Online Search Academy

 | December 27, 2011 |12 Ways to Be More Search Savvy



Google has made it possible for us to have instant information gratification. Just start typing the first letters of your search word and the site intuits your question and offers you the smartest choice of answers.

Seems simple enough. But as quick and facile as the process is, there are ways to be even more efficient, more search-savvy. And it’s our responsibility to teach kids how to find and research information, how to judge its veracity, and when it’s time to ask for a grownup’s help. Daniel Russell, Google’s “search anthropologist” in charge of Search Quality and User Happiness (yes, really), who brought to light some important tips you may not have known.

  1. CONTROL F. A deceptively simple tool, the Control F function (or Command F on Macs) allows you to immediately find the word you’re looking for on a page. After you’ve typed in your search, you can jump directly to the word or phrase in the search list. According to Russell, 90 percent of Internet users don’t know this, and spend valuable time scrolling through pages of information trying to find their key word. “They’re being terribly inefficient,” Russell says.
  2. KEEP IT SIMPLE. Use search terms the way you’d like to see them on a Web site. But think of how the author would phrase it. “It’s not about you, it’s about the author,” Russell says. “What would they say and how would they say it? What are some common terms and phrases they’d write? It’s the kind of thing that people over-think and are hyper-analytical about.” Stay on topic and keep it simple.
  3. DEFINE OPERATOR. This has to be one of the best items of Google’s offerings. To learn the definition of a word, just type “Define,” then the word.
  4. ONE MORE SEARCH. It’s one thing to do a quick search for Lady Gaga’s birthday. But for more important questions that have a direct implication on your life, do one more search. Go deeper and find a second corroborating source, just like a journalist would. “We are a credulous society,” Russell says. “When you have something you care about, something you’re going to spend a lot of money on, or an issue with your help, do one extra search. Never single-source anything.”
  5. FIND THE SOURCE. Russell knows first-hand that Web sites can sometimes publish false information. Though we all know how to find contact information for an organization, confirm the phone number, look for the author’s names and trustworthy hallmarks like logos, Russell says “the bad guys know that too. They’re very good at mimicking credible sources of information.” On the site, searchers can find details about the source: where it’s located, when it was established, and the IP address.
  6. CONFIRM CONTENT. It’s common to find the same phrases and sentences on different sites all over the Web because people duplicate content all the time. To determine the original source of the content, you can look at the date it was written, but that’s also not entirely accurate. When authors edit an article, that changes the posting date. So even if it was originally written in 2005, the date will say 2011 if it was edited last week. Again, here’s when you put on your journalist hat. Trustworthy websites typically have an “errata column” or something like it where mistakes or corrections are posted. Sites where you see strikethroughs (it looks this) publicly show where previously published information has been corrected or stricken. You’ll also see “Updates” at the top of articles, where clarifications are published, which shows the Web site’s intention of providing the most accurate information. “Those idioms were not practicable or doable in pre-technology days,” Russell says. “You have to understand how the practice of writing and publishing is changing.”
  7. LINK OPERATOR. The way Google ranks sites can be confusing. Sometimes even when a site has negative comments or reviews, it still rises to the top of the search list simply because it’s been mentioned the most. When you want to know what other sites are saying about the site you’re searching, type in “Link:” and you’ll see all the posts that mention that site. Whether it’s following up on a debatable article or the reputation of an online shop or person, it’s another incredibly useful research tool that didn’t exist in “pre-Web times,” as Russell puts it.
  8. DON’T USE THE + SIGN. It might have negative side effects, Russell says. Adding the + sign will force the search engine to look for only that phrase and may tweak the search in a way you didn’t intend. That said, it’s a useful tool for looking up foreign words or very low-frequency words.
  9. PAY ATTENTION TO “GOOGLE INSTANT.” In most cases, Google’s instant search function, which is fairly new, will accurately predict what you’re searching for and offer suggestions. “Pay attention to it,” Russell says. “You don’t need to keep typing!” And sometimes it’ll help you come up with the right words for your search phrase. It’s all part of tapping into the wisdom of the crowd, he says. “It’s good when you’re stuck in a hard research problem. Like ‘Which kind of hybrid vehicle should I buy?’ might result in ‘hybrid minivans’ or other ideas you might not have known about.’”
  10. SWITCH ON SAFETY MODE. If you’ve got kids in the house, Russell suggests enabling safe search. In your Search Settings, scroll down to SafeSearch Filtering (or use Control F to find it quickly!) and choose what level filter you want to use. You can tailor it to every computer in the house. Google offers all kinds of safe search tips and functions on Google’s Family Safety Center. And what to tell kids if they accidentally stumble upon an inappropriate site? “I always tell my kids the Internet is a big, wide place, and if you find something inappropriate, hit the “back” button,” he says. A teacher he knows tells her class to just instantly close the laptop when they find something objectionable. “It’s an instant signal to the teacher in a K-8 class that something is not right, and it gives the teacher the opportunity to talk about how the student got there, and how to avoid that in the future.” The tactic might not work as well in the high school setting, though, Russell jokes.
  11. FUNCTIONS GALORE. You can use Google to do calculations (just type in “Square root of 99″ or “Convert 12 inches to mm”). You can search patents, images, videos, language translations. And even if you can’t remember a Google function, you can easily search it. “I use Google to Google Google,” Russell says. “You don’t have to remember URLs.”
  12. LEFT-HAND SIDE TOOLS. Most people don’t notice these exist, but when you search a topic, a list of useful, interesting tools come up. For example, when you type in War of 1812, on the left hand side, you’ll see “Images,” “Videos,” etc., but below that you’ll see things like “Timeline,” which maps out a time sequence of events around the War of 1812 and links to each of those events. There’s also a dictionary, related searches, and a slew of other helpful links.

