人教版高中英语新课程教材中,语言运用（Using Language）是每个单元必不可少的部分,提供了围绕单元中心话题的听、说、读、写的综合性练习,是单元中心话题的延续和升华.如何设计Using Language部分的教学,使自己的教学模式既不落俗套,又能真正体现新课程标准所倡导的教学理念,正是广大一线英语教师一直努力探索的问题.

打磨Using Language,倡导新理念

The Sage Encyclopedia of Qualitative Research Methods

the cambridge handbook of the learning sciences is available in our digital library an online access to it is set as public so you can download it instantly. Our books collection spans in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the the cambridge handbook of the learning sciences is universally compatible with any devices to read.

The Cambridge Handbook Of The Learning Sciences

The Sage Encyclopedia of Social Science Research Methods

Purpose - The aim of this paper is to present a sampling method using virtual networks to study "hard-to-reach" populations. In the ambit of social research, the use of new technologies is still questioned because the selection bias is an obstacle to carry on scientific research on the Internet. In this regard, the authors' hypothesis is that the use of social networking sites (Web 2.0) can be effective for the study of "hard-to-reach" populations. The main advantages of this technique are that it can expand the geographical scope and facilitates the identification of individuals with barriers to access. Therefore, the use of virtual networks in non-probabilistic samples can increase the sample size and its representativeness. Design/methodology/approach - To test this hypothesis, a virtual method was designed using Facebook to identify Argentinean immigrant entrepreneurs in Spain (214 cases). A characteristic of this population is that some individuals are administratively invisible in national statistics because they have double nationality (non-EU and EU). The use of virtual sampling was combined with an online questionnaire as a complementary tool for Web 2.0 research in behavioural sciences. Findings - The number of cases detected by Facebook and the virtual response rate is higher than traditional snowball technique. The explanation is that people increase their level of confidence because the researcher shows his personal information (Facebook's profile) and also participates in their groups of interest (Facebook's groups). Moreover, the online questionnaires administration allows the quality of the information to be controlled and avoids duplication of cases. Originality/value - The present article is the first that uses Facebook as an instrument to study immigrants. Therefore its adoption represents a great challenge in the social research field because there are many barriers of access and search. It also proposes a novel mix of traditional methodologies updated with the use of new virtual possibilities of studying hard to reach populations, especially in areas of social research where the contributions of these methods are less developed.

Social research 2.0: virtual snowball sampling method using Facebook

Increasingly, algorithms are used to make important decisions across society. However, these algorithms are usually poorly understood, which can reduce transparency and evoke negative emotions. In this research, we seek to learn design principles for explanation interfaces that communicate how decision-making algorithms work, in order to help organizations explain their decisions to stakeholders, or to support users' "right to explanation". We conducted an online experiment where 199 participants used different explanation interfaces to understand an algorithm for making university admissions decisions. We measured users' objective and self-reported understanding of the algorithm. Our results show that both interactive explanations and "white-box" explanations (i.e. that show the inner workings of an algorithm) can improve users' comprehension. Although the interactive approach is more effective at improving comprehension, it comes with a trade-off of taking more time. Surprisingly, we also find that users' trust in algorithmic decisions is not affected by the explanation interface or their level of comprehension of the algorithm.

Explaining Decision-Making Algorithms through UI: Strategies to Help Non-Expert Stakeholders

The sharing economy has quickly become a very prominent subject of research in the broader computing literature and the in human--computer interaction (HCI) literature more specifically. When other computing research areas have experienced similarly rapid growth (e.g. human computation, eco-feedback technology), early stage literature reviews have proved useful and influential by identifying trends and gaps in the literature of interest and by providing key directions for short- and long-term future work. In this paper, we seek to provide the same benefits with respect to computing research on the sharing economy. Specifically, following the suggested approach of prior computing literature reviews, we conducted a systematic review of sharing economy articles published in the Association for Computing Machinery Digital Library to investigate the state of sharing economy research in computing. We performed this review with two simultaneous foci: a broad focus toward the computing literature more generally and a narrow focus specifically on HCI literature. We collected a total of 112 sharing economy articles published between 2008 and 2017 and through our analysis of these papers, we make two core contributions: (1) an understanding of the computing community's contributions to our knowledge about the sharing economy, and specifically the role of the HCI community in these contributions (i.e.what has been done) and (2) a discussion of under-explored and unexplored aspects of the sharing economy that can serve as a partial research agenda moving forward (i.e.what is next to do).

