自适应学习系统支持模型与实现机制研究
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摘要
一些网络学习系统如Blackboard、Moodle、SaKai等提供给学习者都是千篇一律的学习资源，不能根据用户特性动态适应地呈现个性化学习内容。因此，构建自适应学习系统是解决学生需求资源动态、个性化与教学资源静态化两者关系的有效方案，是化解“总体资源无限”与“个体需求资源有限”之间矛盾的有效手段，实现了学生在远程学习环境下学习的两大功能，其一是学习者主动地适应远程学习方式，实现学习者自我组织，制定并执行学习计划、自主选择学习策略，对学习进行自我评估；其二是系统能够为学习者提供适应性学习服务支持，实现了一种系统能够根据用户特性主动向学习者注入资源的学习方式，在这种环境里学习，系统会把学生当作服务的顾客，视为“上帝”，为学生提供学习方法、策略支持服务，学习资源支持服务及情感支持服务等，实现个性化学习支持服务，即能根据学习者的多种特点和行为倾向，如学习风格、媒体倾向、兴趣、认知水平等，采用相应的教学策略，推荐个性化的学习路径和学习资源，进而有效促进学习者的个性化教育，对学生个性全面发展及未来创新人才培养起着重要作用。
自适应学习系统是一个方兴未艾的研究方向，其部分成果已经呈现出可以大范围应用的可能性，然而系统模型建构尚很薄弱且缺少必要的实证研究。本文试图从模型建构和教学实践两大层面全面推进该方向的研究，同时综合应用了多种研究方法，如文献研究法、解释结构模型法、实证研究法、调查研究法、评价研究法等。具体研究工作主要包括：
1.通过文献研究法、调查研究法分析了自适应学习系统研究的必要性，然后通过大量的文献分析，总结自适应学习系统存在的共性特点和不足，期望能在系统参考模型、用户模型、领域模型及教学策略模型等方面进行优化研究，设计并研发自适应学习系统软件产品，实现根据用户的个体差异适应性呈现学习资源和学习路径。
2.根据可查文献，深入分析自适应学习系统主要参考模型特点与不足，然后研究以自适应学习系统通用模型为主要参考模型，同时考虑各模型中符合研究需求的因素设计了自适应学习系统参考模型，它主要包括用户模型、领域模型（或称知识模型）、自适应模型、自适应引擎和呈现模型等五个部分，并添加了对学习行为关键因素描述及对学习内容、导航和序列等三方面适应性显示描述，可更利于指导系统研发，为定义系统组件提供准则。
3.采用解释结构模型法从学习风格和知识水平两方面设计用户模型建构参考规范并构建用户模型。其中学习风格模型构建分两个步骤，首先采用Felder-Silverman学习风格模型的显性方法初始化学习风格，然后利用规则和贝叶斯网络法挖掘网络学习行为的隐性方法修正学习风格，实现学习对象按照修正后的用户学习风格准确地适应性呈现。知识水平模型是基于概念累积计分法和布卢姆认知理论构建。
4.采用解释结构模型法设计知识模型建构参考规范，并采用本体技术构建知识模型，为系统根据用户模型进行知识的自适应呈现提供了前提条件。
5.采用解释结构模型法设计系统的对象模型和关系模型，如章与节之间对象模型和关系模型、节与知识点之间对象模型和关系模型、知识点与学习对象之间对象模型和关系模型、用户与媒体类型的关系模型、用户与知识点抽象程度的关系模型、评测对象模型、用户学习行为的对象模型等，从而实现根据学习者的多种特点和行为倾向，如学习风格、媒体倾向、兴趣、认知水平等，采用相应的教学策略，推荐个性化的学习路径和学习资源。
6.以“系统主动注入和用户主动查找”的双主资源获取策略为依据，设计了自适应学习系统架构、多元化学习资源，然后基于建构的对象模型和关系模型，采用JAVA+SQLServer技术和本体技术研发出自适应学习系统，最后以研发系统为例，详细介绍了基于用户模型的学习对象和学习活动序列适应性呈现过程，并利用AprioriAll算法实现个性化学习活动序列优化推荐，实证研究证明了可以减少“学习迷航”和“认知过载”等问题，能提高学习效率与质量。
7.以研发的自适应学习系统为研究平台，从用户为中心研究视角，依据评价方法、变量和模型，主要通过分析用户行为客观变量实证评价自适应学习系统的学习成效，结果表明适应性、个性化学习有利于提高学习过程效率，突出表现在可以花费更少的学习时间、访问更少的学习对象等即可完成知识学习任务。与此同时，通过问卷调查也得出学习者对自适应学习系统在学习乐趣、学习付出、学习动机和学习满意度等方面都给予了肯定。
根据本研究的进展和不断反思，认为仍存在需要进一步深入研究点，包括利用探讨协同过滤技术实现向学习者推送与其有相同或相近兴趣偏好特性学习者的学习信息；将自适应学习系统应用到其它学科教学中，评价其实效性；将自适应学习系统成果应用基础教育教学中进行研究；以及研究如何将E-learning环境下自适应学习成果迁移到泛在学习环境中并有效利用。
Some web-based learning systems, such as Blackboard, Moodle, SaKai, etc, provide thesame learning resources to learners. According to user characteristics, personalized dynamicadaptive learning content don’t presented. So the construction of adaptive learning system isan effective scheme to resolve the problem between dynamic students' needs of resources andstatic teaching resources. It is also an effective method to resolve the contradiction betweenoverall infinite resources and individual needs of finite resources. It realizes two functions inthe distance learning environment, which one is that learers can actively adapt distancelearning, organize learning resources, make and carry out learning plan, select study strategyand can be self-assessment.Another is that system can provide learners with adaptive learningservice support and in this learning circumstance, the system will put the students as servicecustomers, as god and provide learning methods, strategies and emotional service.That is tosay, adaptive learning system can recommend personal learning path and learning resources,according to students' characteristics and learning behaviors, such as learning styles, mediatypes, learning interests, cognitive level, etc. It can effectively enhance students' individualeducation and improve the full development of a growing personality.
