Unpacking the researcher-teacher co-design process of a seamless language learning environment with the TPACK framework
Unpacking the researcher-teacher co-design process of a seamless language learning environment with the TPACK framework
复制标题
使用 TPACK 框架解析无缝语言学习环境的研究人员与教师共同设计过程
DOI：
10.1145/2723576.2723598
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
G. Aw
中科院分区：
文献类型：
--
作者：
L. Wong;C. Chai;Ronnel B. King;Xujuan Zhang;G. Aw
本文描述了一种可视化和分析的方法，从学习者在现实世界中积累的大量学习经验中挖掘有用的学习日志，作为无处不在的学习日志。无处不在的学习日志（ULL）被定义为学习者利用无处不在的技术在日常生活中所学知识的数字记录。它允许学习者通过照片、音频、视频、位置、RFID 标签和传感器数据记录他们的学习经历，并与其他人共享和重复使用 ULL。通过构建由积累的 ULL 组成的真实世界语料库，其中包含学习者在现实世界中学习了什么、何时、何地以及如何学习等信息，并通过分析它们，我们可以支持学习者更有效地学习。所提出的系统将通过分析他们过去的 ULL 来预测他们未来的学习机会，包括他们的学习模式和趋势。预测可以通过基于学习者、知识、地点和时间等ULL信息的网络分析和学习者使用时间图的自我分析来实现。通过预测他们在学习路径中下一步倾向于学习什么，它为他们提供了更多的学习机会。积累的数据如此庞大，数据之间的关系如此复杂，以至于很难掌握 ULL 之间的关联程度。因此，本文提出了一个系统，利用网络图和网络分析来帮助学习者掌握学习者、知识、地点和时间之间的关系。
This paper describes a method of the visualization and analysis for mining useful learning logs from numerous learning experiences that learners have accumulated in the real world as the ubiquitous learning logs. Ubiquitous Learning Log (ULL) is defined as a digital record of what learners have learned in the daily life using ubiquitous technologies. It allows learners to log their learning experiences with photos, audios, videos, location, RFID tag and sensor data, and to share and reuse ULL with others. By constructing real-world corpora which comprise of accumulated ULLs with information such as what, when, where, and how learners have learned in the real world and by analyzing them, we can support learners to learn more effectively. The proposed system will predict their future learning opportunities including their learning patterns and trends by analyzing their past ULLs. The prediction is made possible both by network analysis based on ULL information such as learners, knowledge, place and time and by learners' self-analysis using time-map. By predicting what they tend to learn next in their learning paths, it provides them with more learning opportunities. Accumulated data are so big and the relationships among the data are so complicated that it is difficult to grasp how closely the ULLs are related each other. Therefore, this paper proposes a system to help learners to grasp relationships among learners, knowledge, place and time, using network graphs and network analysis.