Thursday, October 16, 2014

A history of the blackboard: How the blackboard became an effective and ubiquitous teaching tool.

Corollary: The most effective tool for conceptual data modeling is a whiteboard.

The blackboard-centered classroom offers more than pedagogical efficiency; it also offers an effective set of teaching possibilities. In such a classroom students are focused on the teacher (on a good day), but most importantly, they are focused. The teacher is not the focus of the class but rather a lens through which the lesson is created and clarified. The teacher draws the class toward her, but she projects the lessons onto the blackboard behind her, a blank surface upon which smaller ideas may be gathered into larger ones. The blackboard is the surface of thought.

At Maddy’s middle school, Smart Boards are now front and center, and on these interactive whiteboards, she and her fellow scholars and their teachers can connect to the Internet and display bits and pieces of information, work out problems and ideas, annotate and edit their work, shuffle digital objects spatially in order to find new connections. The Smart Board is futuristic, yet it serves the same purpose as the blackboard of my childhood. It gives the student more than something to look at; it provides a necessary focus.

A history of the blackboard: How the blackboard became an effective and ubiquitous teaching tool.

Friday, August 1, 2014

Premium offerings for reader engagement look an awful lot like news literacy : Columbia Journalism Review

News literacy—an important component of information literacy—includes having some sense of how media outlets collect and disseminate the news.

Still, characterizing paid, “behind-the-scenes” access to reporting-and-publishing processes as an opportunity to increase news literacy seems excessively cheery…

This behind-the-scenes access to journalists, their reporting secrets, and their reading habits sounds a lot like what goes on in most journalism classes—journalism professors (many of whom are working journalists) regularly share tips for reporting in the field, how they broke stories, and which news sources to pay attention to. But online, these anecdotes are being offered to consumers rather than aspiring creators of journalism. And while it might be a novel revenue model (successful or not), it’s a tried and true news literacy model.

…especially when those who buy in must think they can afford it…

While the impetus for these new premium offerings is revenue, and they don’t purport to be an educational service, they are indeed fulfilling a desire for news literacy-type information, and they end up being learning opportunities—though exclusively available to those who can pay for them.

…and those premium subscribers are probably news groupies in the first place.

Premium offerings for reader engagement look an awful lot like news literacy : Columbia Journalism Review

Thursday, July 31, 2014

Language Log » The state of the machine translation art

And here’s an example of poor unstructured data yielding useless results.

However, to be fair to the statistical machine translation industry, we must allow for any defects in the quality of the input. And after the above paragraphs were posted, Daniel Sterman, an experienced editor with a thorough knowledge of Hebrew, gave me this very useful analysis, which makes a considerable difference:

The original Hebrew is riddled with spelling and grammatical errors, which is why machine translation didn't work. You mentioned in your post "with limited errors" – this sentence's errors go well beyond that, and far into the realm of "my translation software was never designed to handle this level of idiocy".

Language Log » The state of the machine translation art

Extemporaneous Comments on Data Quality

At the MIT Chief Data Officer & Information Quality Symposium last week, I sat down for an on-camera chat with theCube, a production of siliconangle.  Topics included:

  • Why the state of the art in unstructured data quality lags so far behind that for structured data quality.
  • A few ways to apply structured-data DQ techniques to unstructured data.
  • Why Big Data is not revolutionary, and why every Chief Data Officer needs to recognize that.

Watch the video.

Wednesday, July 30, 2014

It's Time to Push Back When Government Controls the Message -

Public relations experts and their clients will frame this as an attempt to honor data quality by keeping the message tight and on point.  But those who see this as a threat to data quality are correct.

Rick Santorum was talking — but not quite without interruption. In a 2005 interview with Mark Leibovich, then of the Washington Post and now of the New York Times, the Republican senator from Pennsylvania was describing how he felt at the funeral of Pope John Paul II.

As Mr. Leibovich wrote it, part of the Santorum interview went like this:

“It’s part of the awe of this job that I do,” he says. “Every day. You’re making these decisions and … ” He fights for the right words. “It’s a great — —”

“Is it humbling, senator?” Robert Traynham, his communications aide, interjects.

“Yes, it’s very humbling!”

“And it’s uniquely American, isn’t it, Senator?” prompts Traynham.

“Oh, absolutely.”

That telling snippet — superbly handled by Mr. Leibovich — was pointed out recently by Ron Fournier, the National Journal columnist and former Associated Press Washington bureau chief, who is one of many journalists pushing back against a pervasive practice: Interviews with government officials that include public relations “minders.”

It's Time to Push Back When Government Controls the Message -

Wednesday, May 7, 2014

Speaking and Writing; Complexity and Agility

Lurking within this post is a rebuttal to the following principle of Agile Development:

The most efficient and effective method of
conveying information to and within a development
team is face-to-face conversation.

Near the end, the post compares spoken and written communication:

In a sense, RSVP [Rapid Serial Visual Presentation – Ed.], with its inflexibility in the timing of information flow, turns reading back into something a lot more like spoken language comprehension, though without some of the nuanced information we get from intonation or facial and body movements. Clearly, we do manage to cope with spoken language, even without the benefits of regressive listening or control over the presentation rate of speech. What written language does, though, is liberate language from the temporal tyrannies that are present during the comprehension—and production—of language. This is one of the main reasons why written language often achieves a complexity that is seldom heard in spoken language. [Emphasis mine – Ed.]

Note: In this context, RSVP involves presenting sentences to the reader one word at a time—at a fixed location on a screen—using a controlled rate of presentation. That removes an essential aspect of reading: the ability to backtrack to reconcile a parsing ambiguity, a homograph, or any other source of confusion.

Language Log: So much to read, so little time

Principles behind the Agile Manifesto

Data Accessibility in Linguistics Research

Praiseworthy information responsibility in linguistics research.

I'll spare you the details, though I intend to try some of the ideas out myself later. What I want to underline here is something that the six papers in the session had in common.

What they all had in common was that they reported results on published databases. Two papers used NIST SRE 2008 data. Three papers used theNIST RT05, RT07, RT08, and/or RT09 datasets. One paper used the AMI corpus. And one used the REPERE collection.

None of the presentations used proprietary or unpublished data. This illustrates the fact that in most speech processing fields, it has become normal to cite the performance of new algorithms on data that is also available to others, so that comparisons are quantitatively meaningful.

In some sense, this is also really about accessibility. When you want to evaluate or extend someone's ideas, it's critical to be able to replicate their work — and that requires access to the datasets they analyzed.

Language Log: Accessibility and diarization