To those who wonder if Google is making us stupid, Russell has a pithy response: “Plato said that about books.”

I better go search that.


Udacity Leaves University Environment

Sebastian Thrun and Udacity’s “pivot” toward corporate training.

By , Slate,

“Sebastian Thrun, godfather of the massive open online course, has quietly spread a plastic tarp on the floor, nudged his most famous educational invention into the center, and is about to pull the trigger. Thrun—former Stanford superprofessor, Silicon Valley demigod, and now CEO of online-course purveyor Udacity—just admitted to Fast Company’s openly smitten Max Chafkin that his company’s courses are often a “lousy product.”

This is quite a “pivot” from the Sebastian Thrun, who less than two years ago crowed to Wired that the unstemmable tide of free online education would leave a mere 10 purveyors of higher learning in its wake, one of which would be Udacity. However, on the heels of theembarrassing failure of a loudly hypedpartnership with San Jose State University, the “lousiness” of the product seems to have become apparent. The failures of massive online education come as no shock to those of us who actually educate students by being in the same room with them—and, accordingly, Chafkin’s unabashed display of sycophantic longing hasblazed up the academic Internet.

But what is the big deal about Thrun’s pivot, and why are academics and higher-ed writers alternately wary and gleeful about it? On the surface, Thrun appears duly chagrinned that his brainchild, so proudly hailed in neoliberal wet dreams, has failed the tired, poor, and huddled masses yearning to learn for free. And on the surface, the new direction of Udacity, which is to leave the university environment and focus on corporate training courses, seems appropriate: Sure, go “disrupt” a bunch of corporations, they love that kind of thing.

What’s got the academic Internet’s frayed mom jeans in a bunch, however, is that Thrun’s alleged mea culpa is actually a you-a culpa. For Udacity’s catastrophic failureto teach remedial mathematics at San Jose State University, Thrun blames neither the corporatization of the university nor the MOOC’s use of unqualified “student mentors” in assessment. Instead, he blames the students themselves for being so damn poor.

The way Fast Company has it, Thrun chucks those San Jose State students under the self-driving Google car faster than he chugs up a hill on his custom-made road bike, leaving a panting Max Chafkin in the dust to ponder the following Thrunism: “These were students from difficult neighborhoods, without good access to computers, and with all kinds of challenges in their lives. … It’s a group for which this medium is not a good fit.”

The problem, of course, is that those students represent the precise group MOOCs are meant to serve. “MOOCs were supposed to be the device that would bring higher education to the masses,” Jonathan Rees noted. “However, the masses at San Jose State don’t appear to be ready for the commodified, impersonal higher education that MOOCs offer.” Thrun’s cavalier disregard for the SJSU students reveals his true vision of the target audience for MOOCs: students from the posh suburbs, with 10 tablets apiece and no challenges whatsoever—that is, the exact people who already have access to expensive higher education.

It is more than galling that Thrun blames students for the failure of a medium that was invented to serve them, instead of blaming the medium that, in the storied history of the “correspondence” course (“TV/VCR repair”!), has never worked. For him, MOOCs don’t fail to educate the less privileged because the massive online model is itself a poor tool. No, apparently students fail MOOCs because those students have the gall to be poor, so let’s give up on them and move on to the corporate world, where we don’t have to be accountable to the hoi polloi anymore, or even have to look at them, because gross.

