大数据视角分析学习变革——美国《通过教育数据挖掘和学习分析促进教与学》报告解读及启示

“数据驱动学校,分析变革教育”的大数据时代已经来临,利用教育数据挖掘技术和学习分析技术,构建教育领域相关模型．探索教育变量之间的相关关系．为教育教学决策提供有效支持将成为未来教育的发展趋势。“大数据”的出现．将掀起人类教与学的又一次变革。2012年,美国国家教育部发布了《通过教育数据挖掘和学习分析促进教与学》报告．对美国国内大数据教育应用领域和案例。以及应用实施所面临的挑战进行了详细的介绍。借鉴此报告．我们认为未来我国教育领域的大数据研究和应用。应加强国家和地方对相关的研究和应用在技术层面、管理体制层面以及法律制度层面的支持,按需合理规划具体研究和应用,整合现有资源,发挥后进优势,借助“大数据”实现真正意义上的个性化学习．进而实现教育公平。     

MAGIS: mobile augmented-reality games for instructional support

ABSTRACT

We present Mobile Augmented-Reality Games for Instructional Support (MAGIS), a framework for the development of mobile augmented-reality (AR) games for education. The framework supports off-the-shelf, state-of-the-art technologies that enable AR tracking and rendering on consumer-level mobile devices, and integrates these technologies with content-generation tools that simplify the development of educational AR games, especially those that extensively use narrative-based game design and player-location tracking such as location-based historical or museum adventure games. We use Igpaw: Intramuros, a proof-of-concept game developed using MAGIS, to help describe the current state of the framework and to show its efficacy for implementing outdoor location-based educational games, and we briefly outline future development plans to improve MAGIS’ AR support (especially those involving indoor scenarios) as well as to improve the instructional design and authoring phases of AR applications written using this framework.     

浅论苏轼“以才学为诗”

宋代诗人喜欢"以才学为诗"。苏轼尤甚。苏轼之所以喜好"以才学为诗",自有其不便言说的独特的创伤初衷,这就是:(1)要抒胸中"忧愁不平气",(2)作家(诗人)与学者兼于一身特殊身份所使然,(3)强烈开宗立派意识所驱使。由此形成了苏轼独特的文本类型,即:(1)用典使事型,(2)多次韵型,(3)艺术鉴赏型,(4)因物寓理型。同时,苏轼在"以才学为诗"的实际操作中,自有其一套与之相适应的审美特征:一是经史百家学问穿插互用;二是以才学寓托诗人心志和寻求精神之"适";三是凭才学拓展诗歌题材。此外,苏轼"以才学为诗"具有极其重要的创作意义。     

解析PKS背诵模式的认知优势——基于思维导图和词块的应用

PKS背诵模式从语言学和认知心理学的角度对背诵重新解析.该模式以词块为关键词作为背诵的基本单位,以图片表达词块帮助学习者形成视觉映像,利用思维导图管理词块,再辅助以视听读结合的输入方式,寓背诵于娱乐,激发学习者的学习动机,提高背诵效率.PKS背诵有着强大的认知优势,词块可以清除理解障碍,思维导图可以促进背诵图式的建构,二者结合拥有强大的助记功能.该模式的应用有利于培养学习者的想象力和自主学习能力.     

高校学报的版式设计与文字编排

通过对高校学报版式设计与文字编排的研究分析,指出了高校学报版式设计的重要性,提出了通过追求文字编排中各元素的变化与统一,强调了版式设计要有独创风格,从而更好地满足读者的视觉享受。     

Exploring adverse drug reactions of diabetes medicine using social media analytics and interactive visualizations

The aim of this study is to propose an automatic and real-time social media analytics framework with interactive data visualizations to support effective exploration of knowledge about adverse drug reaction (ADR) surveillance. This proposed framework has been prototypically implemented on the basis of social media data. A longitudinal diabetes patient online community data (AskaPatient.com) as well as FDA Adverse Event Reporting Systems (FAERS) data as a benchmark were used to evaluate our proposed approach’s performance. Based on the results, our approach significantly increases the precision and accuracy for ADR extraction. The number of ADR cases, the time when the ADRs occurred, and the rating of Glucophage have been visualized that resulted by mining a collection of 870 ADRs posted in Askapatents.com over a certain time period (from 2001 to 2015). The results have important implications for pharmaceutical companies and hospitals wishing to monitor ADRs of medicines.     

