Learning by doing helps students perform better in science

Jann Ingmire, News Officer for Social Sciences and the School of Social Service Administration News Office, University Communications reports, "Students who physically experience scientific concepts understand them more deeply and score better on science tests, according to a new UChicago-led study."

Illustration by Prof. Sian Beilock et al.

Brain scans showed that students who took a hands-on approach to learning had activation in sensory and motor-related parts of the brain when they later thought about concepts such as angular momentum and torque. Activation of these brain areas was associated with better quiz performance by college physics students who participated in the research.

The study, published online April 24 in Psychological Science, comes from the Department of Psychology’s Human Performance Lab, directed by Prof. Sian Beilock, an internationally known expert on the mind–body connection and author of the book How the Body Knows Its Mind.

Using Bodies and Brains to Learn Science 
 

Beilock and her co-authors, Prof. Susan Fischer at DePaul University, UChicago graduate student Carly Kontra and postdoctoral scholar Dan Lyons, explain that hands-on experiences may benefit students more than previously realized, particularly in the world of virtual laboratories and online learning, This may be especially true for the initial stages of learning and in areas of science education that lend themselves to physical experiences.

“This gives new meaning to the idea of learning,” said Beilock. “When we’re thinking about math or physics, getting students to actually physically experience some of the concepts they’re learning about changes how they process the information, which could lead to better performance on a test.”
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Source: UChicago News and The University of Chicago Channel (YouTube)

Correlates between the Science of Learning and the Practice of Teaching by Saad Nagi, PhD

Follow on Twitter as @saadsnagi

Saad Nagi, PhD, Research Fellow in Neuroscience at the University of Western Sydney writes, "The phenomenon of human learning is not a unitary construct, rather it comprises a gamut of cognitive traits including memory, attention, decision making and social functioning. According to David Ausubel, an eminent educational psychologist: “The single most important factor influencing learning is what the learner already knows. Ascertain this and teach him accordingly”."

What we already know and can retrieve is underpinned by the neural system of memory, and the use of pre-existing neural networks can form the basis of further learning. Retrospective evaluations of events in the “long term” (in behavioural neuroscience, this refers to a period longer than a day) have found memory processing to be a high fidelity system. However, the transient storage of information, i.e., working memory, appears to be less resilient and prone to rapid deterioration.
“Consolidation” is the term attributed to the hypothetical transformation of a memory trace from an unstable, short-term form to a stable, long-term form. Recent research has focused on a rather enigmatic aspect of memory processing, dubbed “reconsolidation” – it is a special state brought about by the retrieval of items in the long-term memory, which makes them prone to alterations.

Learning is not simply based on enhanced neural activity, but also structural modifications that can be determined by changes in synaptic density (synaptogenesis). In a study involving rats, this question was explored by training a test group for a low-intensity but challenging motor skill, compared to the other groups that were exposed to high-intensity physical exercise but with relatively little learning involved. Notably, a higher density of blood vessels was found in the latter (angiogenesis, a compensatory response to increased/repetitive synaptic activity), but a significant increase in synaptic density was only found in the former, thereby demonstrating that learning is underpinned by neuro-structural changes in the brain.
The cortical-hippocampal system – comprising complex, bi-directional flow of information between the neocortex, the parahippocampal region and the hippocampus – underpins the neural coding mechanisms of conscious memory. The latter is particularly important in the organisation of memories in space and time.

Consistent with the recent advances in the neurobiology of learning, a list of potential correlates will be discussed from the literature that either inform the basis of accepted teaching practices or provide ideas for further exploration with the aim of improving the current design of learning environments. It must be acknowledged, however, that cognitive neuroscience has not advanced to a point yet where it could translate into guidelines for effective teaching practices (nor would such a circumscribed approach necessarily provide the desired outcomes), but drawing parallels between the two fields illustrate the neural underpinnings of the pedagogy of education, and highlight some of the pervasive ‘neuromyths’ that have taken root in the education sector and the society at large by courtesy of the so-called ‘brain-based learning’ industry.
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Source: Brain Blogger (blog) 

Influence And Collaborative Networks - Classical Music Creation Secrets Uncovered By Big Data

"We know that everyone has influences and inspirations and collaborations, but they often go unmentioned. How do classical music composers collaborate with and influence one another?" according to Science 2.0.

Photo: Science 2.0

We have some answers. Big Data has been able to show how culture has evolved and influenced the recording market.

A new study was based on the largest classical music recordings database to date, using online retailer ArkivMusic and music reference site AllMusicGuide. The authors focused on analyzing networks of composers contemporary to CD publications, using modern data analysis and data modeling techniques. They first determined how fundamental properties of the network of Western classical music composers correlate with the artistic styles and active periods of their composer.

They found such network displays the small-world property and a modular structure.
Read more...

Additional resources
Topology and evolution of the network of western classical music composers.
D. Park, A. Bae, M. Schich and J. Park (2015), EPJ Data Science, 4:2, DOI: http://dx.doi.org/10.1140/epjds/s13688-015-0039-z

Source: Science 2.0

Microsoft sets up machine learning demo site that guesses your age

Follow on Twitter as @Joab_Jackson

Joab Jackson, U.S. Correspondent, IDG News Service reports, "Its purpose is to convince developers that machine learning algorithms are easy to use."

