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Top 10 Reasons to Use Technology in Education: iPad, Tablet, Computer, Listening Centers

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What Teachers Should Know
By Pedro De Bruyckere, Paul A. Kirschner, Casper D. Hulshof

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One of the most frequently cited reasons for justifying the need for change in education, or at least for labeling education as old-fashioned, is the enormous technological (r)evolution our world has undergone in recent years. Nowadays, we have the Internet in our pocket, in the form of a smartphone, which has exponentially more computing power than the Apollo Guidance Computer that put the first men on the moon! A school with desks, blackboards or whiteboards, and-perish the thought-books seems like some kind of archaic institution, one that, even if it does use a smartboard or a learning platform, operates in a manner that bears a suspiciously strong resemblance to the way things were done in the past.

In education, we often have the feeling that we are finding it harder and harder to reach our students. That is why we are so feverishly interested in smartboards or learning platforms or anything new on the market that might help. Every new tool seems like a possible solution, although sometimes we really don't know what the problem is or even if there is one.

Regrettably, we have become saddled with a multiplicity of tools, methods, approaches, theories, and pseudotheories, many of which have been shown by science to be wrong or, at best, only partially effective. In this article, which is drawn from our book Urban Myths about Learning and Education, we discuss these miracle tools and the idea that young people today are somehow "digital natives," and we examine the fear that technology is making our society and our students less intelligent. To illustrate that many claims about technology in education are in fact spurious, we will focus in this article on five specific myths and present the research findings that dispel them.

Myth 1: New technology is causing a revolution in education.

School television, computers, smartboards, and tablets such as the iPad-it was thought that all these new tools would, or will, change education beyond recognition. But if you look at the research of someone like Larry Cuban, it seems that classroom practice has remained remarkably stable during recent years. ¹ Even Microsoft cofounder Bill Gates-whom you would hardly suspect of being against technology in education-summarized his view on the matter as follows: "Just giving people devices has a really horrible track record."

The correct use of tools and resources nevertheless does have the potential to change education. Very often these change phenomena are general rather than specific. For example, the influence of the printed word is gigantic, but this influence-like so many other tools and resources-is anchored in society as a whole. You need to come down to the level of something like the book or the blackboard if you want to consider a resource that has specifically changed education.

In 1983, Richard Clark published a definitive study on how it was pedagogy (i.e., teaching practice) and not the medium (i.e., technological tools and resources, such as whiteboards, hand-held devices, blogs, chat boards) that made a difference in learning, stating that instructional media are "mere vehicles that deliver instruction but do not influence student achievement any more than the truck that delivers our groceries causes changes in our nutrition." ²

In 1994, Clark went as far as to make a daring prediction: namely, that a single medium would never influence education. He based this position on his opinion that, at that time, there was no proof to show that a medium was capable of ensuring that pupils and students could learn more or more effectively. He saw the medium as a means, a vehicle for instruction, but that the essence of learning remained-thankfully-in the hands of the teacher. ³

We are now 20 years further down the line, and the question needs to be asked: Does Clark's position still hold true? During those 20 years, we have seen the explosion of almost unimaginable technological possibilities. Even so, Clark and Richard Mayer continue to assert that nothing has fundamentally changed. ⁴ They argue that 60 years of comparative studies about teaching methods and teaching resources all confirm that it is not the medium that decides how effectively learners learn.

Clark and David Feldon confirm that the effectiveness of learning is determined primarily by the way the medium is used and by the quality of the instruction accompanying that use. ⁵ When media (or multimedia) are used for instruction, the choice of medium does not influence learning. John Hattie described, for example, how instructional methods that are more effective within conventional environments, such as learner control and explanative feedback, are also more effective within computer-based environments. ⁶

This can be called the "method-not-media" hypothesis, as tested in a study where students received an online multimedia lesson on how a solar cell works that consisted of 11 narrated slides with a script of 800 words. ⁷ Focusing on the instructional media being used, students received the lesson on an iMac in a lab or on an iPad in a courtyard. But they also used different instructional methods.

Students received either a continuous lesson with no headings (this was the standard method) or a segmented lesson in which the learner clicked on a button to go to the next slide, with each slide having a heading corresponding to the key idea in the script for the slide (this was the enhanced method). By combining changes in both medium and method, we can see what matters most. Across both media, the enhanced group outperformed the standard group on a transfer test where students had to use the information in settings other than those in the text, yielding a method effect on learning outcomes for both desktop and mobile medium.

Across both methods, looking at the medium, the mobile group produced stronger ratings than the desktop group on self-reported willingness to continue learning, yielding a media effect on motivational ratings for both standard and enhanced methods. Effective instructional methods can improve learning outcomes across different media, whereas using hand-held instructional media may increase students' willingness to continue to engage in learning.

If we look at the influence of technology on the effectiveness of instruction, the picture is not fully clear. This can partly be explained by the fact that relatively little research has been carried out that involves the comparison of two similar groups, one group learning with and the other group learning without the benefits of a new technology.

The different metastudies on this subject, analyzed by Hattie, reveal a considerable variation in results. ⁸ A review study on the implementation of technology, more specifically Web 2.0 tools such as wikis, blogs, and virtual worlds, in K-12 and higher education, suggests that actual evidence regarding the impact of those technologies on student learning is fairly weak. ⁹ There are still a number of studies that point to a positive gain in learning terms, ¹⁰ but the majority equate the positive learning effect resulting from the good use of technology with good teaching. The crucial factor for learning improvement is to make sure that you do not replace the teacher as the instrument of instruction, allowing computers to do what teachers would normally do, but instead use computers to supplement and amplify what the teacher does.

A 2009 metastudy about e-learning did, however, tentatively conclude that the use of both e-learning and contact education-which is known as blended learning-produces better results than lessons given without technology. ¹¹ This is also the case when you use computer game-based learning; the role of instruction still needs to have a real significant learning effect, reflecting the conclusion of one meta-analysis. ¹² Such instructional support may appear in several forms, such as providing feedback, scaffolding, and giving advice.

Still, there remain some questionable claims that technology can change, by itself, the present system of education. Clark and Feldon summarize the various claims and responses: ¹³

The claim: Multimedia instruction accommodates different learning styles and so maximizes learning for more students. Clark and Feldon describe how learning styles have not proven to be "robust foundations on which to customize instruction." And, as we explained in our book, the idea of learning styles* in themselves is already a very stubborn and harmful urban myth in education.

The claim: Multimedia instruction facilitates student-managed constructivist and discovery approaches that are beneficial to learning. In fact, Clark and Feldon found that "Discovery-based multimedia programs seem to benefit experts or students with higher levels of prior knowledge about the topic being learned. Students with novice to intermediate levels of prior knowledge learn best from fully guided instruction." This is another example of how the medium does not influence the learning. Prior knowledge is an individual difference that leads to learning benefits from more guidance at low to moderate levels but not at higher levels, regardless of the media used to deliver instruction.

The claim: Multimedia instruction provides students with autonomy and control over the sequencing of instruction. Although technology can deliver this, the more important question is whether this is a good thing. Letting students decide the pace of learning (e.g., by allowing them to pause or slow down videos or presentations) is beneficial to learning. But only a small group of students has the benefit of being given the chance to select the order of lessons, learning tasks, and learning support. For the majority of students, this has a mostly negative influence on learning.¹⁴

The point that teachers should remember is this: the medium seldom influences teaching, learning, and education, nor is it likely that one single medium will ever be the best one for all situations.

Myth 2: The Internet belongs in the classroom because it is part of the personal world experienced by children.