Successful education needs personal interaction and accountability, period. This is, in fact, the same reason students feel annoyed, alienated, and anonymous in large lecture halls and thus justified in sexting and playing World of Warcraft during class—and why the answer is not the MOOC, but the tiny, for-credit, in-person seminar that has neither a sexy acronym nor a potential for huge corporate partnerships.

Granted, Thrun’s hasty retreat from a full university takeover is delightful for advocates of actual education, and his new vocational focus seems like a great idea for its participants at first glance. But here’s the other problem, which is perhaps even more pernicious: The single thing MOOCs unequivocally do better than traditional educational methods is play to the distinct advantages of the advantaged. Congratulations?

As Audrey Watters and Mike Caulfield have both argued, Thrun’s new venture will still probably have a 93 percent attrition rate, and that attrition rate does not actually seem to bother its creator. For Thrun has implied that MOOC failures are chaff being separated from wheat, the herd being thinned in a meritocracy, a “feature” rather than a bug in the system, as Caulfield has put it.

And just as with university MOOCs, those 7 percent who make it are going to be the same 7 percent who always make it: individuals who are not from “rough” neighborhoods, who have easy access to resources, and none of the pesky “challenges” that come from attempting to survive in an economy rigged against them. The workers who will thrive in a corporate training MOOC are those who do not need a MOOC to get a promotion.

If the only university students who can benefit from a MOOC are those who can already afford an elite education, and if the only corporate trainees who succeed are those already primed for success, then what is the point? Thrun’s admission seems to have “pivoted” the MOOC to premature obsolescence. Perhaps the professoriate’s latest source of terror—our wholesale replacement by actors strutting and fretting upon a new kind of stage—will never come about. As Rees puts it, Thrun has done us “a huge favor by demonstrating the value of what most of us do every day,” proving “beyond a shadow of a doubt that real higher education can’t be automated.”

This takeaway has the potential to be monumental for the future of higher education: MOOCs reify, rather than break down, privilege barriers, and as such they are not the disruptive solution their hagiographers insisted they were. The problems MOOCs were supposed to solve still plague the current university, of course. But skyrocketing tuition and a faculty labor crisis will need a different kind of savior—one who doesn’t show up in a driverless car.”


This is how Sebastian Thrun describes himself and his mission on his homepage at Stanford: “I am a research professor at Stanford, a Google Fellow, and a co-founder of Udacity.

At Google, founded Google X, which is home to projects like the Google self-driving carand the recently announced Google Glass. We are trying to radically innovate, innovate, innovate. And I am on a mission to learn from Google’s amazing founders, Sergey and Larry.

At Udacity, we are trying to democratize higher education. Udacity stands for “we are audacious, for you, the student”. This is an audacious step, and it has been a thrill ride.

At Stanford, I still have my research group, after giving up my tenure earlier in 2011. We do all sorts of research on using AI to improve people’s daily lives.”

I wonder whether the whole concept will be transfered to by Sebastian Thrun. Let the monopolization begin.




Rebecca Schuman is an education columnist for Slateand adjunct professor at the University of Missouri–St. Louis.

Advantages and Disadvantages of the Khan Academy Model


For my learning experience I chose a subject in mathematics. The Khan model enables students to control the time, location, and speed of teaching and achieve mastery by an infinite amount of practice questions. So how did this benefit relate to my session in practice?

I could choose the time and place of my lecture(s) and was flexible how often I could rewind, forward, or simply stop a lecture. When one part of the lecture is too hard to handle in one setting, I can repeat it as often as I need to without embarrassment and judgment of others. Also I can go back to other modules to relearn what I may have forgotten,i. e., how to add exponents or simplify fractions.
The blackboard presentation of the concept with the adequate use of different colors is similar to what a classroom experience may offer.

Not all instructors – especially in math – can explain complicated matters in a simple way. In other words, knowing is different from being able to explain to others. Here the step-by-step approach of Khan is really coming to play. He offers little hints to algebra to remind students of previously learned skills. Hereby, I can concentrate on the new skill versus trying to remember the old assumed skills.

The non-edited recording of the videos enhances the human aspect to reinforce that everybody will make mistakes. The lessons are just right in length. Little packages of new concepts are easier to digest and make me acknowledge my progress. Since the pace is controlled solely by students, math anxiety issues are significantly reduced. By offering as many exercises to me as I personally need I can master the skill and know I in fact did for sure master it. This is a confidence booster and encourages further learning.