Adaptive learning system is an ascendant research direction. It is possible that part ofachievements has been widely used. However, the construction of system model is still veryweak and this system also lacks the necessary empirical research. This thesis attempts topromote this research from model construction and teaching practice, and also applies variousresearch methods, for example literature research, interpretative structural modelling method,empirical research, investigation research, evaluative method, etc. The following is thecontents of this work.
Firstly, this thesis analyses the necessity of studying adaptive learning system throughliterature research and investigation research. Then, we propose and construct AdaptiveLearning System which can provide personal learning resource and learning paths based-oncommon features and insufficiences of current adaptive learning systems.
Secondly, based on the features and insufficiences of current adaptive learning systemsreference model, this thesis proposes a reference model of Adaptive Learning System,including user model, domain model (or knowledge model), adaptation model, adaptiveengine, presentation model, and adding learning behaviors description and adaptivepresentation description of learning resources, navigation and sequence in favor of developingsystem and defining system guidelines.
Thirdly, based-on learning style and knowledge level, this thesis designs user modelconstruction reference standard by using interpretative structural modelling method. Thisthesis adopts two steps to construct learning style model, which first has been initializedthrough Felder-Silverman learning style model, and then has been updated based-on rules orBayesian Nets method. Knowledge level model has been constructed based-on Cumulativescore law concept and cognitive theory of Blum.
Fourthly, this thesis designs knowledge model construction reference standard by usinginterpretative structural modelling method and ontology technology, which provides aprerequisite for adaptively presenting knowledges based-on user model.
Fifthly, using interpretative structural modelling method, this thesis designs some objectmodels and relation models, such as chapter and section, section and knowledge, knowledgeand learning object, which can recommend personal learning path and learning resourcesaccording to students' characteristics and learning behaviors, such as learning styles, mediatypes, learning interests, cognitive level, etc.
Sixthly, this thesis designs adaptive learning system architecture and diversified learningresources based-on double-subject personalized recommendation strategy. Then, based-onobject model and relation model this thesis has developed Adaptive Learning System by usingJAVA, SQLServer and ontology technology, which is set as an example to introduce howlearning objects and learning active sequence have been represented based-on user model, andintroduce personal optimizational recommendation of learning active sequence based-onAprioriAll algorithm, which students can avoid learning lost, reduce cognitive overload andthe learning efficiency and quality can been improved.