How often have you heard this? It sounds so logical, doesn't it? At the same time, many teachers have discovered, at their expense, that using information and communications technology in their lesson "randomly," in an unstructured way, does not always have lasting success. The problem is that most research studies have been evaluations of relatively short-term projects. Some research, for instance, focuses on the extent to which participants liked the medium being used during the actual test, which for a student actually lasted for about 12 minutes. ¹⁵

Also note that in this research, being motivated because of the medium did not help learning as much as the chosen pedagogical approach. But when we discuss implementing technology and the Internet in the classroom, people argue not for using it once or only for a short period, but for long-term implementation. Therefore, it is the impact over a longer period that really needs to be determined.

A study by the Canadian Higher Education Strategy Associates described how students had a preference for "ordinary, real life" lessons rather than e-learning or the use of some other technology. ¹⁶ It was a result that surprised the researchers. "It is not the portrait we expected, whereby students would embrace anything that happens on a more highly technological level. On the contrary-they really seem to like access to human interaction, a smart person at the front of the classroom."

The findings also revealed that the more technology was used to teach a particular course, the fewer the students who felt they were able to get something out of that course. While the 1,380 students from 60 Canadian universities questioned for this survey were generally satisfied with the courses they took, the level of satisfaction fell significantly when more digital forums, online interactions, or other technological elements were involved. Yet, at the same time, more than half the respondents said that they would skip a lesson if there was more information or a comparable video lesson online.

Although these results at first glance seem to be fairly negative for e-learning, the responses to some additional questions were more positive. The majority of students (59.6 percent) said that they would like more electronic content in their courses. When asked what they would specifically like to see online, 53.6 percent answered that they would like more online course notes, with 46.4 percent advocating more recordings of lessons on the web.

These findings are broadly in keeping with the results of a 2011 literature study that investigated the expectations of young people with regard to new forms of education and information and communications technology. ¹⁷

The study reached the following conclusions: First, the technological gap between the students and their teachers is not enormous, and certainly not so large that it cannot be bridged. In fact, the relationship is determined by the requirements teachers place on their students to make use of new technologies. There is little evidence that students expect the use of these new technologies. Second, in all the studies consulted, the students persistently report that they prefer moderate use of information and communications technology in their courses. ("Moderate" is, of course, an imprecise term that is difficult to quantify.) Third, students do not naturally make extensive use of many of the newest technologies, such as blogs, wikis, and virtual worlds. Students who need or are required to use these technologies in their courses are unlikely to object to them, but there is not a natural demand among students for any such use.

Maybe this will change as technology becomes more and more ingrained. However, a study of students in Glasgow, Scotland, found little change; these students appeared to conform to fairly traditional pedagogies, albeit with minor uses of technology tools that deliver content. Research comparing traditional books with e-readers shows that students prefer paper. ¹⁸

The sad thing is that even if students did prefer to use technology in school, this would not mean that they would learn more. In 2005, Clark and Feldon wrote, "The best conclusion at this point is that, overall, multimedia courses may be more attractive to students and so they tend to choose them when offered options, but student interest does not result in more learning and overall it appears to actually result in significantly less learning than would have occurred in 'instructor led' courses." ¹⁹ A decade later, based on 10 years of additional research, Clark and Feldon stand by this conclusion. ²⁰

In her book, danah boyd describes the main reasons young people use technology. These reasons are mainly social, such as sharing information with each other, and meeting each other online and in real life. They do discuss schoolwork with each other, but this is very different from using Facebook as a learning tool or their phone as a learning machine. ²¹

Myth 3: Today's "digital natives" are a new generation who want a new style of education.

Digital natives! Whenever the question of digital innovation in education is discussed, this is a term that immediately comes to the surface. But it should be avoided. Even the person who coined the term digital natives, Marc Prensky, admitted in his most recent book, Brain Gain, that the term is now obsolete. ²²

The concept is usually used to describe young people who were born in the digital world and for whom all forms of information and communications technology are natural. The adults who were born earlier are therefore "digital immigrants," who try with difficulty to keep up with the natives. Prensky first coined both terms in 2001. ²³

With this concept, he referred to a group of young people who have been immersed in technology all their lives, giving them distinct and unique characteristics that set them apart from previous generations, and who have sophisticated technical skills and learning preferences for which traditional education is unprepared. However, Prensky's coining of this term-and its counterpart for people who are not digitally native-was not based on research into this generation, but rather created by rationalizing phenomena that he had observed. ²⁴

As the digital native concept became popular, extra claims were added to the initial concept. Erika Smith, of the University of Alberta, describes eight unsubstantiated claims in the different present discourses on digital natives: ²⁵

They possess new ways of knowing and being.

They are driving a digital revolution and thereby transforming society.

They are innately or inherently tech savvy.

They are multitaskers, team oriented, and collaborative.

They are native speakers of the language of technologies and have unique viewpoints and abilities.

They embrace gaming, interaction, and simulation.

They demand immediate gratification.

They reflect and respond to the knowledge economy.

Smith is not alone in concluding that there is little to no proof for these claims. A meta-analysis conducted in 2008 had already shown that there was little hard evidence to support the use of the term digital natives. ²⁶

But maybe the concept of digital natives was more a kind of prediction, and we just had to wait. Perhaps today's young people are true digital natives. If we look at the research performed in high-tech Hong Kong by David M. Kennedy and Bob Fox, the answer is more nuanced. ²⁷ Kennedy and Fox investigated how first-year undergraduate students used and understood various digital technologies. They discovered, like danah boyd did with the American teenagers, that the first-year undergraduate students at Hong Kong University do use a wide range of digital technologies.

The students use a large quantity and variety of technologies for communicating, learning, staying connected with their friends, and engaging with the world around them. But they are using them primarily for "personal empowerment and entertainment." More importantly, Kennedy and Fox describe that the students are "not always digitally literate in using technology to support their learning. This is particularly evident when it comes to student use of technology as consumers of content rather than creators of content specifically for academic purposes."

Other researchers have reported that university students use only a limited range of technologies for learning and socialization. For example, one study found that "the tools these students used were largely established technologies, in particular mobile phones, media player, Google, [and] Wikipedia. The use of handheld computers as well as gaming, social networking sites, blogs, and other emergent social technologies was very low." ²⁸ This finding has been supported by a number of other researchers who came to similar conclusions, ²⁹ namely that university students do not really have a deep knowledge of technology, and what knowledge they do have is often limited to basic Microsoft Office skills (Word, Excel, PowerPoint), emailing, text messaging, Facebook, and surfing the Internet.

When looking at the same topic in another continent, Europe, the large-scale EU Kids Online report of 2011 placed the term digital natives in first place on its list of the 10 biggest myths about young people and technology. Just 36 percent of Europe's 9- to 16-year-olds said that they knew more about the Internet than their parents. ³⁰

Studies in other countries, including Australia, Austria, Canada, Switzerland, and the United States, all come to the same conclusion: there is no such thing as a generation of digital natives. ³¹

Myth 4: The Internet makes us dumber.

In recent years, a number of authors-often neurologists-such as Baroness Susan Greenfield and Manfred Spitzer, in his 2012 book Digitale Demenz ("Digital Dementia"), have appeared from a new group of technological critics who seem to agree that we are all becoming more stupid because of the technology we are using. ³² Though what they posit in their books-very strong, sometimes not completely well-founded positions ³³ -need to be taken with a grain of salt, they refer to the plasticity of the brain in arguing that the Internet is rewiring our brains in a harmful way.

