泛在学习中教学质量评价的数据挖掘研究
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摘要
根据《国家中长期教育改革和发展规划纲要(2010-2020年)》的要求,“到2020年,我国将形成人人皆学、处处可学、时时能学的学习型社会。”这与泛在学习(U-learning)的理念不谋而合,可以说,学习型社会的建设促进了U-learning(Ubiquitous Learning)的实现,而U-learning则为实现终身学习提供了可靠的支持。
在当代网络通信与计算机技术的支持下,泛在计算、数字化学习、移动学习、终身学习、后现代远程教育一齐勾画出了未来教育发展模式的新蓝图。随着普适计算网络(Ubiquitous Computing Network)的不断深入和推进,如何正确评价泛在学习的各种不同学习模式,对不同学习形式的评价数据提出相适应的数据挖掘策略,寻找影响评价结果的关键因素,从而提高学习水平、改善学习质量,为各类学习形式向泛在方向又好又快发展提供控制依据,这成为了值得我们关注的焦点。
本文首先通过整理和归纳普适技术支持下的各类开放学习模式,研究教育领域学习质量评价体系相关理论和原则,提出了狭义泛在学习模型。其次,通过研究人类主观意识对客观学习资源形态的相互关系,以移动学习为例,提出的“移动学习信息设计原则”为后续泛在学习资源设计提供了有利的参考。另外,本文通过对电子学习资源平台系统的效益质量评估进行建模,提出学习资源评价指标集,并进行权重分配实验,为今后的泛在学习资源建设把控提供有效方法和重要参考。最后,通过对各种学习形式下的数据进行多组不同实验,为今后广义泛在学习提供数据挖掘实验参考案例和方法过程。
本文的主要工作,概括起来说,可以分为以下几个方面:
一、归纳和整理了新时代下泛在学习的不同模式,提出了当前技术背景下的狭义泛在学习模型。
二、借鉴目前国内外已有的学习评价系统,提出了促进泛在学习资源评估和发展的指标集合,并结合教育领域数据挖掘研究成果和其它数据挖掘领域的经验,对学习评价指标的权重分配进行实验,提出了可供参考的分配方法。
三、通过因子分析实验,找出影响主观评价结果的关键因素,尤其对教育类主观定序尺度数据的评价数据进行探讨,提供了一种有效的处理此类数据的解决方法。
四、通过基于神经网络的数据挖掘技术,对客观学习数据和学习行为进行分类判别实验,为对今后的泛在学习评价进行数据挖掘提供了研究策略和过程方法。
总而言之,本文在普适计算网络下的泛在学习模式和学习质量评估方面做出了有益的探索与研究。
The "National Long-term Educational Reform and Development Plan (2010-2020)" states that, "By 2020, China will be a 3A learning society, which means everybody can learn anywhere, anytime, and with any device." It also coincides with the idea of Ubiquitous Learning (U-learning). The construction of a 3A-learning society can help to promote the implementation of the U-learning, which can enable the general public to achieve life-long learning.
With the fast development of modern network communications and computer technology, ubiquitous computing, distance education, mobile learning, lifelong learning, and E-learning will become the major components of future education. With the wide development of Ubiquitous Computing Networks, how to properly evaluate different learning models becomes critical in improving the quality of learning at many forms. The evaluation will involve digging for strategy of the assessment data, looking for the key factors which impact the learning assessment. And these will spur a better and faster development of a 3A-learning society.
After studying various types of universal open-learning-modes supported by current technologies, the thesis proposes a specific ubiquitous learning model. By connecting the relationship between human subjective consciousness and the objective form of learning resources, the author, in collaboration with experts from San Diego State University, generated the "message design principles for mobile learning". And these principles could be a favorable reference for the follow-up design of U-learning resources. In addition, this article provides a quality assessment method based on E-learning resources platforms and systems, in order to evaluate the effectiveness of the learning resources. The author also conducted experiments on the weighting distribution test to provide examples on U-learning evaluation index-setting, and other experiments on U-learning analysis by data mining technology, providing various reference and cases on process methods for future study on U-learning.
The main work of this paper can be divided into four parts as followed:
First, summarize the specific context of ubiquitous learning models under the current technology and the different learning forms.
Second, after study on the current evaluation system and some mainstream data mining cases, this thesis suggests a set of assessment index on keeping electronic learning materials (E-materials) effective and efficient. This assessment can help to develop sustainable E-materials to meet the potential needs of U-learning. A weighting distribution experiment on this evaluation index set has been put forward, which could be a good reference on weighting coefficients and distribution methods for U-learning evaluation.
Third, this thesis provides an effective solution for the sequencing data of subjective evaluation after the study on a variety of data mining algorithms. It also gives an experimental model for future’s U-learning data analysis.
