远程学习者学业求助策略研究

远程学习者在学习过程中遇到困难时进行积极地学业求助,是一种比较好的解决问题的策略。对当前远程学习者在学业求助行为倾向、求助对象、求助方式等方面的调查研究发现：我国远程学习者具有比较积极地学业求助行为倾向,能够进行工具性求助,但也存在执行性求助和回避求助等不良的学业求助策略。以网络课程为平台,建设学业求助的硬环境;以教师为主导、同伴协作为途径,设计学业求助的软环境;激发学习者个体的自我效能,促进远程学习者的学业求助策略的改进。     

Leveraging spectator emotion: A review and conceptual framework for marketing health behaviors in elite sports

Elite sport provides an exciting and dynamic emotional experience for spectators. Social marketers using sport sponsorship to promote health messages are yet to consider the impact of the temporal emotional sporting context in which health messages are promoted. The authors provide a critical review of the evidence for the influence of emotion on behavior and seek to elucidate the implications for health sponsorship in sport. Articles were identified via electronic database searches and returned-article references, with thirty-three suitable articles included. Drawing on evidence from sport sponsorship, social marketing, advertising, and cause-related marketing, the review highlights few studies have investigated emotion, health communication, and behavior in a sport sponsorship context. The findings indicate that positive (e.g. happiness, compassion) and negative (e.g. fear, guilt) emotions influence intentions and behaviors with the interaction of message framing important when delivering social marketing messages. A conceptual model presents how emotions may influence behavioral health sponsorship outcomes and provides areas of future research to develop best policy and practice when delivering health sponsorship in a sporting context.     

居民用水阶梯式水价的可行性研究——政府视角下的模型分析

我国现行的居民用水定价方法总体上有四种,分别是统一定价、两部制定价、统一计量式定价和阶梯式定价。文章在分析了统一定价和两部制定价的优势和劣势后,主要就统一计量式定价和阶梯式定价两种计量收费模式进行分析,在这两种定价方式的约束下分别建立了居民和供水企业的水资源消费行为模型,并从政府的角度,本着节约用水、提高供水企业的运营效益、保障低收入家庭的基本生活用水三个原则,讨论得出阶梯式水价更适合我国现阶段国情。     

Assessing behavioral data science privacy issues in government artificial intelligence deployment

In today's global culture where the Internet has established itself as the main tool for communication and commerce, the capability to massively analyze and predict citizens' behavior has become a priority for governments in terms of collective intelligence and security. At the same time, in the context of novel possibilities that artificial intelligence (AI) brings to governments in terms of understanding and developing collective behavior analysis, important concerns related to citizens' privacy have emerged. In order to identify the main uses that governments make of AI and to define citizens' concerns about their privacy, in the present study, we undertook a systematic review of the literature, conducted in-depth interviews, and applied data-mining techniques. Based on our results, we classified and discussed the risks to citizens' privacy according to the types of AI strategies used by governments that may affect collective behavior and cause massive behavior modification. Our results revealed 11 uses of AI strategies used by the government to improve their interaction with citizens, organizations in cities, services provided by public institutions or the economy, among other areas. In relation to citizens' privacy when AI is used by governments, we identified 8 topics related to human behavior predictions, intelligence decision making, decision automation, digital surveillance, data privacy law and regulation, and the risk of behavior modification. The paper concludes with a discussion of the development of regulations focused on the ethical design of citizen data collection, where implications for governments are presented aimed at regulating security, ethics, and data privacy. Additionally, we propose a research agenda composed by 16 research questions to be investigated in further research.