It is certainly true that what's known as the Flynn effect (the observed rise in IQ scores over time) has come to a halt in some countries, but the reasons for this halt are neither uniform nor clear. James Flynn, who named this effect, shared his doubts in his 2012 book  Are We Getting Smarter?   ³⁴ about whether the effect actually measures that we really have become smarter. There are other plausible reasons for the rise in the test scores, such as education more closely mimicking IQ tests. Research even suggests that the better scores on IQ tests result from increased luckier guessing on harder test items. ³⁵
As a result, it is not easy to say whether the Internet might be partly responsible for the halt in the phenomenon, as we do not know for certain what actually caused the Flynn effect. ³⁶ Some authors even see the use of new media as an important contributory factor in the rise of average IQ that has been evident in recent years. ³⁷

Nowadays, we are relying more and more on technology. As an illustration of this fact, Betsy Sparrow, a professor at Columbia University in New York, has described the "Google effect." ³⁸ Together with her team, she discovered that students remember information more easily if they think that this information is not likely to be available on the Internet. Her study also revealed that students are better at remembering where to find something on the Internet than they are at remembering the information itself. In this respect, the popular Google search engine is increasingly acting as a kind of "external memory."

But is this really evidence to show that the Internet is making us dumber? To be honest, we don't know. At the moment, there is no conclusive, empirical proof that decides the issue one way or the other. Although Nicolas Carr has provided many indications in his book The Shallows, his arguments are personal and anecdotal, rather than scientific. ³⁹ Perhaps Steven Pinker is right when he says that we are now making better use of our brains by using Google for "unnecessary information," ⁴⁰ just as we now use satellite navigation or another global positioning device instead of a map. And in the final analysis, we certainly know more now than we did in the past. So why should we be more stupid?

In an opinion piece from 2010, in reaction to the publication of Carr's book, two leading neurologists explain why the digital alarmists are wrong: ⁴¹

The basic plan of the brain's "wiring" is determined by genetic programs and biochemical interactions that do most of their work long before a child discovers Facebook and Twitter. There is simply no experimental evidence to show that living with new technologies fundamentally changes brain organization in a way that affects one's ability to focus. Of course, the brain changes any time we form a memory or learn a new skill, but new skills build on our existing capacities without fundamentally changing them. We will no more lose our ability to pay attention than we will lose our ability to listen, see or speak.

Still, there are reasons to consider being careful with the total amount of screen time that children may have in a normal day. The American Academy of Pediatrics (AAP) warns that studies have shown excessive media use can lead to attention problems, school difficulties, sleep and eating disorders, and obesity. ⁴² This view has been confirmed by a study by researchers from Iowa State University. ⁴³ Therefore, the AAP recommends no more than one to two hours of screen time a day for children two years and older. John Hattie also describes a clear negative impact of excessive television consumption on learning. Finally, a recent review article in The Neuroscientist paints a disturbing picture of what is happening to this group: ⁴⁴

Growing up with Internet technologies, "Digital Natives" gravitate toward "shallow" information processing behaviors characterized by rapid attention shifting and reduced deliberations. They engage in increased multitasking behaviors that are linked to increased distractibility and poor executive control abilities. Digital natives also exhibit higher prevalence of Internet-related addictive behaviors that reflect altered reward-processing and self-control mechanisms. Recent neuroimaging investigations have suggested associations between these Internet-related cognitive impacts and structural changes in the brain.

Note that many of these studies examined the influence of television rather than the influence of interactive technology, such as smartphones and social media. Also note that most of these studies found a correlation rather than a causal relation; that is, there may be other reasons why children who watch a lot of television have poorer school results.

Myth 5: Young people don't read anymore.

Of course young people read. They read a lot. As Amelia Hall Sorrell and Peggy F. Hopper explain, teenagers constantly read what is available to them through the different forms of technology that continue to evolve.45 But when people think that young people today read less, it's not about reading online content or text messages, it's about reading books.

In 2010, Reader's Digest in the United Kingdom conducted a survey on the reading habits of some 2,000 adults and 700 children. ⁴⁶ The results revealed that one in five children hardly ever reads a book, one in three never reads a book, and one in 20 has never read a book. These figures support a perception that many people seem to have; namely, that young people and children don't read anymore, and certainly not for pleasure. But is a survey in a popular monthly magazine a reliable source for such a sweeping claim?

Perhaps more scientifically gathered data could tell us more. A 2007 report, To Read or Not to Read, describes a significant decline in reading by youngsters in the United States in the previous 20 years. ⁴⁷ The study compared data from 1982 and 2002, and found that less than one-third of the 13-year-olds were daily readers. The percentage of 17-year-olds who read nothing at all for pleasure doubled over the same 20-year period. Yet the amount they read for school or homework stayed the same. However, these data are already quite old and stem from the beginnings of the digital era.

The Programme for International Student Assessment (PISA) study carried out by the Organisation for Economic Co-operation and Development (OECD) looks not only at learning results but also at the learning behavior of the respondents. In 2011, PISA published a report analyzing the pleasure reading of young people. ⁴⁸ This study found that, on average, two out of three students read every day for pleasure. It also noted that the percentage of students who reported that they read for enjoyment daily dropped in the majority of OECD countries between 2000 and 2009, but in some countries that proportion increased. In the United States, the average remained the same. Boys and girls from families with a higher socioeconomic status read more than young people from families with a lower socioeconomic status; moreover, the gap between the two has increased between 2000 and 2009.

In 2012, Stage of Life polled teenagers about their reading habits and found that 77.7 percent of them read at least one extra book per month for personal pleasure beyond what is required for school. Nearly a quarter (24.5 percent) read five or more books per month outside of school. These figures are much higher than the PISA figures, but this probably is due to the way the teenagers were selected. ⁴⁹

In the United States, the Pew Research Center examined the reading habits of the American audience in 2012, youth included. ⁵⁰ Book readers under the age of 30 consumed an average of 13 books in the previous 12 months and a median of six books; in other words, half of book readers in that age cohort had read fewer than six, and half had read more than six.

Still, even in these digital times, libraries remain important to many American youngsters.** Pew found that in the 12 months before the survey in 2013, 53 percent of Americans aged 16 and older had visited a library or bookmobile, 25 percent had visited a library website, and 13 percent had used a handheld device such as a smartphone or tablet computer to access a library website.

To sum all this up, young people are still doing a lot of reading, and these statistics make clear that many of them are reading for pleasure. However, we need to be careful about making too many sweeping assertions, since the reading figures in many countries are falling. Even so, we know that reading continues to be important: both reading by young people themselves and parents reading to their children.

Though there is good empirical proof out there refuting these myths, they persist. Why? Anthropologists tell us that myths function in culture and society to express, enhance, and codify belief, while language historians51 attribute their persistence to increased, almost unlimited, information availability. Our society serves up so much instant and pervasive information, which we fail to examine discerningly, that we end up circulating and strengthening myths through repetition and enhancement.

This vicious cycle is compounded by what journalist Farhad Manjoo discusses in True Enough: Learning to Live in a Post-Fact Society. ⁵² Self-styled experts (educational charlatans) publish anything they want and come at us from all directions, in every medium, without any "check" on their expertise. The "real danger of living in the age of Photoshop isn't the proliferation of fake photos," Manjoo writes. "Rather, it's that true photos will be ignored as phonies."

In education, how do we combat this? In our view, there is only one answer: the educational sciences must be driven by theories and theory development, and not by simple observations and conclusions. Strong empirical data must come from experiments set up according to good research methodologies (i.e., randomized control trials, real control conditions, samples large and representative enough to justify implementation decisions, etc.) rather than legends and hype. Only after these evidence-informed methods are slowly but surely tested in real-life settings can we think about large-scale implementation.

Finally, teachers, administrators, and politicians must learn to become knowledgeable and aware consumers. To that end, we suggest keeping in mind the following: if something sounds too good to be true, it probably isn't true.