The prime requirement to benefit from all the advantages listed above is 24/7 access to a tablet, laptop or traditional computer and the internet. Although I generally have this access, many still do not. Students can have technical problems with their access tools and the internet streaming is not without speed and capacity issues.

When watching four videos on my chosen math subject I observed that the difficulty level was significantly lower than what I was required to reproduce in my face-to-face setting. The Khan lectures must choose a happy medium. The forwarding and rewinding or revisiting of lectures is in the hands of the students. If only the Khan videos are used in a blended classroom and not the self-paced practice questions, obviously mastery of a subject is questionable.

Although I can repeat, stop, and move forward without limits, my questions I may have, cannot be answered immediately as in a classroom. While I may look at other modules in the Khan website to relearn what I am missing to understand the new concept, it seems far more efficient to simply ask my instructor for explanation and seek advice as to which subjects to revisit.

The encouragement a teacher can offer, can not be replicated in the online setting. Many times I have seen class interrupted by questions to the professor who then adjusted his lesson on the spot to have more students understand the new concept. Qualified instructors will adapt to class reaction and offer many different ways of teaching the same idea to include all learning styles. The positive influence of teachers as far as igniting interest and giving encouragement and guidance is underestimated. Teachers can create new pathways for students only second to parental influence.

Not all students are able to self-adjust the pace of lessons. As some benefit from the flexible Khan model, others may simply not know how to bridge learning gaps from previous semesters. Therefore the Khan model is most beneficial in a blended setting, where pacing is not solely in the hands of the students. As it stands, most students have to figure out a way to learn, schedule and organize themselves before being left alone in the task of learning content prescribed by firm curricula.

Hardcopy vs. Digital Reading and Comprehension

By John Jones via dmlcentral, 5 November 2013

Although electronic texts have been with us for many decades, in the past few years electronic reading has become increasingly popular. The ready availability of mobile, connected devices like smartphones and tablets, along with dedicated e-readers like the Kindle and Nook, have moved electronic reading out from behind a desk into the environment. This change has brought increasing attention to the differences between reading in print and reading via digital devices.

In a recent article in Scientific American, Ferris Jabr argues that “paper still has advantages over screens as a reading medium,” claiming that “most studies published since the early 1990s” support this claim […]

If true, this claim deserves serious attention. Companies like Apple see the education market as an important growth area, and textbook publishers are pushing electronic books—either rentals or individual books that can’t be resold—as a way to eliminate the used textbook market and increase sales. When powerful companies such as these have a compelling business interest in promoting certain educational technologies, it is important for academics to question if these technologies serve students and learning, and, if reading on paper truly is superior to electronic reading, educators, parents, and students should be concerned about this trend.

Jabr’s article:  He argues that paper is superior to screens for four reasons:

Together laboratory experiments, polls and consumer reports indicate that digital devices prevent people from efficiently navigating long texts, which may subtly inhibit reading comprehension. Compared with paper, screens may also drain more of our mental resources while we are reading and make it a little harder to remember what we read when we are done. Whether they realize it or not, people often approach computers and tablets with a state of mind less conducive to learning than the one they bring to paper. And e-readers fail to re-create certain tactile experiences of reading on paper, the absence of which some find unsettling.

These four claims underlie the argument of the article: as the title puts it, “the brain prefers paper.” Indeed, these claims would suggest that any serious reading of any kind should make a point to avoid digital texts.

However, on closer reading there are a number of problems with the evidence Jabr uses to support his claims. Throughout the article, Jabr draws broad conclusions from very limited studies and he frequently fails to acknowledge that there are many different kinds of reading technologies for both paper and screens, and these technologies produce significantly different effects. In this post, I’m going to address his first claim: digital reading inhibits the navigation and comprehension of long texts.

Jabr argues that two features of “digital devices” make it difficult for readers to “navigate a text.” Where books have fixed pages and volumes—their width, height, and depth—electronic texts do not. This feature of a printed book, Jabr argues, allows readers to navigate them more efficiently. Readers tend to fashion what they read into “a kind of physical landscape,” a “mental representation” of the text that, when combined with the fixity of print books helps readers to find what they read. For instance, if I recall that a particular passage appeared in the upper half of the left page of a book and that it came before the book’s midpoint, and this can help me find this passage more effectively.