Creating value from Social Big Data: Implications for Smart Tourism Destinations

This paper aims to demonstrate how the huge amount of Social Big Data available from tourists can nurture the value creation process for a Smart Tourism Destination. Applying a multiple-case study analysis, the paper explores a set of regional tourist experiences related to a Southern European region and destination, to derive patterns and opportunities of value creation generated by Big Data in tourism. Findings present and discuss evidence in terms of improving decision-making, creating marketing strategies with more personalized offerings, transparency and trust in dialogue with customers and stakeholders, and emergence of new business models. Finally, implications are presented for researchers and practitioners interested in the managerial exploitation of Big Data in the context of information-intensive industries and mainly in Tourism.     

An extension of the technology acceptance model in the big data analytics system implementation environment

Research on the adoption of systems for big data analytics has drawn enormous attention in Information Systems research. This study extends big data analytics adoption research by examining the effects of system characteristics on the attitude of managers towards the usage of big data analytics systems. A research model has been proposed in this study based on an extensive review of literature pertaining to the Technology Acceptance Model, with further validation by a survey of 150 big data analytics users. Results of this survey confirm that characteristics of the big data analytics system have significant direct and indirect effects on belief in the benefits of big data analytics systems and perceived usefulness, attitude and adoption. Moreover, there are mediation effects that exist among the system characteristics, benefits of big data analytics systems, perceived usefulness and the attitude towards using big data analytics system. This study expands the existing body of knowledge on the adoption of big data analytics systems, and benefits big data analytics providers and vendors while helping in the formulation of their business models.     

A knowledge-based semantic framework for query expansion

Searching for relevant material that satisfies the information need of a user, within a large document collection is a critical activity for web search engines. Query Expansion techniques are widely used by search engines for the disambiguation of user’s information need and for improving the information retrieval (IR) performance. Knowledge-based, corpus-based and relevance feedback, are the main QE techniques, that employ different approaches for expanding the user query with synonyms of the search terms (word synonymy) in order to bring more relevant documents and for filtering documents that contain search terms but with a different meaning (also known as word polysemy problem) than the user intended. This work, surveys existing query expansion techniques, highlights their strengths and limitations and introduces a new method that combines the power of knowledge-based or corpus-based techniques with that of relevance feedback. Experimental evaluation on three information retrieval benchmark datasets shows that the application of knowledge or corpus-based query expansion techniques on the results of the relevance feedback step improves the information retrieval performance, with knowledge-based techniques providing significantly better results than their simple relevance feedback alternatives in all sets.     

Time gap analysis by the topic model-based temporal technique

This study proposes a temporal analysis method to utilize heterogeneous resources such as papers, patents, and web news articles in an integrated manner. We analyzed the time gap phenomena between three resources and two academic areas by conducting text mining-based content analysis. To this end, a topic modeling technique, Latent Dirichlet Allocation (LDA) was used to estimate the optimal time gaps among three resources (papers, patents, and web news articles) in two research domains. The contributions of this study are summarized as follows: firstly, we propose a new temporal analysis method to understand the content characteristics and trends of heterogeneous multiple resources in an integrated manner. We applied it to measure the exact time intervals between academic areas by understanding the time gap phenomena. The results of temporal analysis showed that the resources of the medical field had more up-to-date property than those of the computer field, and thus prompter disclosure to the public. Secondly, we adopted a power-law exponent measurement and content analysis to evaluate the proposed method. With the proposed method, we demonstrate how to analyze heterogeneous resources more precisely and comprehensively.