Study Smarts: How to Learn More in Less Time (Study smart series)
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My primary data source is Twitter, a social media platform that is widely used by experts and laypeople alike for communication around crises, including forecast and risk information for hurricanes. Through this approach, researchers in HCI and information science rely not on what a person says they would do during a hypothetical disaster, nor on what one remembers they did in a past real disaster, which is done in an experimental lab setting or in a survey; instead, this approach is based on what people actually do—as they report it on Twitter—in real time, in a real event.
However, Twitter data does not magically provide insights about people for a specific topic such as uncertainty. Using this platform for this kind of research requires a thoughtful methodological approach. To understand how people make sense of uncertain information around a particular event, the research does not just collect and analyze every tweet posted in relation to that event.
For one, this would return hundreds of millions of tweets, a scale at which only quantitative analysis could be applied, limiting meaningful sociobehavioral insights. Additionally, many if not most of these tweets would have nothing to do with uncertainty and risk, as this is only one topic out of many that people experience and then report on Twitter. To gain meaningful insights about how people experience and make sense of uncertainty around hurricanes, I use a human-centered data science approach. This involves 1 collecting contextual data, 2 applying context-sensitive methods, and 3 iterating between the micro scale of individual activity and the macro scale of social dynamics.
University of Colorado, Boulder, Motivated by this approach, I implemented a research agenda based on a dataset of tweets during the Atlantic hurricane season. Specifically, these tweets contain visual representations images portraying hurricane forecasts and risks—e. The data collection also includes associated contextual data, including all the retweets, quote tweets, and replies attached to these authoritative-sourced risk image tweets.
Using a variety of context-sensitive methods, the research investigates how and why various risk representations diffuse differently on Twitter and how people make sense of a particular type of risk representation portraying uncertainty in hurricanes, the spaghetti plot. Figure 1. Cone of uncertainty Cone of uncertainty forecast graphic showing the probable track of Hurricane Maria. Source: ABC7. Figure 2. Radar image Radar imagery of Hurricane Irma.
Figure 3. Spaghetti plot Spaghetti plot showing projected paths from various computer models for Hurricane Irma. Source: 10News. First, we used a quantitative approach to study the data at a macro scale to measure the diffusion of various types of hurricane risk image tweets, or how much people engage with them. We thought this would be an indicator of how much people use each type of tweet in their decision-making and risk processing during hurricanes.
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We wanted to measure more active engagement with tweets that could also provide more context for why someone engaged, so we chose instead to use replies, retweets, 8 Though retweets reflect a more passive form of engagement than replying or quote-tweeting—both of which entail the user writing additional content—they are still the most common way of measuring diffusion on Twitter. We hope that by including reply and quote tweet diffusion in this research, these forms of engagement will become just as normalized as retweets for future diffusion work, especially because they provide context about the diffusing user beyond the retweet.
We also were interested in temporal patterns of diffusion, i. These novel diffusion metrics consider the diffusion of information on Twitter contextualized within the rapid and evolving nature of hurricanes themselves. The diffusion results from this analysis alone do not tell the full story, but instead inform how to conduct subsequent qualitative analyses on the content of the contextual data surrounding different kinds of images, iterating back to the micro scale of individual activity.
In particular, this involved analysis of replies and quote tweets. For instance, we found that cone of uncertainty tweets continued to be replied to for a significantly longer amount of time than other categories, so we investigated the long-term replies to these tweets. Many people reply to cone of uncertainty image tweets with questions grounded in managing uncertainty, i. What does a normal hurricane look like? Asking for those of us with no expertise on the subject. A deeper analysis into one type of forecast image, the spaghetti plot , elaborates upon these high-level findings across many image categories.
In a study that is currently underway, we use a technique called discourse analysis to analyze conversations i. Spaghetti plot images similar to figure 3 were chosen for this analysis because they so explicitly portray meteorological uncertainty, and preliminary analyses also revealed that the audience for these images expressed uncertainty about them. That still seem pretty reasonable even given the continued eastward shift? However, there are other responses to spaghetti plot tweets that reflect unique ways of managing and expressing uncertainty as compared to other risk image types.
Spaghetti plots are themselves unique since they display a range of hurricane track forecasts produced from various computer models rather than a single forecast. You got this Pulling4Purple. These analyses into how people engage with and make sense of hurricane forecast imagery reveal a variety of forms of uncertainty. In addition to the meteorological uncertainty inherent in the forecast information, it is important to address the uncertainty that arises when people contextualize the information to their own situations.
While from a meteorological point of view it may be important to communicate the multitude of tracks a hurricane may take, for instance, members of the public may just want to know whether they should cancel their Disneyland trip. Uncertainty around hazardous weather and risk representations is not only expressed via questions, but also statements of hope and team-like support, presenting opportunity for risk communicators to address uncertainty in new ways.
Her research investigates risk communication for hurricanes among authoritative communicators and members of the public via social media in order to support the public's risk assessments and decision making. Social Science Research Council.
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The SSRC is an independent, international, nonprofit organization. It fosters innovative research, nurtures new generations of social scientists, deepens how inquiry is practiced within and across disciplines, and mobilizes necessary knowledge on important public issues. Contents Download Save. Don't Read This pp. Part I: Learning Tips pp. Beg, Borrow, or Make a Course Outline pp. Figure Out What the Goal Is pp.
Learn the Special Vocabulary pp. Skim Book Prefaces, Intros, and Such p. Cut Your Reading Time in Half pp.
Study Smarts: How to Learn More in Less Time (Study Smart Series)
Beware the Over-Underlined Textbook pp. Catch the Lecturer's Clues pp. Learn by Taking Organized Notes pp. Devise a Lecture Shorthand pp.
airtec.gr/images/rastrear-celular/3153-rastrear-un-celular.php Get It Right the First Time pp. Preview for Lectures, Prethink for Discussions and Seminars pp. Work Through All Sample Problems pp. Part II: Remembering Tips pp. Train Your Brain to Think on Cue pp. Eliminate Brain Interference pp. Reinforce the Right Memories pp.
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Roll with Your Alertness Cycles pp. Make Sleep Work for You p. Space Out Your Practice Times pp. Make Your Muscles Do the Remembering p. See and Say pp. Keep Each Study Session Short pp.