https://dl.acm.org/doi/pdf/10.1145/3134673

The Sharing Economy in Computing: A Systematic Literature Review

We describe a new set of interaction techniques that allow users to interact with physical objects through augmented reality (AR). Previously, to operate a smart device, physical touch is generally needed and a graphical interface is normally involved. These become limitations and prevent the user from operating a device out of reach or operating multiple devices at once. Ubii (Ubiquitous interface and interaction) is an integrated interface system that connects a network of smart devices together, and allows users to interact with the physical objects using hand gestures. The user wears a smart glass which displays the user interface in an augmented reality view. Hand gestures are captured by the smart glass, and upon recognizing the right gesture input, Ubii will communicate with the connected smart devices to complete the designated operations. Ubii supports common inter-device operations such as file transfer, printing, projecting, as well as device pairing. To improve the overall performance of the system, we implement computation offloading to perform the image processing computation. Our user test shows that Ubii is easy to use and more intuitive than traditional user interfaces. Ubii shortens the operation time on various tasks involving operating physical devices. The novel interaction paradigm attains a seamless interaction between the physical and digital worlds.

Ubii: Physical World Interaction Through Augmented Reality

Recent work in fair machine learning has proposed dozens of technical definitions of algorithmic fairness and methods for enforcing these definitions. However, we still lack an understanding of how to develop machine learning systems with fairness criteria that reflect relevant stakeholders’ nuanced viewpoints in real-world contexts. To address this gap, we propose a framework for eliciting stakeholders’ subjective fairness notions. Combining a user interface that allows stakeholders to examine the data and the algorithm’s predictions with an interview protocol to probe stakeholders’ thoughts while they are interacting with the interface, we can identify stakeholders’ fairness beliefs and principles. We conduct a user study to evaluate our framework in the setting of a child maltreatment predictive system. Our evaluations show that the framework allows stakeholders to comprehensively convey their fairness viewpoints. We also discuss how our results can inform the design of predictive systems.

Soliciting Stakeholders’ Fairness Notions in Child Maltreatment Predictive Systems

AI-based decision support tools (ADS) are increasingly used to augment human decision-making in high-stakes, social contexts. As public sector agencies begin to adopt ADS, it is critical that we understand workers’ experiences with these systems in practice. In this paper, we present findings from a series of interviews and contextual inquiries at a child welfare agency, to understand how they currently make AI-assisted child maltreatment screening decisions. Overall, we observe how workers’ reliance upon the ADS is guided by (1) their knowledge of rich, contextual information beyond what the AI model captures, (2) their beliefs about the ADS’s capabilities and limitations relative to their own, (3) organizational pressures and incentives around the use of the ADS, and (4) awareness of misalignments between algorithmic predictions and their own decision-making objectives. Drawing upon these findings, we discuss design implications towards supporting more effective human-AI decision-making.

/pdf/improving-human-ai-partnerships-in-child-welfare-2snypjav.pdf

Hao Fei Cheng

Papers

Explaining Decision-Making Algorithms through UI: Strategies to Help Non-Expert Stakeholders

The Sharing Economy in Computing: A Systematic Literature Review

Ubii: Physical World Interaction Through Augmented Reality

Soliciting Stakeholders’ Fairness Notions in Child Maltreatment Predictive Systems

Improving Human-AI Partnerships in Child Welfare: Understanding Worker Practices, Challenges, and Desires for Algorithmic Decision Support