherits all the advantages of e-learning and overcomes limitations of learning time and space that limit web-based learning systems. Therefore, this study presents a personalized mobile English vocabulary learning system based on Item Response Theory and learning memory cycle, which recommends appropriate English vocabulary for learning according to individual learner vocabulary ability and memory cycle. The proposed system has been successfully implemented on personal digital assistant (PDA) for personalized English vocabulary learning. The experimental results indicated that the proposed system could obviously promote the learning performances and interests of learners due to effective and flexible learning mode for English vocabulary learning.     

Effects of learning support in simulation-based physics learning

This paper describes the effects of learning support on simulation-based learning in three learning models: experiment prompting, a hypothesis menu, and step guidance. A simulation learning system was implemented based on these three models, and the differences between simulation-based learning and traditional laboratory learning were explored in the context of physics studies. The effects of the support type on learning performance were also quantified. In second-year junior high school students it was found that the outcome for learning about the basic characteristics of an optical lens was significantly better for simulation-based learning than for laboratory learning. We also investigated the influences of different learning models on the students’ abstract reasoning abilities, which showed that the different learning models do not have different effects on individuals with different abstract reasoning abilities. However, we found that students who are better at higher abstract reasoning benefit more from simulation-based learning, and also that the learning results are better for experiment prompting and a hypothesis menu than for step guidance.     

基于多示例的K-means聚类学习算法

多示例学习是继监督学习、非监督学习、强化学习后的又一机器学习框架。将多示例学习和非监督学习结合起来,在传统非监督聚类算法K-means的基础上提出MIK-means算法,该算法利用混合Hausdorff距离作为相似测度来实现数据聚类。实验表明,该方法能够有效揭示多示例数据集的内在结构,与K-means算法相比具有更好的聚类效果。     

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。     

面向认知的多源数据学习理论和算法研究进展

多源数据学习在大数据时代具有极其重要的意义.目前,多源数据学习算法研究远远超前于多源数据学习理论研究,经典的机器学习理论难以应用于多源数据学习,更难以提供多源数据学习算法在实际应用中的理论保障.从学习的最终目的是知识这一认知切入点出发,对人类学习的认知机理、机器学习的三大经典理论（计算学习理论、统计学习理论和概率图理论）以及多源数据学习算法设计这3个方面的研究进展进行总结,最后给出未来研究方向的思考.     

零样本学习研究进展

近几年来,深度学习在机器学习研究领域中取得了巨大的突破,深度学习能够很好地实现复杂问题的学习,然而,深度学习最大的弊端之一,就是需要大量人工标注的训练数据,而这需要耗费大量的人力成本.因此,为了缓解深度学习存在的这一问题,Palatucci等于2009年提出了零样本学习(Zero-shot learning).零样本学习是迁移学习的一种特殊场景,在零样本学习过程中,训练类集和测试类集之间没有交集,需要通过训练类与测试类之间的知识迁移来完成学习,使在训练类上训练得到的模型能够成功识别测试类输入样例的类标签.零样本学习的意义不仅在于可以对难以标注的样例进行识别,更在于这一方法模拟了人类对于从未见过的对象的认知过程,零样本学习方法的研究,也会在一定程度上促进认知科学的研究.鉴于零样本学习的应用价值、理论意义和未来的发展潜力,文中系统综述了零样本学习的研究进展,首先概述了零样本学习的定义,介绍了4种典型的零样本学习模型,并对零样本学习存在的关键问题及解决方法进行了介绍,对零样本学习的多种模型进行了分类和阐述,并在最后指明了零样本学习进一步研究中需要解决的问题以及未来可能的发展方向.