To demonstrate the new machine learning capabilities in its Azure cloud, Microsoft has set up a Web site that guesses the ages of people in uploaded photos.

how-old.net

The site, How Old Do I Look, is meant to show how easy it is for developers to use machine learning algorithms to make predictions and spot trends, said Joseph Sirosh, Microsoft corporate vice president in the company’s cloud and enterprise group.

The site, which he unveiled on Thursday at Microsoft’s Build 2015 developer conference in San Francisco, asks the user to upload a picture with someone’s face. After a minute or two, it will return a guess for how old the person in the photo is.

Thursday’s demo at Build was designed to familiarize developers with how machine learning could be used, and embedded into applications. Sirosh used a picture of a family whose members ranged in age from the teens to the early 70s. The service was able to predict their ages fairly closely, he said. The service can even guess the age of people depicted in paintings. It calculated the Mona Lisa’s age as 23, which was about the age of the model in the Leonardo da Vinci painting.
Read more... 

Source: ITworld

From Sci-fi to reality: Machine Learning by Joey Withers

Joey Withers, DBA & Senior Database Developer at Rockpool Digital, discusses the development and business applications of Machine Learning.

It almost seems like the beginning of a sci-fi movie when people talk about machine learning. It refers to systems that learn and adapt to data, rather than follow pre-programmed instructions.

While we won’t be facing Skynet’s Terminators anytime soon, companies are now starting to understand the advantages of intelligent systems.

Data has become one of the most coveted assets in the world of business. It can be used to provide a better product or service by helping companies to understand what customers want.

The systems that analyse this data are powered by complex algorithms. Historically, these algorithms were pretty set in stone and if a company wanted a different output from its analyses, the algorithm needed to be changed.

Now, the systems can adapt its algorithm depending on the data that it receives.

Take Facebook, for instance. Algorithms used to automatically tag people in a photograph have a higher success rate, as it ‘learns’ if it was correct based on interactions from its users. The more times a person uses the feature, the more opportunities there are for the algorithm to learn and the better it becomes.

Machine learning is improving, but due in part to the specialist skills required to utilise the technology, its wider usage is still limited to programmers. That could soon be changing.

Microsoft now has Machine Learning functionality on its Azure cloud computing platform. The platform allows users to build predictive models via a graphical drag and drop interface, without the need to write any code.

With easier access to this technology, businesses can identify its most influential clients, ensure that inventory is in the right place at the right time and predict results, therefore allowing the appropriate action to be taken.

Azure ML is designed specifically to make machine learning accessible to business users and more generalised developers.

Classically, in order to benefit from Machine Learning, you would require an in depth understanding of statistics and mathematics and be skilled in writing code in R, the functional programming language used among statisticians and data miners to utilise machine learning.
Read more...

Source: The Information Daily

This Way To The All-Star Degree? by Dayna Catropa and Margaret Andrews

"We originally thought that business schools might be the first to feel the heat of the changing market for higher education.  Turns out that may not be true – it might be the undergraduate market, quickly followed by the rest of higher education." according to Dayna Catropa and Margaret Andrews.

Arizona State University (ASU) and edX, the partnership between MIT and Harvard, recently announced an initiative that will allow people to do their entire first year of an undergraduate program online.  According to the announcement of an “alternative entry into higher education” on ASU’s website:

“The Global Freshman Academy will give learners anywhere in the world the opportunity to earn freshman-level university credit after successfully completing a series of digital immersion courses hosted on edX, designed and taught by leading scholars from ASU.

By allowing students to learn, explore and complete courses before applying or paying for credit, the Global Freshman Academy reimagines the freshman year and reduces academic and monetary stress while opening a new path to a college degree for many students.”

We’ve long been wondering when someone would devise the All-Star degree, whereby one institution (be it a school or another organization) would award/accept credit from a variety of education providers, be it MOOCs or other types of online, on-campus, or other forms of higher education, and bundle them together to award a degree.  We recently mused about this in the MBA market and something like this was predicted back in a 1981 article on how we might disaggregate the higher education market.  As mentioned in yesterday’s IHE article on the ASU/edX agreement, “The contract also creates an opportunity for ASU to award more credit than initially reported. The university will consider -- “subject to appropriate review and approval” -- awarding credit for MOOCs offered by other institutions, the contract states.”  This could be the All-Star degree we’ve been predicting. 

With the new ASU/edX agreement, we are getting much closer to a fundamental change in how higher education is created, consumed and degrees are awarded.  True, accreditors still need to weigh in on the new initiative, but still.
Read more...

