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.     

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).     

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。     

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     

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.     

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.     

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.     

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。     

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.     

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。     

Robot Position/Force Control in Unknown Environment Using Hybrid Reinforcement Learning

Abstract

Robot position/force control provides an interaction scheme between the robot and the environment. When the environment is unknown, learning algorithms are needed. But, the learning space and learning time are big. To balance the learning accuracy and the learning time, we propose a hybrid reinforcement learning method, which can be in both discrete and continuous domains. The discrete-time learning has poor learning accuracy and less learning time. The continuous-time learning is slow but has better learning precision. This hybrid reinforcement learning learns the optimal contact force, meanwhile it minimizes the position error in the unknown environment. Convergence of the proposed learning algorithm is proven. Real-time experiments are carried out using the pan and tilt robot and the force/torque sensor.     

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.     

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。     

网络环境下互动学习模式对大学计算机基础学习效果的影响

应用合作学习理论,探讨网络合作学习方法是否适用于独立学院非计算机专业的计算机应用基础课程,是否可以提升学生的学习效果及学习动机。实验表明对学习基础差的学习动机有提升,网络合作学习对计算机应用基础的学习效果有帮助。学习基础差的学生其合作态度高于学习基础好的学生。     

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.