Pedro De Bruyckere is a researcher at Arteveldehogeschool University College in Ghent, Belgium. Paul A. Kirschner is a professor of educational psychology and Distinguished University Professor at the Welten Institute at the Open University of the Netherlands; he is also a visiting professor of education at the University of Oulu, Finland. Casper D. Hulshof is a researcher at the University of Utrecht, the Netherlands. This article is adapted from their book Urban Myths about Learning and Education (Academic Press, 2015). Reprinted with permission from Elsevier Inc. Copyright ©2015.

*For more about the research behind learning styles, see " Do Visual, Auditory, and Kinesthetic Learners Need Visual, Auditory, and Kinesthetic Instruction?" in the Summer 2005 issue of American Educator. ( back to the article)

†For more about fully guided instruction, see "Putting Students on the Path to Learning" and "Principles of Instruction" in the Spring 2012 issue of American Educator. ( back to the article)

‡For more about multitasking, see " Have Technology and Multitasking Rewired How Students Learn?" in the Summer 2010 issue of American Educator. ( back to the article)

§For more about how to engage young people in reading for pleasure, see " For the Love of Reading" in the Spring 2015 issue of American Educator. ( back to the article)

**For more about the importance of school librarians to learning, see " Beyond the Stacks" in the Winter 2014-2015 issue of American Educator. ( back to the article)

Endnotes

1. Larry Cuban, Oversold and Underused: Computers in the Classroom (Cambridge, MA: Harvard University Press, 2001).

2. Richard E. Clark, "Reconsidering Research on Learning from Media," Review of Educational Research 53 (1983): 445-459.

3. Richard E. Clark, "Media Will Never Influence Learning," Educational Technology Research and Development 42, no. 2 (1994): 21-29.

4. Ruth C. Clark and Richard E. Mayer, E-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning, 3rd ed. (San Francisco: Pfeiffer, 2011).

5. Richard E. Clark and David F. Feldon, "Ten Common but Questionable Principles of Multimedia Learning," in The Cambridge Handbook of Multimedia Learning, 2nd ed., ed. Richard E. Mayer (Cambridge: Cambridge University Press, 2014), 151-173.

6. John Hattie, Visible Learning: A Synthesis of Over 800 Meta-Analyses Relating to Achievement (London: Routledge, 2009).

7. Eunmo Sung and Richard E. Mayer, "Online Multimedia Learning with Mobile Devices and Desktop Computers: An Experimental Test of Clark's Methods-Not-Media Hypothesis," Computers in Human Behavior 29, no. 3 (2013): 639-647.

8. Hattie, Visible Learning.

9. Khe Foon Hew and Wing Sum Cheung, "Use of Web 2.0 Technologies in K-12 and Higher Education: The Search for Evidence-Based Practice," Educational Research Review 9 (2013): 47-64.

10. For example, Katja Rüther, "ICT en leerprestaties: Een systematische review naar de invloed van ICT op leerprestaties in exacte vakken in het voortgezet onderwijs" (BSc thesis, Universiteit Twente, 2012) describes positive effects on mathematics and sciences.

11. Barbara Means, Yukie Toyama, Robert Murphy, Marianne Bakia, and Karla Jones, Evaluation of Evidence-Based Practices in Online Learning: A Meta-Analysis and Review of Online Learning Studies (Washington, DC: U.S. Department of Education, 2009).

12. Pieter Wouters and Herre van Oostendorp, "A Meta-Analytic Review of the Role of Instructional Support in Game-Based Learning," Computers & Education 60 (2013): 412-425.

13. Clark and Feldon, "Ten Common but Questionable Principles."

14. See also, Paul A. Kirschner, "Is What Learners Say That They Prefer Good for Them?," Blogcollectief Onderzoek Onderwijs (blog), July 19, 2015, www.onderzoekonderwijs.net/2015/07/19/is-what-learners-say-that-they-pr...- for-them.

15. Sung and Mayer, "Online Multimedia Learning."

16. Edyta Kaznowska, Jason Rogers, and Alex Usher, The State of E-Learning in Canadian Universities, 2011: If Students Are Digital Natives, Why Don't They Like E-Learning? (Toronto: Higher Education Strategy Associates, 2011).

17. Christopher Jones and Binhui Shao, The Net Generation and Digital Natives: Implications for Higher Education (York, UK: Higher Education Academy, 2011).

18. William Douglas Woody, David B. Daniel, and Crystal A. Baker, "E-books or Textbooks: Students Prefer Textbooks," Computers & Education 55 (2010): 945-948.

19. Richard E. Clark and David F. Feldon, "Five Common but Questionable Principles of Multimedia Learning," in The Cambridge Handbook of Multimedia Learning, ed. Richard E. Mayer (Cambridge: Cambridge University Press, 2005), 97-117.

20. Clark and Feldon, "Ten Common but Questionable Principles."

21. danah boyd, It's Complicated: The Social Lives of Networked Teens (New Haven, CT: Yale University Press, 2014).

22. Marc Prensky, Brain Gain: Technology and the Quest for Digital Wisdom (New York: Palgrave Macmillan, 2012).

23. Marc Prensky, "Digital Natives, Digital Immigrants," On the Horizon 9, no. 5 (2001): 1-6.

24. Paul A. Kirschner and Jeroen J. G. van Merriënboer, "Do Learners Really Know Best? Urban Legends in Education," Educational Psychologist 48 (2013): 169-183.

25. Erika E. Smith, "The Digital Native Debate in Higher Education: A Comparative Analysis of Recent Literature," Canadian Journal of Learning and Technology 38, no. 3 (2012).

26. Sue Bennett, Karl Maton, and Lisa Kervin, "The 'Digital Natives' Debate: A Critical Review of the Evidence," British Journal of Educational Technology 39 (2008): 775-786.

27. David M. Kennedy and Bob Fox, "'Digital Natives': An Asian Perspective for Using Learning Technologies," International Journal of Education and Development Using Information and Communication Technology 9, no. 1 (2013): 64-79.

28. Anoush Margaryan, Allison Littlejohn, and Gabrielle Vojt, "Are Digital Natives a Myth or Reality? University Students' Use of Digital Technologies," Computers & Education 56 (2011): 429-440.

29. See, for example, Neil Selwyn, "The Digital Native-Myth and Reality," Aslib Proceedings 64 (2009): 364-379; and Peter Williams and Ian Rowlands, The Information Behaviour of the Researcher of the Future, Work Package II: The Literature on Young People and Their Information Behaviour (London: University College London, 2007).

30. Sonia Livingstone, Leslie Haddon, Anke Görzig, and Kjartan Ólafsson, EU Kids Online: Final Report (London: London School of Economics and Political Science, 2011).

31. Kirschner and van Merriënboer, "Do Learners Really Know Best?"

32. Susan Greenfield, Tomorrow's People: How 21st-Century Technology Is Changing the Way We Think and Feel (London: Allen Lane, 2003); and Manfred Spitzer, Digitale Demenz: Wie wir un und unsere Kinder um den Verstand bringen (Munich: Droemer, 2012).

33. Susan Greenfield went as far as stating that technology is one of the main reasons why there is higher prevalence of autism and ADHD today. See Frank Swain, "Susan Greenfield: Living Online Is Changing Our Brains," New Scientist, July 30, 2011. For reactions of other researchers, see Ben Goldacre, "Serious Claims Belong in a Serious Scientific Paper," Comment Is Free (blog), Guardian, October 21, 2011, www.theguardian.com/commentisfree/2011/oct/21/bad-science-publishing-cl...; and Deevy Bishop, "An Open Letter to Baroness Susan Greenfield," BishopBlog (blog), August 4, 2011, www.deevybee.blogspot.be/2011/ 08/open-letter-to-baroness-susan.html.