Source: Inside Higher Ed (blog)

Upcoming Webinar: Making Complex Texts Compelling to Middle-School Students

Amplify’s All-Digital Core Curriculum for Middle School ELA 

Making Complex Texts Compelling to Middle-School Students
Tuesday, May 12, 2015, 2 to 3 p.m. ET

With all the changes happening in middle school students' lives, keeping them engaged can be challenging. How can you help them stay focused to develop the close-reading skills required by new, more rigorous standards? Amplify Learning's Chief Executive Officer Larry Berger and Chief English Language Arts Officer Deborah Reck will discuss strategies for making great literature compelling for middle school students. They'll share key insights on the best ways to help students make meaning of complex texts and get the motivation to dig more deeply, read more critically, and write more vividly about great literature and non-fiction. They will also address how to accomplish these same goals with English Language Learners, and how all of these strategies have been used to create Amplify's all-digital core curriculum for middle school ELA.
Register now for this free live webinar

Can't attend? All Education Week webinars are archived and accessible "on demand" for up to four months after the original live-streaming date. 

Source: Education Week

MathType: Equations in PowerPoint by Design Science

You've done your research and completed your paper.
Now it is time to present your work! 

You've finished your research and you need to present it
Perhaps you need to present your work in PowerPoint using computations and calculations you worked out in various applications. Or say you created a handout for your class using Word and you want to turn it into a PowerPoint slide.

Reuse existing equations -- save time and avoid mistakes
Instead of creating equations from scratch, you can use MathType to extract equations from hundreds of applications and websites such as Mathematica, Maple, Apple Pages 5, Microsoft Office, Wikipedia, and many more.
 
3 ways to create new equations
If you don't have existing equations you can reuse, MathType offers three approaches to creating equations in your presentations:

• Interactive: Save time by using point-and-click templates and symbols. MathType applies mathematical spacing rules automatically as you type.
• Keyboard shortcuts: Great for those who prefer using the keyboard to create equations. Insert a new equation, or edit an existing one, using just a keystroke.
• TeX/LaTeX input: If you already know the TeX typesetting language, MathType allows you to build equations by typing TeX into MathType's editing window.

MathType 6.9 is now available
MathType 6.9 for Windows works with Microsoft Office 2013 and is available in English, French, and German!

	Purchase MathType from our secure online store.
	Read about all of the new MathType features.
	Try the full version of MathType before you buy!

Source: Design Science

Will machine learning spell the end of programming?

IDG Connect writes, "This is a contributed piece by Guillaume Bouchard, Senior Scientist at the Xerox Research Centre Europe."

Photo: IDG Connect

Could you spend your working day teaching a machine to do its job better?

In a future of increased automation, with petabytes of data to be translated into meaningful information, the roles of human and machine will become more distinct in the workplace. A new role for humans is unveiling itself: improving machine learning systems.

With the assistance of humans, machine learning is likely to spell the end of computer programming as we know it. Today computer programmes are used to automate actions, but to change programme behaviour, you need a software engineer. With machine learning, the programme behaviour improves by itself when told by a human what’s right and what’s wrong. 

Machines That Can Listen and Learn with Guillaume Bouchard 
 

Learning through the data surge
From the days of Turing, scientists have explored the notion that computers should be able to improve themselves over time. This was the birth of machine learning, which introduced us to the idea of artificial intelligence. Its popularity has since fluctuated but over the last few years we’ve seen the field once again become the scientific topic du jour.

The study of neural networks, from being an almost forgotten art, is now being revisited by the likes of Andrew Ng at Google, who recently developed large scale artificial neural networks using Google's distributed computer infrastructure. These networks were ‘trained’ to recognise higher level concepts such as cats, simply through ‘watching’ YouTube videos. In addition, the study of ‘deep learning’ algorithms, such as those that helped Facebook do tasks such as automatically tagging uploaded pictures for example, is back underway in some of the world’s leading laboratories.

One advance fuelling this surge is the growth of big data. As the reams of mostly disorganised information businesses deal with increases exponentially, humans recognise the potential for feeding this to machines that can then make sense of and act upon this data at speeds we can only dream of.

This will help businesses stay ahead of changing times and customer needs. The potential to increase efficiency and improve customer experience is overwhelming, and suggests the rise of machine learning is only just beginning.
Read more... 

Source: IDG Connect and XeroxGlobal Channel (YouTube)

My life as a tech teacher, part 9: Planning for phase 2

Freelance technology journalist Alex Cruickshank continues from here. "It's the Easter school break, but there's no rest for the wicked. I've been thinking what to teach the children next term."

Photo: FreeDigitalPhotos.net

It's not that we're reaching the limits of Scratch. Far from it. It's quite possible to create complex computer games using Scratch, with multiple levels of interaction and various sprites, backgrounds, sounds and visual effects. It's not actually coding, though. Not really.

Scratch is great for building logical thought processes and for mental 'chunking' - breaking down larger goals into smaller steps. For that reason alone I'm reluctant to leave it behind. But coding to me – and to pretty much every programmer around the world – means typing in code. It means writing programmatic structures, defining and calling subroutines, testing, refining, fixing and testing again. It's about words.

I'm a reasonably good programmer, but not in modern programming languages. If I were to write my programming CV it would include references to building a powerful multi-user content management system in Perl in the early 2000s, to modifying and compiling Linux kernel modules and programs in C, loads of HTML/CSS/JS and... some BASIC. These days I mostly dabble in *nix shell scripts, though they can reach a few thousand lines and are riddled with reg-exps. A useful skill set for me, probably less so for the children I'm teaching.
Read more... 

Source: IDG Connect