Seventhly, based-on Adaptive Learning System, this thesis analyzes user behaviorsobjective variables and evaluates learning effectiveness from user-centered evaluations.Theresults show that adaptive and personalized learning can improve learning effectiveness andstudents can complete the learning tasks using less time and learning objects. Meanwhile,students give a positive on learning enjoyment, learning effort, learning motivation andlearning satisfaction in adaptive learning system.
According to research progress and continual reflection, this thesis proposes that thereare still some points which need to be studied in further. For example, we may studypersonalization recommendation among learners based-on collaborative filtering, which ishelpful that students can find new knowledge and adaptive learning system may be applied toother subject teaching and basic education learners to evaluate learning effectiveness and howadaptive learning achievement may be migrated from E-learning to U-learning, which can beused effectively.
自适应学习系统是一个方兴未艾的研究方向，其部分成果已经呈现出可以大范围应用的可能性，然而系统模型建构尚很薄弱且缺少必要的实证研究。本文试图从模型建构和教学实践两大层面全面推进该方向的研究，同时综合应用了多种研究方法，如文献研究法、解释结构模型法、实证研究法、调查研究法、评价研究法等。具体研究工作主要包括：
1.通过文献研究法、调查研究法分析了自适应学习系统研究的必要性，然后通过大量的文献分析，总结自适应学习系统存在的共性特点和不足，期望能在系统参考模型、用户模型、领域模型及教学策略模型等方面进行优化研究，设计并研发自适应学习系统软件产品，实现根据用户的个体差异适应性呈现学习资源和学习路径。
2.根据可查文献，深入分析自适应学习系统主要参考模型特点与不足，然后研究以自适应学习系统通用模型为主要参考模型，同时考虑各模型中符合研究需求的因素设计了自适应学习系统参考模型，它主要包括用户模型、领域模型（或称知识模型）、自适应模型、自适应引擎和呈现模型等五个部分，并添加了对学习行为关键因素描述及对学习内容、导航和序列等三方面适应性显示描述，可更利于指导系统研发，为定义系统组件提供准则。
3.采用解释结构模型法从学习风格和知识水平两方面设计用户模型建构参考规范并构建用户模型。其中学习风格模型构建分两个步骤，首先采用Felder-Silverman学习风格模型的显性方法初始化学习风格，然后利用规则和贝叶斯网络法挖掘网络学习行为的隐性方法修正学习风格，实现学习对象按照修正后的用户学习风格准确地适应性呈现。知识水平模型是基于概念累积计分法和布卢姆认知理论构建。
4.采用解释结构模型法设计知识模型建构参考规范，并采用本体技术构建知识模型，为系统根据用户模型进行知识的自适应呈现提供了前提条件。
5.采用解释结构模型法设计系统的对象模型和关系模型，如章与节之间对象模型和关系模型、节与知识点之间对象模型和关系模型、知识点与学习对象之间对象模型和关系模型、用户与媒体类型的关系模型、用户与知识点抽象程度的关系模型、评测对象模型、用户学习行为的对象模型等，从而实现根据学习者的多种特点和行为倾向，如学习风格、媒体倾向、兴趣、认知水平等，采用相应的教学策略，推荐个性化的学习路径和学习资源。
6.以“系统主动注入和用户主动查找”的双主资源获取策略为依据，设计了自适应学习系统架构、多元化学习资源，然后基于建构的对象模型和关系模型，采用JAVA+SQLServer技术和本体技术研发出自适应学习系统，最后以研发系统为例，详细介绍了基于用户模型的学习对象和学习活动序列适应性呈现过程，并利用AprioriAll算法实现个性化学习活动序列优化推荐，实证研究证明了可以减少“学习迷航”和“认知过载”等问题，能提高学习效率与质量。
7.以研发的自适应学习系统为研究平台，从用户为中心研究视角，依据评价方法、变量和模型，主要通过分析用户行为客观变量实证评价自适应学习系统的学习成效，结果表明适应性、个性化学习有利于提高学习过程效率，突出表现在可以花费更少的学习时间、访问更少的学习对象等即可完成知识学习任务。与此同时，通过问卷调查也得出学习者对自适应学习系统在学习乐趣、学习付出、学习动机和学习满意度等方面都给予了肯定。
根据本研究的进展和不断反思，认为仍存在需要进一步深入研究点，包括利用探讨协同过滤技术实现向学习者推送与其有相同或相近兴趣偏好特性学习者的学习信息；将自适应学习系统应用到其它学科教学中，评价其实效性；将自适应学习系统成果应用基础教育教学中进行研究；以及研究如何将E-learning环境下自适应学习成果迁移到泛在学习环境中并有效利用。
Some web-based learning systems, such as Blackboard, Moodle, SaKai, etc, provide thesame learning resources to learners. According to user characteristics, personalized dynamicadaptive learning content don’t presented. So the construction of adaptive learning system isan effective scheme to resolve the problem between dynamic students' needs of resources andstatic teaching resources. It is also an effective method to resolve the contradiction betweenoverall infinite resources and individual needs of finite resources. It realizes two functions inthe distance learning environment, which one is that learers can actively adapt distancelearning, organize learning resources, make and carry out learning plan, select study strategyand can be self-assessment.Another is that system can provide learners with adaptive learningservice support and in this learning circumstance, the system will put the students as servicecustomers, as god and provide learning methods, strategies and emotional service.That is tosay, adaptive learning system can recommend personal learning path and learning resources,according to students' characteristics and learning behaviors, such as learning styles, mediatypes, learning interests, cognitive level, etc. It can effectively enhance students' individualeducation and improve the full development of a growing personality.