34. James R. Flynn, Are We Getting Smarter? Rising IQ in the Twenty-First Century (Cambridge: Cambridge University Press, 2012).

35. Michael Anthony Woodley, Jan te Nijenhuis, Olev Must, and Aasa Must, "Controlling for Increased Guessing Enhances the Independence of the Flynn Effect from g: The Return of the Brand Effect," Intelligence 43 (2014): 27-34.

36. Paul A. Kirschner, "The Disturbing Facts about Digital Natives," Blogcollectief Onderzoek Onderwijs (blog), July 30, 2015, www.onderzoekonderwijs.net/2015/07/30/the-disturbing-facts-about-digita....

37. Steven Johnson, Everything Bad Is Good for You: How Today's Popular Culture Is Actually Making Us Smarter (New York: Riverhead Books, 2005).

38. Betsy Sparrow, Jenny Liu, and Daniel M. Wegner, "Google Effects on Memory: Cognitive Consequences of Having Information at Our Fingertips," Science 333, no. 6043 (August 5, 2011): 776-778.

39. Nicholas Carr, The Shallows: What the Internet Is Doing to Our Brains (New York: W. W. Norton, 2010).

40. Steven Pinker, "Mind Over Mass Media," New York Times, June 10, 2010.

41. Christopher Chabris and Daniel Simons, "Digital Alarmists Are Wrong," Los Angeles Times, July 25, 2010.

42. "Media and Children," American Academy of Pediatrics, accessed April 10, 2014, www.aap.org/en-us/advocacy-and-policy/aap-health-initiatives/pages/medi....

43. Douglas A. Gentile, Rachel A. Reimer, Amy I. Nathanson, David A. Walsh, and Joey C. Eisenmann, "Protective Effects of Parental Monitoring of Children's Media Use: A Prospective Study," JAMA Pediatrics 168, no. 5 (2014): 479-484.

44. Kep Kee Loh and Ryota Kanai, "How Has the Internet Reshaped Human Cognition?," Neuroscientist (forthcoming), published electronically July 13, 2015, doi:10.1177/1073858415595005.

45. Amelia Hall Sorrell and Peggy F. Hopper, "Are They Reading or Not?," National Forum of Teacher Education Journal 22, no. 3 (2012).

46. "Literacy Concerns as a Fifth of Youngsters Admit Hardly Ever Reading Books," Telegraph, November 6, 2010.

47. National Endowment for the Arts, To Read or Not to Read: A Question of National Consequence (Washington, DC: National Endowment for the Arts, 2007).

48. Organisation for Economic Co-operation and Development, "Do Students Today Read for Pleasure?," PISA In Focus, no. 8 (Paris: OECD, 2011).

49. "Teen Trend Summary Report-Books & Reading," Stage of Life, accessed August 20, 2015, www.stageoflife.com/TeensandBooks.aspx.

50. Kathryn Zickhur, Lee Rainie, and Kristen Purcell, Younger Americans' Library Habits and Expectations (Washington, DC: Pew Research Center, 2011).

51. See, for example, Keith M. Botelho, Renaissance Earwitnesses: Rumor and Early Modern Masculinity (New York: Palgrave Macmillan, 2009).

52. Farhad Manjoo, True Enough: Learning to Live in a Post-Fact Society (Hoboken, NJ: Wiley, 2008).

[illustrations by Viktor Koen]

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One of the most frequently cited reasons for justifying the need for change in education, or at least for labeling education as old-fashioned, is the enormous technological (r)evolution our world has undergone in recent years.

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Neuroscientist News. (2016 April). Neuroscientists working to test brain training claims.

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The draw is huge: Play video games and get smarter. For the past decade, various groups have claimed that their cognitive training programs do everything from staving off neurodegenerative disease to enhancing education and improving daily functioning. Absent from many of these claims has been neuroscientific evidence. Cognitive neuroscientists are now rigorously testing the potential benefits of such "brain training" tools.

New work, being showcased today (April 5) at the Cognitive Neuroscience Society's (CNS) annual conference in New York, on working memory tasks in children and on pairing noninvasive brain stimulation with cognitive training in adults is showing promising results. Researchers are identifying who may benefit from cognitive training and the new methods most likely to result in long-lasting, positive effects on cognition.
See Also: Systematic review shows 'smart drug' modafinil does enhance cognition
"We hope that by better understanding how and why cognitive abilities are altered by training, we can better harness its wider benefits," says Duncan Astle of the Medical Research Council in the UK, who is chairing the symposium on brain training. While more testing is required to apply these techniques to real-world training interventions, the studies are providing a baseline of neuroscientific evidence for developing tools that yield wider benefits.

Tasks for the developing brain

Working memory sits at the heart of many of the brain training studies. The ability to hold information in mind for brief periods of time is central to our daily lives. And, Astle says, "we know that differences in working memory during childhood are incredibly strong predictors of educational progress." As a cognitive neuroscientist long interested in how the brain develops working memory capabilities in childhood, Astle has set out with colleagues to test if it is possible to train a child's memory.

In work recently published in the Journal of Neuroscience and also new, yet-unpublished work that Astle will present at the CNS conference, his team investigated tasks in 8- to 11-year-olds to boost working memory. They found that the training tasks yielded improvements in working memory capacity that were also reflected in measurements taken from magnetoencephalography (MEG, which uses magnetic fields to image the brain), showing increased strength of neural connectivity when the brain was at rest.

The children performed 20 training sessions, each around 30 minutes and with 8 games, from their home computers. The games required children to remember spatial or verbal information for brief periods of time and to use this information in an ongoing task. For example, one game involved remembering the locations and order of asteroids that flashed up in sequence as they swirled across the screen. At the end of each trial, children had to click on the asteroids in order. In the experimental group, the games became more difficult as the children got better; "the children were always being worked at the limits of their current capabilities," Astle says. In the control group, the difficulty of the games remained the same.

The MEG data showed significant changes to connectivity between frontoparietal networks and the lateral occipital complex and inferior temporal cortex in those in the experimental group. "We think that the training enhances an attentional process that children are able to use strategically on similarly structured but untrained tasks," Astle says. "But it is important to note that we have not demonstrated the wider benefits of this training."

The 8 to 11 age range is "very good because the children are able to handle pretty complex tasks, and yet are still far from adult levels of performance - i.e. there is still plenty of development left to happen," Astle says. "We think that this is a really important age range in which to understand working memory and training effects. However, there is a great need to understand these processes better across the lifespan, so we are always exploring the literature more widely to see how our findings fit in with other groups that study other age ranges."

Stimulation for working memory

Among the tools scientists are using for trying to boost cognition is tDCS (transcranial direct-current stimulation) - a non-invasive brain stimulation technique that involves passing a very weak direct current through the brain. "While it is not immediately clear how the current affects neural activity, the prevailing opinion is that it makes neurons either more susceptible to firing, or less susceptible, depending on which electrode is placed where," says John Jonides of the University of Michigan.
Related: Neurotransmitter GABA predicts learning
In new work Jonides in presenting at the CNS conference, he and colleagues have found that tDCS has a robust effect on working memory, with enhancements lasting over a course of months. "Previous research has been equivocal about whether tDCS enhances training, and there have been no long-term investigations of how long that training effect lasts," Jonides says.

In the new study, 62 participants randomly received tDCS stimulation to either the right or left prefrontal cortex or received sham stimulation while performing a visuospatial working memory task. After 7 training sessions, those who received the tDCS stimulation had increased working memory capabilities, even several months after completing their training. They also found that those who receive stimulation on the right prefrontal cortex had selective ability to transfer the working memory to non-trained tasks.

