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A systematic mapping review of context-aware analysis and its approach to mobile learning and ubiquitous learning processes

机译:系统意识分析及其移动学习方法的系统映射综述及普遍存在过程

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In the last ten years, it was observed that the continuous upgrades in mobile device's technology have increased and demonstrated their great potential in various learning environments. Besides, it has motivated researchers to apply more innovative computational techniques regarding context-aware and a set of variables that have been used at different virtual learning proposals. This paper presents a Systematic Mapping Review that focuses on context-aware analysis and its approach to learning processes in mobile learning (m-learning) and ubiquitous learning (u-learning). Furthermore, the study identifies variables that have been used in the past decade for context-awareness analytics and that have been applied to those learning processes. Especially at systems' adaptations to learning styles and student-specific characteristics. A methodological process was applied to address problems through identification, critical evaluation, and integration of the most relevant works, where high-quality individual studies address one or several research questions. Results show that external variables, including location, time, and software, are the most used variables in the context-aware analysis, with 52.25% prevalence in research papers. Internal variables, including personal information, learning styles, and teaching styles, had a prevalence of 33.33%; variables less frequently used in the research included socioeconomic information and emotional content. The remaining 14.41% represents academic activities. These findings provide a basis for future research and development in the field of m-learning.
机译:在过去的十年中,据观察,移动设备技术的持续升级增加,并在各种学习环境中展示了它们的巨大潜力。此外,它具有激励的研究人员,用于应用更多关于上下文知识的创新计算技术和已经在不同的虚拟学习提案中使用的一组变量。本文提出了一个系统的映射评论,重点侧重于上下文感知分析及其在移动学习中的学习过程的方法(M-Learning)和普遍学习(U-Learning)。此外,该研究识别过去十年用于上下文意识分析的变量,并且已应用于这些学习过程。特别是在系统的改编到学习风格和学生特定的特征。通过鉴定,关键评估和最相关作品的整合来应用方法处理来解决问题,其中高质量的个体研究解决了一个或多个研究问题。结果表明,外部变量,包括位置,时间和软件,是上下文感知分析中最常见的变量,研究论文中的52.25%。内部变量,包括个人信息,学习款式和教学方式,患病率为33.33%;在研究中常用的变量包括社会经济信息和情感内容。其余14.41%代表学术活动。这些调查结果为M-Learning领域的未来研发提供了基础。

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