As anyone who has read a Kindle book or other electronic book format such as EPUB knows, these books do not have fixed pages: rather, they present texts as a continuous scroll, and on subsequent readings, the same portion of text may appear in a different position on the screen, depending on features such as how the reader navigated to the passage or the size of the text.

Of course, digital texts of all kinds represent the location of a reader in the text, generally as scroll bars, and they enable readers to find individual passages by searching for single words or phrases. Although Jabr acknowledges this, he argues that these scroll bars make it “difficult to see any one passage in the context of the entire text.” He expands on this point, arguing that in electronic reading – glancing at a progress bar – gives a far more vague sense of place than feeling the weight of read and unread pages. And although e-readers and tablets replicate pagination, the displayed pages are ephemeral. Once read, those pages vanish.

It is worth pointing out here that Jabr is not comparing “paper” to “screens,” the supposed point of the article. Rather, he is comparing two particular technologies: the bound book, what book scholars call a codex, with electronic texts like Kindle books or Web pages that scroll text. Here we have an example of Jabr attempting to compare a particular print technology—the book—with a particular form of digital text—scrolling text—then using these two to represent all forms of “paper” and “screen” reading.

Consider how Jabr’s arguments fail when addressing other forms of print technology. The ability to locate a selection of text spatially in a two-page layout is irrelevant for pages that aren’t bound. Further, the fixity of pages isn’t an exclusive property of print. One of the most popular formats of digital texts, Adobe’s PDF format, does fix the content of pages, affording readers the ability to find text just as they would in a book, by its location on a page. Finally, the need to find a selection of text via spatial memory seems unimportant when reading digital text, as one can simply search for the selection.

Jabr addresses this last point by suggesting that the issue at stake isn’t simply the retrieval of information, but comprehension. As you recall, he argues that “digital devices prevent people from efficiently navigating long texts” and this “may subtly inhibit reading comprehension.” However, the article only cites two studies in which participants showed worse comprehension after reading digital texts compared with those who read print texts, and it is difficult to see how either study supports Jabr’s conclusions. Neither study compares the reading a book to digital scrolling text (in fact, Jabr cites a third study, in which participants reading scrolling text performed equally well on comprehension as those in other groups), and, in each case, the participants read relatively short texts—not long texts—with the exact same spatial layout in print and on screen, a result that suggests the differences in tested comprehension have nothing to do with the fixity of the texts or their physical volume and says nothing about comprehension related to reading long texts, print or otherwise.

In one study of Norwegian 10th graders, participants were asked to answer a series of questions on a computer after reading either a PDF or a printout of a four page document. Both groups were able to consult the document while answering questions, but the students in the PDF group were unable to search their digital texts for answers, and when answering the questions had to switch between the PDF and quiz windows on their computer screens. Students with paper handouts, in contrast, were able to access their handouts while answering the quiz on the computer, glancing between the two. Here, the inability to search the text limited one of the main navigational features of digital texts, and the researchers themselves suggest that having to use the same screen to scan the text and answer questions may have impaired the PDF group.

In the second study, from 2005, university students were given comprehension tests after reading either a printed document or a PDF on a low-resolution (800×600) monitor. In this study, the PDF group scored lower on reading comprehension while also reporting greater stress and tiredness. As with the previous study, this study did not test for reading comprehension of spatially fixed paper texts versus scrolling digital text or for comprehension of long texts. It is likely, the low resolution monitors—which were likely unable to show an entire page of the reading at a time—may have interfered with the students comprehension. Indeed, participants in the paper group in that study reported greater difficulties in concentration and time pressure, while the PDF group reported problems with reading and following the text itself, which supports this conclusion…]


Also see:

Ferris Jabr’s article “The Reading Brain in the Digital Age: The Science of Paper versus Screens”, Scientific American, 11 April 2013:

Image Credit:

“Jing” It

Jing represents an easy and free tool to flip the classroom or simply present a project in a comprehensive and helpful way.

Capture an image of what you see on your computer screen with Jing. Simply select any window or region that you want to capture, mark up your screenshot with a text box, arrow, highlight or picture caption, and decide how you want to share it.

Send your screenshots and videos all over the web. As soon as you’re done with your screen capture or screen recording, it’s ready to upload to and share through IM, email, social media and more.

This link will give you an oversight about JING:

Techsmith offers a variaty of tutorials to help get you started:

In combination with, instructors can create videocasts to introduce projects and  learning content before the face-to-face lesson. Here is an example: (Please excuse the poor audio.)