"The long-lasting effect of the training was completely unexpected," Jonides says. "We investigated this largely on a lark, not expecting to find much, but the fact that the training effect lasts as long as months is both surprising and very provocative because it opens up the use of tDCS for long-term learning enhancement."

Jonides says that his study is just one data point in understanding these techniques, noting that it is still the early days of studying brain stimulation. Replication and generalization to other training and transfer tasks are necessary to continue testing the long-term impacts and best targets for stimulation.

"We need high-level, rigorous validation that focuses on understanding the mechanism of action, transfer of benefits, and sustainability of the effects in diverse populations," says Adam Gazzaley of the University of California, San Francisco, who will be presenting work to develop and validate "closed-loop" video games as cognitive enhancement tools. The closed loop approach enables scientists to intervene, record the impact of the intervention, and then reuse that data to iterate and optimize the process cyclically.
Learn More: Specific gene linked to adult growth of brain cells, learning and memory
His team is using tDCS and tACS (with alternative current) to boost plasticity in the underlying brain cortex. "The goal is to accelerate the learning process that occurs during game play, especially for those individuals with damage," Gazzaley says.

"There is great promise and reason for excitement in this approach, but we are still in our infancy and have much to learn both on the development and validation side," Gazzaley says.

"Behind strong claims we need strong scientific evidence," Astle says. "Sadly the hype surrounding the field has lost touch with its scientific foundations. As a result, it is tempting to ditch the whole endeavor. On the contrary, I think that this needs to spur scientists to invest in high-quality training studies."

Note: Material may have been edited for length and content. For further information, please contact the cited source.

Cognitive Neuroscience Society Annual Meeting April 2-5, 2016
Symposium Seesion 8: Taking stock of cognitive training: theory, neural mechanisms and application

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Neuroscientists working to test brain training claims

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The draw is huge: Play video games and get smarter. For the past decade, various groups have claimed that their cognitive training programs do everything from staving off neurodegenerative disease to enhancing education and improving daily functioning. Absent from many of these claims has been neuroscientific evidence.

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Lodge, J. M., & Horvath, J. C. (2016). Science of learning and digital learning environments. (Links to an external site.)  From the Laboratory to the Classroom: Translating Science of Learning for Teachers, Ch.

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Over recent years the field of Science of Learning has increased dramatically. Unfortunately, despite claims that this work will greatly impact education, very little research makes it into teacher practice. Although the reasons for this are varied, a primary concern is the lack of a proper translation framework.

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Howard-Jones, P. (2012). Neuroscience, games & learning. [video]. (29:26 minutes)

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Dr Paul Howard-Jones on Minds, Brains and Learning Games at #LEGup

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Talks from #LEGup 28th November 2013 on the science of learning games http://www.meetup.com/The-London-Educational-Games-Meetup-Group/events/149267532/ Paul Howard-Jones (Graduate School of Education, University of Bristol) researches at the interface between neuroscience and educational theory, practice and policy, and he publishes in all associated areas.

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What kind of learning are we talking about?

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What kind of learning are we talking about?

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Devonshire, I. M., Davis, J., Fairweather, S., Highfield, L., Thaker, C., Walsh, A., ... & Hathway, G. J. (2014). Risk-based learning games improve long-term retention of information among school pupilsPloS One, 9(7), e103640.

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Ian M. Devonshire,

Jenny Davis,

Sophie Fairweather,

Lauren Highfield,

Chandni Thaker,

Ashleigh Walsh,

Rachel Wilson,

Gareth J. Hathway

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Abstract

Risk heightens motivation and, if used appropriately, may have the potential to improve engagement in the classroom. We have developed a risk-based learning game for school pupils in order to test whether such learning games can improve later recall of information. The study was performed during a series of public engagement workshops delivered by undergraduate students. Undergraduate neuroscience students delivered 90-minute science workshops to 9-10 year old school pupils (n = 448) that were divided into 'Risk', 'No risk' and 'Control' classes. 'Risk' classes received periodic multiple-choice questions (MCQs) during the workshops which required small teams of pupils to assign tokens to the answer(s) they believed to be correct. Tokens assigned to the correct answer were returned to the group and an equal number given back as a prize; tokens assigned to incorrect answers were lost. Participation was incentivised by the promise of a brain-related prize to the team with the most tokens at the end of the workshop. 'No risk' classes received MCQs without the risk component whilst the 'Control' classes received no MCQs. When presented with a neuroscience quiz based on workshop content at the end of the workshop, pupils in the 'Risk' classes exhibited significantly greater recall of information one week later. Quiz scores were higher than scores from the day of the workshop which suggested pupils may have discussed the workshop content outside of the classroom, thereby increasing knowledge over and above what was learned during the workshop. This is supported by feedback from pupils in 'Risk' classes which indicated that 'Risk' workshops were more interesting than 'No risk' and 'Control' workshops. These data suggest that there is a role for risk in the classroom but further investigations are required to elucidate the causal mechanisms of improved retention of information.

Citation: Devonshire IM, Davis J, Fairweather S, Highfield L, Thaker C, Walsh A, et al. (2014) Risk-Based Learning Games Improve Long-Term Retention of Information among School Pupils. PLoS ONE 9(7): e103640. https://doi.org/10.1371/journal.pone.0103640

Editor: Rochelle E. Tractenberg, Georgetown University Medical Center, United States of America

Received: November 27, 2013; Accepted: July 5, 2014; Published: July 29, 2014

Copyright: © 2014 Devonshire et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This project was funded by the School of Life Sciences teaching fund and a University of Nottingham 'Cascade Award'. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

High levels of engagement and attention facilitate learning in the classroom and, in an effort to improve academic performance, educators are constantly developing new approaches and strategies to better engage young learners. One such approach is the use of learning games in the hope that the enjoyment and intrinsic motivation so often associated with entertainment games will carry over into their educational equivalent [1]. Another major benefit of learning games is that they encourage participation by reducing the negative effects of failure on self-esteem that other teaching styles, such as direct questioning, can cause. The research field that has built up around this approach is known as game-based learning [2], [3], [4], [5]. Attempts at emulating the popularity of entertainment games in the classroom however, especially using personal or tablet computers, has had mixed success [1], [6] and the translation of skills acquired in a game to real world contexts is often poor [7], [8]. One possible reason for inconsistent results in this field is that the critical factor(s) that make entertainment games so compelling is hotly debated and/or when identified, is poorly integrated into the educational game [2]. The inclusion of multiple experimental conditions to enable comparison between different teaching strategies instead of purely pre/post testing schedules (i.e. between learning games and more traditional teaching styles) does not happen commonly enough in the game-based learning field and may help identify subtle benefits of educational games [8]. However, it has recently been proposed that the inclusion of an element of risk into computer or classroom-based learning games might be crucial in engaging learners and improving retention of information learned during the game [9].

Risk heightens motivation [10] and moderate risk could, therefore, be initially used to engage otherwise disinterested pupils. Risk-taking can be defined as "the participation in behaviour which involves potential negative consequences (or loss) balanced in some way by perceived positive consequences (or gain)" [11]. Adolescents are more tolerant of ambiguously risky situations than adults [12] and risk-taking behaviour peaks at an age when brain networks responsible for socio-emotional behaviour (e.g. amygdala, ventral striatum, superior temporal sulcus) develop more rapidly than those necessary for cognitive control (such as the lateral prefrontal and parietal cortex) [13], [14], allowing stimulus-driven behaviour to overrule goal-driven behaviour. Indeed, this difference between the developmental rate of brain regions is believed to allow organisms to better take advantage of learning opportunities in their environment [12]. Thus, childhood could be an opportune time to utilise the benefits of risk-based learning strategies. Eventually, as the cognitive control network catches up and integration between brain areas improves - especially in the frontal cortex [15], [16] - perceptions of reward and risk are sharpened, impulsive behaviour is inhibited and the child slowly learns to balance short-term gains with long-term consequences, a vital skill for adulthood [17], [18], [19], [20].