Adaptive learning system is an ascendant research direction. It is possible that part ofachievements has been widely used. However, the construction of system model is still veryweak and this system also lacks the necessary empirical research. This thesis attempts topromote this research from model construction and teaching practice, and also applies variousresearch methods, for example literature research, interpretative structural modelling method,empirical research, investigation research, evaluative method, etc. The following is thecontents of this work.
Firstly, this thesis analyses the necessity of studying adaptive learning system throughliterature research and investigation research. Then, we propose and construct AdaptiveLearning System which can provide personal learning resource and learning paths based-oncommon features and insufficiences of current adaptive learning systems.
Secondly, based on the features and insufficiences of current adaptive learning systemsreference model, this thesis proposes a reference model of Adaptive Learning System,including user model, domain model (or knowledge model), adaptation model, adaptiveengine, presentation model, and adding learning behaviors description and adaptivepresentation description of learning resources, navigation and sequence in favor of developingsystem and defining system guidelines.
Thirdly, based-on learning style and knowledge level, this thesis designs user modelconstruction reference standard by using interpretative structural modelling method. Thisthesis adopts two steps to construct learning style model, which first has been initializedthrough Felder-Silverman learning style model, and then has been updated based-on rules orBayesian Nets method. Knowledge level model has been constructed based-on Cumulativescore law concept and cognitive theory of Blum.
Fourthly, this thesis designs knowledge model construction reference standard by usinginterpretative structural modelling method and ontology technology, which provides aprerequisite for adaptively presenting knowledges based-on user model.
Fifthly, using interpretative structural modelling method, this thesis designs some objectmodels and relation models, such as chapter and section, section and knowledge, knowledgeand learning object, which can recommend personal learning path and learning resourcesaccording to students' characteristics and learning behaviors, such as learning styles, mediatypes, learning interests, cognitive level, etc.
Sixthly, this thesis designs adaptive learning system architecture and diversified learningresources based-on double-subject personalized recommendation strategy. Then, based-onobject model and relation model this thesis has developed Adaptive Learning System by usingJAVA, SQLServer and ontology technology, which is set as an example to introduce howlearning objects and learning active sequence have been represented based-on user model, andintroduce personal optimizational recommendation of learning active sequence based-onAprioriAll algorithm, which students can avoid learning lost, reduce cognitive overload andthe learning efficiency and quality can been improved.
Seventhly, based-on Adaptive Learning System, this thesis analyzes user behaviorsobjective variables and evaluates learning effectiveness from user-centered evaluations.Theresults show that adaptive and personalized learning can improve learning effectiveness andstudents can complete the learning tasks using less time and learning objects. Meanwhile,students give a positive on learning enjoyment, learning effort, learning motivation andlearning satisfaction in adaptive learning system.
According to research progress and continual reflection, this thesis proposes that thereare still some points which need to be studied in further. For example, we may studypersonalization recommendation among learners based-on collaborative filtering, which ishelpful that students can find new knowledge and adaptive learning system may be applied toother subject teaching and basic education learners to evaluate learning effectiveness and howadaptive learning achievement may be migrated from E-learning to U-learning, which can beused effectively.
引文
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