Be aware that the audio for your videocasts is limited to 5 minutes. If you want to upgrade, download snagit which is not free, but you can give it a try without fee for 30 days.

Imagery: A Key to Understanding Math

Pascal's Pensees

By Holley Korby, New York Times

How can teachers help students find the beauty in math? There may be roadblocks already set up in math education — students’ disposition toward math anxiety, and pressure to cover material quickly. Or maybe it has something to do with the curse of knowledge — the gap between what experts know and non-experts don’t.

It’s easy for math professors to see the beauty in math, said New York University neuroscientist Pascal Wallisch, because they already have an obvious connection with it. “They perhaps had the luck to enjoy a positive math experience in school,” he said. “Or, frankly, from a cognitive neuroscience perspective, there is little doubt in my mind that they have quite a bit of a different brain than the average person who is trying to unlock the wonders of math, or just learn some math in order to get by.”

Wallisch said that as both a brain scientist and someone for whom math did not come easily, the key to mathematical beauty (and understanding) is visuals. “As primates, we are mostly visual creatures. A good amount of the cortex in primates (upwards of 30%) is dedicated to visual processing in one way or the other. Put differently, things that look interesting or appealing are bound to attract curiosity,” he said. Looking at a picture (or a movie or video) is the same thing as looking at an equation. But while it represents the same information, one method is inherently more appealing to our brains than the other.

According to Wallisch, mathematical imagery is what students are missing, and what causes confusion. He used the example of reading the words “Statue of Liberty,” and how it evokes an immediate image in the mind. But if a person couldn’t read, or had never heard of the Statue of Liberty, they would visualize only letters and words, not Lady Liberty holding her torch — and the same goes for math novices. Since they have no experience, the mix of mathematical symbols on the page don’t mean much. “Mathematicians see equations by imagery built by long-term practice manipulating them,” he said. “The trick is to use software to visualize the equations so that those who don’t have the practice (or the unusual brain) can see the same.”

“Mathematics is a way to read the world of nature and technology around us. If a teacher can convey this, the entire world becomes an exciting textbook.”
Wallisch began creating moving mathematical images for himself using technical computing environment Matlab, and said that, although he uses it for high-level research computations, high school students can just as easily build visual mathematical models with some guidance. By creating images of equations and playing with the variables, Wallisch now sees what all the fuss is about. He wrote in a blog post: “Personally, I’m betting on aesthetics, with Kant: ‘Beautiful is that which is appealing without interest.’ As we can’t presume interest, aesthetics can serve as an important bridge(head).”

California Institute of the Arts math professor Michael S. Schneider agreed that imagery is the best way to show students the beauty of math. He has been helping students connect mathematics to visual imagery for nearly forty years, and wrote the book A Beginner’s Guide to Constructing the Universe: Mathematical Archetypes of Nature, Art and Science to show humans are surrounded with mathematical imagery — including right outside, in nature.

“As a youngster I wasn’t particularly outstanding in math,” he said. “But at 16 I became earnestly interested in the variety of shapes nature produced and wanted to understand why regular shapes keep recurring in nature. I remember pondering the same hexagonal shape found in the beehive, quartz crystal and metal hexnuts. I could understand how a crystal grew mechanically in this precise geometry by accumulating atoms, but how did bees know how to produce the pattern which holds more weight of honey than, say, a checkerboard pattern? I wasn’t comfortable with the ‘trial and error’ explanation, and even if it was in their DNA, how did that knowledge of superior design get there?” Then, he said, he wanted to know more about logarithmic spirals – “in the bathtub, swirling leaves, tornadoes, hurricanes, solar systems, galaxies.” Schneider said he had good math teachers, but these topics were never covered in school; books he looked up on the subjects covered them one at a time, but never altogether in the same place.

Schneider said he became obsessed with understanding the language and shapes of “nature’s geometric alphabet”: circles, spheres, triangles, squares, and more — the shapes that surround us every day if we simply take the time to notice them. “A circle represents the number one,” he said. “Most people can feel why a circle represents unity, its wholeness, completeness. A circle holds more inside it than any other shape having the same perimeter. So it’s practical to know that round pizzas hold more toppings than squares or rectangles having the same length of crust.”

Armed with this set of nature’s images and symbols, Schneider found that numbers and shapes have personalities, each playing different roles in the cosmos. “The universe becomes a book and then a great play with great actors in great parts telling great stories,” he said. “Mathematics is a way to read the world of nature and technology around us. If a teacher can convey this, the entire world becomes an exciting textbook.”

