远程教育杂志
遠程教育雜誌
원정교육잡지
DISTANCE EDUCATION JOURNAL
2013年
4期
20-28
,共9页
学习分析%学习活动流%学习行为%学习情境%智慧教育
學習分析%學習活動流%學習行為%學習情境%智慧教育
학습분석%학습활동류%학습행위%학습정경%지혜교육
Learning analytics%Learning activity streams%Learning behavior%Learning context%Smart education
智慧教育体现了教育信息化发展的新境界,表达了一种技术以智慧性方式促进教育变革与创新的诉求,这一目标的实现离不开学习分析技术。学习分析的核心就是观察和理解学习行为,以倒溯方式考察影响行为产生的需要、动机等因素,以及行为所携带的目的、个性、环境等元素,从而加以利用以优化学习过程及其发生的环境。而一个好的行为模型将大大助力于对这些信息的收集、分析与理解。学习活动流模型的提出补充了以往学习行为分析所没考虑的学习来源的多元化以及学习活动的持续性问题。借鉴活动流的描述机制,情景化注意元数据被加以改造并得到学习活动流的描述模型,而基于学习活动流的学习情境分析则探讨了对这一行为模型的学习分析应用,并以信息感知和资源推送为例展示了其实践应用。
智慧教育體現瞭教育信息化髮展的新境界,錶達瞭一種技術以智慧性方式促進教育變革與創新的訴求,這一目標的實現離不開學習分析技術。學習分析的覈心就是觀察和理解學習行為,以倒溯方式攷察影響行為產生的需要、動機等因素,以及行為所攜帶的目的、箇性、環境等元素,從而加以利用以優化學習過程及其髮生的環境。而一箇好的行為模型將大大助力于對這些信息的收集、分析與理解。學習活動流模型的提齣補充瞭以往學習行為分析所沒攷慮的學習來源的多元化以及學習活動的持續性問題。藉鑒活動流的描述機製,情景化註意元數據被加以改造併得到學習活動流的描述模型,而基于學習活動流的學習情境分析則探討瞭對這一行為模型的學習分析應用,併以信息感知和資源推送為例展示瞭其實踐應用。
지혜교육체현료교육신식화발전적신경계,표체료일충기술이지혜성방식촉진교육변혁여창신적소구,저일목표적실현리불개학습분석기술。학습분석적핵심취시관찰화리해학습행위,이도소방식고찰영향행위산생적수요、동궤등인소,이급행위소휴대적목적、개성、배경등원소,종이가이이용이우화학습과정급기발생적배경。이일개호적행위모형장대대조력우대저사신식적수집、분석여리해。학습활동류모형적제출보충료이왕학습행위분석소몰고필적학습래원적다원화이급학습활동적지속성문제。차감활동류적묘술궤제,정경화주의원수거피가이개조병득도학습활동류적묘술모형,이기우학습활동류적학습정경분석칙탐토료대저일행위모형적학습분석응용,병이신식감지화자원추송위례전시료기실천응용。
As a new realm of the development of e-education, smart education expresses a demand of using technology in in-telligent ways to promote educational revolution and innovation. The achievement of this objective is inseparable from learning analyt-ics. The core of learning analytics is to observe and understand learning behaviors. By investigating the factors (e.g. needs, motiva-tions) which impact these behaviors, and the information (e.g. purpose, personality, context) which is carried by these behaviors, the learning process and learning environment can be optimized. A good behavior model will greatly contribute to collecting, analyzing and understanding above-mentioned information. So the concept of learning activity streams can solve the problems of diversification of learning sources and persistence of learning process which haven’t been considered before. The description model of learning ac-tivity streams is built by learning from description mechanisms of activity streams and contextualized attention metadata. Finally, its practical applications are discussed on the theme of learning context analysis, and demonstrated through two cases of information per-ception and resource push.