We developed a risk-based learning game for pupils aged 9-10 years old (UK Year 5) that was incorporated into school science workshops delivered by undergraduate (UG) neuroscience students; the project was referred to as BrainLab [21]. Pupils played the game in small, competing groups and, therefore, the game can be classified as a form of team-based learning [22], an approach primarily used to develop pupils' interpersonal skills and problem-solving abilities (though these were attributes not specifically assessed in the current study). In our learning game, teams were required to work together to find the correct answer to periodic multiple-choice questions (MCQs) which included an element of risk by requiring each team to bet tokens on different answers.

The manner in which tokens are used in our learning game can be interpreted as the level of confidence pupils have in different answers. There are a number of methodologies used within educational psychology that record learner confidence to evaluate different attributes of the learning process and, therefore, share similarities with our approach. These methodologies include calibration, judgements of learning (JOLs) and confidence-based marking (CBM). Calibration compares confidence with accuracy [23], [24], JOLs examine the learner's confidence that information will be remembered [25], whilst CBM is strictly a form of assessment in which the level of confidence in an answer results in a respective penalisation or boost to the final score according to whether the learner is right or wrong [26], [27]. Our learning game is most similar to CBM and could be viewed as a team-based, tokenised version of the traditional CBM paradigm. However, it differs fundamentally from CBM in that we are not using the game for assessment purposes. By playing the game at key points throughout the workshop, in-between specific workshop activities, with each game question related to the workshop activity that immediately preceded it, the main purpose of the risk-based game is to enhance concentration and engagement during the activities and, therefore, increase the overall information learned. In this respect, our approach also differs from the majority of learning games which incorporate the information to be learned into the game itself and also tend to be computer-based. Thus, although participants in our workshops very much perceived the series of risk-based MCQs as a game, our approach could be considered instead as a novel teaching paradigm that borrows ideas from assessment strategies and learning games. Nevertheless, the approach will continue to be referred to as a learning game; its merits will be evaluated primarily by examining pupils' short- and long-term retention of workshop content, as well as comparing these data to a range of self-reported feedback responses.

Methods

Participants, recruitment and ethical consent

Ethical clearance for the project was obtained from Nottingham University's Medical School Ethics Committee (O12072012-BMS). A total of 448 pupils (229 boys, 219 girls) divided between 18 classes from 13 schools were recruited for the project (9-10 years old; Year 5). Average class size was 24.9 pupils (SD = 4.6; range = 16 to 35) with an average male∶female ratio of 51∶49 (range = 38∶62 to 64∶36). Recruitment was performed by the University of Nottingham's Widening Participation (WP) team and IntoUniversity, a national charity that provides university experience and academic support for school pupils in disadvantaged areas. Both the WP team and IntoUniversity utilised existing school networks within the Nottingham area to fit in with the wider context of the University of Nottingham WP initiative to raise awareness of higher education among disadvantaged schools in the local area. During recruitment, head teachers were provided with an information sheet that described the project and its aims together with a list of possible workshop dates. If the school wished to participate, a registration form was completed which allowed the school to provide contact details for an appropriate point-of-contact (as this is not always the head teacher), available workshop dates and the opportunity to highlight any special requirements or make specific comments. Ethical consent forms were sent to all schools that had returned a registration form; an individual consent form was sent to each head teacher and all parents were sent an opt-out form along with an information sheet that described the project. Therefore, head teachers provided written informed consent for the project as a whole whilst parents/caregivers were given the option to opt out (note that no parents/caregivers chose to opt out of the current study). Each aspect of the consent procedure was approved by Nottingham University's Medical School Ethics Committee.

The workshops were delivered as part of an UG science communication course which has been described previously albeit briefly [21]; the course represents a new model of public engagement as it combines UG teaching, public engagement and research. To enable replication of the model, full methodological details such as how workshop ideas were developed are provided in the current article. Six final (3 rd) year undergraduate students were recruited to the project in an annual preview session in which each academic offering a project provides a brief description of their project to all final year undergraduate students. All academics offering projects receive teaching credit for supervision provided. Every student in their final year must choose either a research project (normally laboratory-based) or a library project. Following a preview session of all available projects students rank their top three choices. All students involved in our project ranked the BrainLab project in first place.

Experimental Design

The independent variable in the design was workshop delivery style, with three conditions that differed in the amount and style of information reinforcement: no reinforcement ('Control'), five equally-spaced multiple-choice questions (MCQs) answered in small teams that re-capped what had just been taught ('No risk') or the same MCQs in which teams could compete with one another by betting tokens on the correct answer ('Risk'). The inclusion of the 'No risk' condition controlled for the novelty of the neuroscience workshop. Teams betting tokens on the correct answer received double the number of tokens back; to provide incentive, it was announced that the team with the most tokens at the end of the game would win small brain-related prizes, though these were not revealed until the end of the workshop (pencils and brain-shaped erasers, donated by The Dana Foundation). School classes were divided such that there were six classes in each experimental condition; all classes were treated as independent. This enabled each of the six undergraduate students to deliver one workshop in each of the three conditions and the data pooled. The order that each student delivered a workshop from each condition was counterbalanced to avoid order effects (e.g. increasing confidence and proficiency of the UG in their delivery). The primary dependent variable was the score from a neuroscience quiz (out of eight) which was completed immediately after each workshop (day 0) and again one week later (day 7). Secondary dependent variables were obtained from the results of an evaluation completed along with the neuroscience quiz immediately after the workshop.

The risk-based learning game

Each class was divided into teams of 4-6 pupils that at five times during the workshop came together to answer an MCQ. One UG helper took charge of each team during the game. Each team were given 20 tokens which they could risk on any of the answers to the question, with the caveat that 10 tokens were always kept back until the last question (ensuring no team could lose all their tokens - and engagement - before the end of the game). For instance, the teams could risk the maximum number of tokens on a single question but lost them all if they got the question wrong, or else spread their tokens across a number of answers. The team received double the number of tokens that had been risked on the correct answer. All other tokens risked on incorrect answers were taken away. The question and five possible answers was read out by the lead UG before pupils made their choices. For example, one question in a workshop entitled 'Super Synapses' was 'What is a chemical message kept inside before it enters the synapse?', with possible answers A) Vehicle, B) Vertebra, C) Vesicle, or D) Veegram. Plastic pots were provided to represent each answer into which pupils could place their tokens. UG helpers kept track of scores and helped to make sure all members of the pupil team were involved in the decision process. In some instances, pupils decided to divide the tokens between each pupil and allow everyone to decide on their own answer. The correct answer was given once all teams had made their choices and a running total of the team scores announced before the next workshop activity began.

