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Posts Tagged ‘information


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The video above has a clickbait title — this one weird trick will help you spot clickbait.

The examples highlight not one but three strategies when evaluating clickbait titles of news or video reports:

  1. Drawing a line between cause and effect
  2. Understanding the impact of sample size on reported results
  3. Distinguishing between statistical or scientific significance and practical bearing

Crash Course provided a ten-part series called Navigating Digital Information. But what good is claiming to be information literate if you cannot prove it?


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This TED-Ed video is a quick test on applying some of that knowledge by evaluating misleading headlines.

The video title states that this test is Level 1. So will there be more difficult tests?

Information retention is not all that defines learning. It is the basis of learning and that is why there are so many theories put into practice (as well as atheoretical practices) that claim to help students remember new information better.

This SciShow Psych video started by pointing out the fallacy of learning styles. It did not go into details perhaps because it already made a video about this in 2016. That video skimmed the surface. A much older video by Daniel Willingham provides more details*.


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The current video highlighted three information retention strategies and the research backing them up. I summarise the three concepts in my own words.

Spaced practice: Spread the learning and practice of new information or skills over as much time and in as many contexts as possible. This is the opposite of cramming — doing as much as possible in as little time as possible.

Quizzing: Test yourself to create retrieval-based learning. The rationale: You do not know what you know or do not know until you test yourself. Combine quizzing with spaced practice and you create more associations with content.

Design for active learning: Get students to teach the content, work on projects, or solve problems collaboratively. Duh.

*Side note on Willingham’s video: He might be a cognitive psychologist and neuroscientist, but he is not a geographer. The example of visualising Algeria in a map of Africa was wrong; he highlighted Chad instead.

I am sad. This is the last episode of Crash Course’s series on Navigating Digital Information.


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This week’s focus was social media.

Host John Green started by outlining how social media has had far reaching consequences, e.g., shaping our vocabulary, changing our expectations of privacy, organising grassroots efforts.

But probably the most important impact of social media might be that it is now the most common source of information and news. This includes disinformation, misinformation, and fake news, all of which are easy to spread with a click or a tap.

The ease of creating, sharing, and amplifying is social media’s best and worst set of affordances. The affordances are neutral, but we can choose to bully and mislead, or make new friends and organise special interest groups.

Regardless of their purpose, social media are powered by targetted advertising and algorithms. Both affect what we read, hear, or watch in our feeds. This can create filter bubbles.

This insulation is a result of social media companies needing to keep us engaged. A consequence of this is that we might not get to process dissenting views or the truth behind the lies we are fed.

If we know what drives social media, we could take Green’s advice by:

  • Following entities that have different perspective from us.
  • Deactivating the recommended results or top posts so that you get a more neutral feed.
  • Avoid going down rabbit holes (deep dives of content or perspectives that result in more of the same or the extreme).
  • Exercising click restraint and practising lateral reading.
  • Having the courage and taking the effort to correct mistakes.

The week’s episode of Crash Course’s navigating digital information focused on click restraint.


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Click restraint is about not relying on the first few returns in Google search. It is about scanning, analysing, and evaluating the rest of the returns. It is not about immediate gratification but about figuring out the most valid and reliable sources of information.

Why exercise click restraint?

Searches are not objective. The search algorithms (rules) are shaped by us and the results are processed by us. We do all these based on our perspectives, biases, or bubbles.

How might we exercise click restraint?

By analysing the search results first:

  1. Scan the titles and URLs of the results for their sources
  2. Read the snippets or blurbs that accompany the titles or URLs
  3. Try to determine if the nature of the resource — opinion piece, satire, official report, etc.

This week’s episode of Crash Courses’s Navigating Digital Information focused on data and its visual representation.


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Data, whether represented by raw numbers or graphics, can seem objective. However, they are not neutral because people gather and interpret them. (As a former academic, I shuddered whenever I overhead colleagues talking about “massaging data”.)

In evaluating data, host John Green reminded us to ask:

  • Does the data support the claim? (Is it relevant?)
  • How reliable is the source of data? (Who commissioned the research and why? Who conducted it and why?)

As for data visualisations, Green reminded us to check if the graphic was based on real data (check its source) and that the data was transferred and presented accurately.

Another consideration specific to data visualisations like infographics is how complex phenomena are simplified in the creative process. This might sacrifice the accuracy of the data.

If we combine both sets of principles, we might be in a stronger position to evaluate the following example. Two organisations, used the same set of data to send messages on climate change.

Organisation A’s image is on the left and B’s is on the right.

Screenshot of graphs from https://www.youtube.com/watch?v=OiND50qfCek&t=201s.

Organisation A had already concluded that temperatures were not rising globally over time, so it manipulated the y-axis to range from -10 to 110 deg F. Organisation B zoomed in a smaller range and the average temperature increase was more pronounced. B critiqued A’s representation as misleading.

Both organisations used relevant data that supported their claims. The data was sourced from a neutral third party (NASA’s GISS). However, the presentation was manipulated by A to obscure the trend.

My perspective: Seeing should not immediately lead to believing because the data might be selectively or “sexily” presented. The first is only sharing data that supports preconceived notions; the second is using elaborate or compelling-looking visuals to disinform or lie.

A side note: Have you ever noticed that “lie” is central to believe?

This week’s Crash Course’s video on navigating digital information focused on evaluating images and videos.


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Host John Green provided examples of how images could be used to represent and misrepresent both context and context. If it is easy to fool someone with text, it is even easier with images.

When presented with any image we might verify its context and content by a) seeking its source and determining its reliability, and b) searching laterally for its validity. If links or cues are not available in a suspicious image, we might use Google’s image search or Tineye to evaluate its worth.

How about videos? The principles are the same: Determine the veracity of its source, the reputation of its creator(s), and whether or not is was altered. It might be difficult to do the last item nowadays, but difficult is not impossible. What works for text also works for videos — search, read, and watch laterally.


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