Schneider admits that today’s math teachers are strapped for time and resources to really explore the beautiful part of math, partly due to the way textbooks are constructed, and the pressure to cover material quickly. He believes that for students to see the beauty in math, teachers need more time and freedom. “I think that math education gets too abstract too quickly without first providing a sense-based foundation,” he said.

But appreciating the beauty in mathematics could start by just having students look around them. “The universe may be a mystery, but it’s not a secret,” he said.


Data-Self vs. Real-Self

By LYNDSAY GRANT October 25, 2013 – 11:20am

Understanding Education through Big Data

The seduction of ‘Big Data’ lies in its promise of greater knowledge. The large amounts of data created as a by-product of our digital interactions, and the increased computing capacity to analyse it offer the possibility of knowing more about ourselves and the world around us. It promises to make the world less mysterious and more predictable.

This is not the first time that new technologies of data have changed our view of the world. In the nineteenth century, statistical ‘objective knowledge’ supplanted the personal knowledge of upper-class educated gentlemen as the main way in which governments came to know about those they governed. In our own time we seem to be facing a new revolution in which the basis of how we come to ‘know’ something – our epistemological foundations – is becoming reliant on big data analysis. From the perspective of this new epistemological turn, our knowledge – from theperformance of healthcare staff to how we choose a romantic partner – rests on the extent to which it is known through big data analysis. But what does it mean for education if the way that we know about it is governed by big data? Here, I sketch out some of the questions raised by the turn to a ‘big data epistemology’ in education.

Learning Analytics

It is not new that educational institutions collect and analyse data for predicting and intervening in children’s educational performance. But this data is often limited and disconnected, kept in separate repositories, in different formats, or never formally recorded at all. What is new is digitising, meta-tagging and aggregating that data with many other data sets, making possible new connections, predictions and diagnoses. This is the field of ‘learning analytics’ – described as the collection, analysis and use of data patterns to optimize conditions for improving learning.

This is what is being attempted by services like inBloomKnewton, and other new start ups announced at SXSW in March 2013, indicating a potentially lucrative new market tapping into students’ data. These services draw together existing data from a wide range of sources, as well as data produced as a by-product through children’s use of technology. By including so much data about individual children, and comparing that to the data from hundreds of thousands of other children, these services can create a learning profile for each individual child, diagnosing their strengths, weaknesses and challenges. After diagnosing problems, it then prescribes solutions, in the form of more educational technology software from its partners. Bill Gates sees the use of data as the next technological revolution in education, and the Gates Foundation, Carnegie Foundation and others have provided $100 million of support for inBloom. While the partners of these services do not necessarily have direct access to student data (unless the school district or state already has a relationship with them), they benefit by being able to target specific new software directly to individual students and teachers and by having better access to aggregated student data to drive future product development.

Learners and their Profiles

This application of learning analytics reframes teachers’ knowledge of an individual child away from an interpersonal relationship that recognises the uniqueness and difference of the other person, towards a knowledge determined by analysis of a child’s data trails. Big data analysis might then come to be the way that we ‘know’ a child educationally – how they learn, where their strengths and weaknesses lie, what kinds of teaching they might respond to, in short, who they are.

Because most teachers do not have the time, resources, skills, or access to large aggregated data sets needed to undertake such complex analysis, organisations like inBloom and Knewton provide neatly packaged results direct to teachers – taking the process by which judgements are made out of teachers’ control. Teachers and schools become ‘end users’ of data, positioned as unable to engage with, question or unpick the algorithmic processes by which diagnoses and prescriptions are made.

How does this emphasis on data-driven knowledge shape what it is possible to know about learners? Big data sets means that an averaged ‘norm’ can be identified for certain characteristics (age, location, socio-economic status, previous educational performance, etc.), creating an idealised ‘other’ to which an individual is compared. Attention is thereby focused on the gap between a child’s observed data patterns and where the data says they ‘could’ or ‘should’ be. Efforts are consequently focused on closing the gap, and individualised ‘catch-up’ work becomes the norm, while other possible responses – such as looking at how the classroom or curriculum could be organised differently, starting from the learner’s strengths and interests, or understanding the underlying reasons behind a learner’s development – are made less visible.

