【作者】 杨洋；

【导师】 岳丽华；

【作者基本信息】 中国科学技术大学 ， 计算机应用技术， 2012， 博士

【摘要】 舞蹈,作为一种人体动作的艺术,对我们的生活有着深远的影响。然而,学习舞蹈绝对不是一件容易的事情。在本文中,我们提出了一种基于三维运动捕捉技术的智能舞蹈教学系统来指导学生学习舞蹈,该系统可以提供基于虚拟现实的两阶段学习过程以帮助学生学习舞蹈。一次性学习整段舞蹈会让大部分学生感觉到不知所措,所以在第一阶段,通过提取舞蹈动作中的重复,我们将整段舞蹈分割成了很多小段,这些小段即是模式。这些模式是第一阶段的基本学习单元(LO)。为了进一步提供有组织的学习,我们找出这些基本学习单元之间的先修关系(PR)从而构造了一个先修关系图(PS)。之后,根据先修关系图,我们可以构造出知识结构图(K-Struct)。最后,根据本文中提出的运动复杂度度量,一个满足先修关系的由易到难的学习路径(LP)被用来指导学生学习所有的模式。人们在学习中偏爱的方式即是心理学家所说的学习风格(LS)。针对不同的学习风格采取相应的学习策略可以改善学生的学习体验。在本文中,我们提出了一种基于规则的方法来检测学生的学习风格(整体主义或序列主义),该方法可以通过分析学生与先修关系图的交互模式推测出学生的学习风格。对于学生与先修关系图的每次交互,系统会判断该交互支持哪一种学习风格并增加对应学习风格的置信度。另外,基于检测出的学习风格,我们实现了自适应教学系统(AES)用于改善第一阶段的学习体验。该系统以两种方式,即自适应标记(AA)和自适应排序(AO),来实现自适应导航支持(ANS)用于实施第一阶段课程。在结束第一阶段学习后,下一个需要解决的问题就是如何指导学生根据各个模式重构出整段舞蹈。为了解决这个问题,我们提出了一种基于离散增量式教学(DII)的方法,其中,这些增量的大小是在学习过程中动态调整的。第二阶段的学习过程适用于不同背景知识和不同学习风格的学生。系统会根据学生的成绩,自适应的增加新的模式供学生学习。一般来说,在人类工作记忆(WM)容量范围内,学生的分数越高,系统就添加更多的新模式供他们学习。另外,我们还实现了基于自适应标记和自适应排序的自适应导航支持用于实施第二阶段课程。在本文中,我们提出了一种自动生成先修关系图的方法,先修关系图的构造为后面的两阶段学习提供了坚实的基础。我们提出了一种自动的方法来产生由易到难的学习路径从而指导学生有效的学习各个模式。通过考虑不同的学习风格,我们将第一阶段学习环境扩展成了一个自适应的学习环境。在第二阶段学习中,我们提出了一种自适应的离散增量式教学方法来指导学生将各个模式组合成整段舞蹈。我们实现了本文中的提出的两阶段舞蹈学习系统DL-BUS。为了评估我们提出的系统DL-BUS,我们进行了一些用户调查,t-检验和卡方检验方法被用来对实验数据进行研究,结果表明舞蹈教学系统DL-BUS为舞蹈教学提供了一个集趣味性和高效性于一体的平台,该平台的开发对未来舞蹈教学有重大影响。更多还原

【Abstract】 Dance, as an art of the body movement, has great impact on our lives. However, learning to dance is a non-trivial task. In this dissertation, an adaptive educational sys-tem for dance education is presented to guide students in learning dances. It provides a virtual reality learning environment with an efficient two-phase dance lesson.Learning the whole dance at once would overwhelm most students, and for that reason, the dance is divided into small segments which we call patterns by extracting the repetitions. These patterns are treated as the learning objects in phase-1learning. In order to achieve an organized learning, the prerequisite structure is then automati-cally built by considering the relations between the learning objects. Then based on the prerequisite structure, we could build the knowledge structure. Finally, according to the proposed motion complexity measure, an easy-to-complex learning path while respecting the prerequisite relations is derived to guide the students in going through all the patterns.People learn in many different ways. The preferred way people apply in learn-ing is what psychologists called learning style. Adapting different learning strategies to different learning styles yields better learning experience. In this dissertation, we proposed a novel rule-based approach to detect the students’ learning styles (holist or serialist). The approach could infer the students’ learning styles by analyzing the stu-dents’ navigation along the prerequisite structure. Based on the detected learning style, we proposed a method to extend the phase-1learning environment into a learning style based adaptive learning environment. Adaptive navigation support is implemented to facilitate the phase-1learning, two major techniques are involved, i.e., adaptive order-ing and adaptive annotation.After dividing the dance into small segments, the next issue to address is to guide the students to string these patterns together into the full dance. In order to do that, an adaptive discrete increments of instruction is presented, the size of increment is dynami-cally adjusted during the learning process. The learning process of the phase-2adapts to the student’s background knowledge and preference. It keeps monitoring the students’ performances during the learning process, and adaptively adding new patterns for the students to learn. Generally speaking, within the capacity of human working memory, the more the students have scored, the more the additional patterns will be presented. Adaptive navigation support based on the adaptive ordering and adaptive annotation is also implemented to facilitate the phase-2learning.In this dissertation, we proposed an automatic way to construct the prerequisite structure which serves as the basis for further deriving the two-phase lesson. We pro-posed to automatically derive an easy-to-complex phase-1lesson to guide the students in learning the patterns. Furthermore, we proposed a rule-based approach to detect the students’learning styles. By adapting to the different learning styles, the phase-1learning environment is further extended into a adaptive learning environment. As for phase-2learning, we provide the students with adaptive discrete increments of instruc-tion to teach them to string these patterns together into the full dance. We have also im-plemented the proposed two-phase dance learning system called Dance Learning from Bottom-Up Structure (DL-BUS). Several user studies are conducted to evaluate the pro-posed system, t-test and chi-squared test were explored to study the collected data. The experiment results indicate that our proposed DL-BUS is an interesting and effective platform for dance education. DL-BUS will have a great impact on how people learn dance.更多还原