Finally, this thesis uses neural network-based data mining technology to analyze experiments on the study of learning behavior and learning effectiveness. Factor analysis experiment is used to discover the key factors that impact the learning evaluation results.
This thesis provides several solid experiments and can be a good reference on research methods and data mining strategies for the future study on U-leaning.
在当代网络通信与计算机技术的支持下,泛在计算、数字化学习、移动学习、终身学习、后现代远程教育一齐勾画出了未来教育发展模式的新蓝图。随着普适计算网络(Ubiquitous Computing Network)的不断深入和推进,如何正确评价泛在学习的各种不同学习模式,对不同学习形式的评价数据提出相适应的数据挖掘策略,寻找影响评价结果的关键因素,从而提高学习水平、改善学习质量,为各类学习形式向泛在方向又好又快发展提供控制依据,这成为了值得我们关注的焦点。
本文首先通过整理和归纳普适技术支持下的各类开放学习模式,研究教育领域学习质量评价体系相关理论和原则,提出了狭义泛在学习模型。其次,通过研究人类主观意识对客观学习资源形态的相互关系,以移动学习为例,提出的“移动学习信息设计原则”为后续泛在学习资源设计提供了有利的参考。另外,本文通过对电子学习资源平台系统的效益质量评估进行建模,提出学习资源评价指标集,并进行权重分配实验,为今后的泛在学习资源建设把控提供有效方法和重要参考。最后,通过对各种学习形式下的数据进行多组不同实验,为今后广义泛在学习提供数据挖掘实验参考案例和方法过程。
本文的主要工作,概括起来说,可以分为以下几个方面:
一、归纳和整理了新时代下泛在学习的不同模式,提出了当前技术背景下的狭义泛在学习模型。
二、借鉴目前国内外已有的学习评价系统,提出了促进泛在学习资源评估和发展的指标集合,并结合教育领域数据挖掘研究成果和其它数据挖掘领域的经验,对学习评价指标的权重分配进行实验,提出了可供参考的分配方法。
三、通过因子分析实验,找出影响主观评价结果的关键因素,尤其对教育类主观定序尺度数据的评价数据进行探讨,提供了一种有效的处理此类数据的解决方法。
四、通过基于神经网络的数据挖掘技术,对客观学习数据和学习行为进行分类判别实验,为对今后的泛在学习评价进行数据挖掘提供了研究策略和过程方法。
总而言之,本文在普适计算网络下的泛在学习模式和学习质量评估方面做出了有益的探索与研究。
The "National Long-term Educational Reform and Development Plan (2010-2020)" states that, "By 2020, China will be a 3A learning society, which means everybody can learn anywhere, anytime, and with any device." It also coincides with the idea of Ubiquitous Learning (U-learning). The construction of a 3A-learning society can help to promote the implementation of the U-learning, which can enable the general public to achieve life-long learning.
With the fast development of modern network communications and computer technology, ubiquitous computing, distance education, mobile learning, lifelong learning, and E-learning will become the major components of future education. With the wide development of Ubiquitous Computing Networks, how to properly evaluate different learning models becomes critical in improving the quality of learning at many forms. The evaluation will involve digging for strategy of the assessment data, looking for the key factors which impact the learning assessment. And these will spur a better and faster development of a 3A-learning society.
After studying various types of universal open-learning-modes supported by current technologies, the thesis proposes a specific ubiquitous learning model. By connecting the relationship between human subjective consciousness and the objective form of learning resources, the author, in collaboration with experts from San Diego State University, generated the "message design principles for mobile learning". And these principles could be a favorable reference for the follow-up design of U-learning resources. In addition, this article provides a quality assessment method based on E-learning resources platforms and systems, in order to evaluate the effectiveness of the learning resources. The author also conducted experiments on the weighting distribution test to provide examples on U-learning evaluation index-setting, and other experiments on U-learning analysis by data mining technology, providing various reference and cases on process methods for future study on U-learning.
The main work of this paper can be divided into four parts as followed:
First, summarize the specific context of ubiquitous learning models under the current technology and the different learning forms.
Second, after study on the current evaluation system and some mainstream data mining cases, this thesis suggests a set of assessment index on keeping electronic learning materials (E-materials) effective and efficient. This assessment can help to develop sustainable E-materials to meet the potential needs of U-learning. A weighting distribution experiment on this evaluation index set has been put forward, which could be a good reference on weighting coefficients and distribution methods for U-learning evaluation.
Third, this thesis provides an effective solution for the sequencing data of subjective evaluation after the study on a variety of data mining algorithms. It also gives an experimental model for future’s U-learning data analysis.
Finally, this thesis uses neural network-based data mining technology to analyze experiments on the study of learning behavior and learning effectiveness. Factor analysis experiment is used to discover the key factors that impact the learning evaluation results.
This thesis provides several solid experiments and can be a good reference on research methods and data mining strategies for the future study on U-leaning.
引文
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