Know Thyself

Learning analytics also potentially changes how learners come to think of themselves. As our digital interactions are tracked, tagged, organised and presented back to ourselves, Rob Horning argues that it is becoming impossible to separate our own subjective sense of who we are from our ‘data-self’. Our digitised data, and how it is represented back to us becomes “a new dimension of what makes our experiences ‘real’.” So it may be that children’s sense of themselves as learners comes to be more dominated by visualisations of their educational data through apps, web profiles and infographics than through processes of reflection and dialogue. The ancient maxim to “know thyself” becomes instead: “measure thyself.” If the reliability of our knowledge rests on the extent that it can be backed up by big data, our learning profiles may be seen – both by others and ourselves – as more robust and objective descriptions of who we ‘really’ are, supplanting and dismissing our own messy, subjective self-knowledge.

But the question of our ‘real’ identity is a slippery topic. Many researchers now see identity not as a pre-existing fact to be discovered, but as something that we continually make and re-make using a range of resources – including our relationships with other people and technologies. While none of us wakes up in the morning with a total personality transplant, we do make choices about what kind of person we want to be and how we want to present ourselves to others. We make choices about which aspects of ourselves to share and which to keep private. Any description of our learning identity is therefore necessarily always partial – it cannot encompass the totality of who we are because who we are is in flux and depends on the context we are in. It is not just that we do not have enough information – a problem that could be solved by big data – but that no amount of information can pin down our inherently fluid learning identities.

Who Decides Our Learner Identities?

If it is not possible to be completely sure that a learning profile created from data is true or fixed, the important question is who gets to decide what the data means? When a learner’s identity is something they define in their relationships with teachers and peers they have an element of choice in determining what kind of learner they are, and what kind of learner they might want to become. They can provide the context that makes sense of their data. They can challenge or resist others’ interpretations of their actions and motives. In short, they have some control and voice over who they want to be as a learner.

If we do not like our data-driven profiles, we can try to adapt our behaviour to produce more favourable data. Just like Facebook or Twitter, these systems encourage users to continually produce more data. This data is where the real value lies for the providers, allowing more precise targeting, advertising and development of educational software. The considerable commercial value of these kinds of data is evidenced by the World Economic Forum’s decision in 2011 to designate “biological, ambient, environmental data gathered about the person” as a new economic asset class that will open up […] new possibilities for targeted delivery of services and goods.”

But not everyone wants, or is able, to adapt themselves to fit into the particular educational values and assumptions of what it means to be a successful learner built into learning analytic algorithms. For every individual able to adapt and fit in, there are those who can only adapt so far, because to do otherwise would be to deny alternative cultural educational values, aims and notions of success. A refusal to play the game comes at the expense of becoming invisible to powerful networks and the absence of a learning profile backed up by big data may seem suspicious in itself.

The use of big data analytics in education is not necessarily useless or insidious. For one thing, it can provide a useful additional perspective, ‘from the outside in’ about learners’ development. But we need to consider the implications and consequences of using big data analytics as our main way of knowing about education. It tends to simplify big social and political questions about what kinds of learners we are and want to be, or how education should respond to major social and economic challenges, to a simple process of prescribing the next piece of educational software to download. These big questions do not have simple, single answers. Different traditions, different approaches and different people will come up with different answers. Rather than locking ourselves into one perspective, we need to be open to multiple ways of understanding education and learners, opening up the possibility of a range of different responses. Crucially, learners, at the heart of the process, should be part of the debate about their own learning and education.

Banner image credit: infocux Technologies




At Summit Everest in Redwood City blended learning is used to close the gap between different levels of learners. The school’s population reflects its district, which covers the whole economic spectrum: 40% free and reduced lunch and a mixed ethnic background population.

Kyle Moyer teaches AP Calculus to seniors, but some of his students aren’t ready for the course. The school’s mission is to graduate high school students who are both college ready and equipped to succeed in college once they get there. But many come into his math class at a huge skill deficit, so he uses the software to identify the holes and gaps and help fill them.

But teaching some kids basic math while others learn advanced calculus in the same room is a tough juggling act. Adjusting to the tech tools for grading and data analysis hasn’t been easy either. Moyer says he’ll start class by discussing a broad concept that all learners can understand, then he’ll break the class into groups with some kids working to catch up on the computer and others learning calculus. He then moves around and provides individual help.

Moyer says, the technology has its place and it has certainly helped many struggling students catch up. But it has limitations. “A computer as of yet cannot help a student develop that deep mathematical reasoning and connecting concepts in various areas,” Moyer said. That’s his job, but he hopes that the software will give struggling learners a more robust mathematical “arsenal.”