Assessment and evaluation

The primary dependent variable was the score from a pen-and-paper neuroscience quiz which was completed at the end of each workshop (day 0) and again one week later (day 7; extra quiz sheets and stamped, addressed envelopes were given to each school on the day of the workshop to allow these to be returned free of charge). Name and gender was indicated on the quiz sheet but data were anonymised during input. Ten minutes were allotted at the end of the workshop for completion of the quiz and the evaluation (printed on the reverse of the quiz sheet; see below). The quiz comprised eight MCQs that were devised jointly by the academics and students: four questions were unique to a given workshop and four were the same for all workshops. For instance, in a workshop entitled 'Inside the Brain of Usain', on the topic of exercise and the autonomic nervous system that used athletes such as Usain Bolt as the central theme, two questions were 'Nerves make muscles [blank]' (with four possible answers: Contract, Expand, Stretch or Lengthen); and 'What do adrenal glands make?' (Water, Adrenaline, Electricity or Urine). Questions that were used across all workshops included 'What is the name of the outside of the brain?' (Cortex, Thalamus, Hippocampus or Hypothalamus) and 'What do nerve cells make to send information?' (Steam power, Water, Electricity or Radio waves).

Secondary dependent variables were obtained from the results of an evaluation that was completed by the pupils immediately after the first neuroscience quiz (on day 0). The evaluation was presented on the reverse of the neuroscience quiz and, thus, were linked to the quiz scores. Questions in the evaluation were designed to find out if the material was engaging, appropriate to the pupils' age, whether they learned something new, if it was enjoyable and whether the pupils enjoyed the experience to the extent that they would like the group to deliver another similar workshop in the future. We also explored the elements of each workshop that pupils enjoyed most and least. The evaluation comprised five statements that pupils were required to indicate their level of agreement by way of a Likert scale (1 = disagree, 5 = agree) and two open questions in which answered could be written into text boxes. The five statements were: S1) The session was interesting, S2) The session was easy to understand, S3) I learned new information, S4) The session was fun, S5) I would like these people to come back again. The two open questions were 'What did you like most about the workshops?' and 'What did you like least?'. Given the nature of the assessment and evaluation, a very small number of returns were 'spoiled' for reasons such as no attempt being made to answer the questions or the questions were not completed in the allotted time. The number of quizzes or evaluations that were discounted for such reasons are given in the results section where necessary. Further, more constructive, evaluation of the workshops was provided by the completion of evaluation forms by classroom teachers in order to identify to the academics where specific improvements that could be made to the project as a whole to enhance the experience for both school pupils and UG students.

Data analysis and statistics

Both quiz score and evaluation feedback data were found to be non-normally distributed as determined by D'Agostino & Pearson omnibus normality tests and, therefore, non-parametric statistical tests were used throughout. Quiz score and feedback data were divided by gender and Kruskal-Wallis (non-parametric) one-way analysis of variance (ANOVA) tests performed with Dunn's multiple comparison tests performed where appropriate. To examine the effect of delivery style on score improvement, the difference between quiz scores collected on the day of the workshops and one week later were also analysed which, due to the necessity to match pupils, invariably contained fewer pupils than the original quiz. This reduced pupil number was due to, for example, missing names on quiz sheets or absences from school. All statistical analysis was performed in Prism 6 (Graphpad, San Diego, CA, USA); data are reported in means ±95% confidence intervals throughout.

Results

Longer-term retention of information is improved by using risk-based learning games

When follow-up quiz scores were collected one week after the neuroscience workshops, a significantly increased score was found for all pupils combined, and for boys specifically ( Figure 2B). Average scores for all pupils in the 'Control', 'No risk' and 'Risk' conditions were 5.80±0.33, 5.70±0.27 and 6.30±0.26, respectively (Kruskal-Wallis test statistic = 11.590 p = <0.01, total number of values = 405; Dunn's test mean rank difference between Risk and No risk conditions = 46.84, p<0.01; spoiled quiz sheets = 43). Average scores for boys in the 'Control', 'No risk' and 'Risk' conditions were 5.65±0.55, 5.62±0.36 and 6.38±0.36, respectively (Kruskal-Wallis test statistic = 9.475 p = <0.01, total number of values = 204; Dunn's test mean rank difference between Risk and No risk conditions = 29.56, p<0.01; spoiled quiz sheets = 25). Average scores for girls in the 'Control', 'No risk' and 'Risk' conditions were 5.93±0.40, 5.79±0.40 and 6.28±0.37, respectively (Kruskal-Wallis test statistic = 3.712, total number of values = 201; spoiled quiz sheets = 18).

To examine the change in individual pupils' quiz scores between the two test sessions, only pupils for which quiz scores could be identified at, and matched between, both time points were used. Classes at the very beginning of the project could not be matched due to names not being given on quiz sheets; further pupils had to be removed from analysis due to being absent at the second time point or failing to include their name. Quiz scores from a total of 291 pupils were successfully matched between the two time points (Control = 89, No risk = 89, Risk = 113); the difference in quiz scores between these times are shown in Figure 3. There was a significant effect of the 'Risk' condition over both 'Control' and 'No risk' conditions when all pupils were examined together (Kruskal-Wallis test statistic = 11.93, p<0.01, number of values = 291; mean rank difference between 'Control' vs. 'Risk' = 32.15, p<0.05; mean rank difference between 'No risk' vs. 'Risk' = 35.36, p<0.01). Compared to their performance on the day of the workshop, pupils in the 'Risk' condition scored 0.50±0.25 marks higher one week later; in the 'Control' and 'No risk' conditions, pupils scored −0.15±0.32 and −0.14±0.30 marks, respectively, compared to the first time they completed the quiz. For boys, scores in the 'Control', 'No risk' and 'Risk' conditions were −0.04±0.50, −0.13±0.41 and 0.55±0.38, respectively (Kruskal-Wallis test statistic = 6.62, p<0.05, number of values = 151; mean rank difference between 'No risk' vs. 'Risk' = 21.19, p<0.05). For girls, scores in the 'Control', 'No risk' and 'Risk' conditions were −0.26±0.40, −0.14±0.45 and 0.44±0.33, respectively (Kruskal-Wallis test statistic = 6.86, p<0.05, number of values = 140; mean rank difference between 'Control' vs. 'Risk' = 20.02, p<0.05).

Discussion

The current study utilised UG student-led science workshops in local schools to investigate the impact of risk-based learning games on retention of information. Workshop feedback showed that pupils found the 'Risk' workshops more interesting than 'No risk' or 'Control' workshops and the involvement of risk also resulted in significantly higher recall of workshop information by pupils one week later. This result will be discussed by describing what is known about the effects of risky environments on brain function and also consider how the use of external rewards to incentivise participation in the learning game may also influence academic performance.

The potential of risk-based learning games to improve learning has been repeatedly highlighted and studies have been performed on the effects of such games upon, for example, galvanic skin responses of participants, how fair the games are and levels of uncertainty that participants prefer [9], [28]. Puzzlingly, the question of whether or not risk-based learning games can actually improve learning was never examined and, to the best of our knowledge, has never before been examined in a controlled manner. Risky play during infancy is believed to offer an important mechanism through which children overcome fears [29] and may still offer much to older children and adults by increasing arousal levels, especially when choices are made about how much risk to endure in a game [30]. The impact of risk-based learning games on the retention of information in the current study was measured by scores on a neuroscience quiz taken at two time points: immediately following science workshops and one week later. Although there were no differences between quiz scores in the 'Risk', 'No risk' and 'Control' conditions at day 0 ( Figure 2A), scores from pupils in the 'Risk' group one week later were significantly higher ( Figure 2B). When the change in quiz scores between the two time points was examined on a pupil-by-pupil basis, the average change in quiz score from pupils in the risk condition were higher than both non-risk and control conditions ( Figure 3). In studies that assess retention of information over time, the actual measure in practice is the amount of information that has been forgotten. However, in the current study, scores in the 'Risk' condition one week after the workshop were higher than on the day of the workshop. In other words, knowledge increased in the week following the workshop. A clue as to why overall knowledge increased in one group comes from the pupil feedback data. More pupils in the risk workshops found their workshops interesting and believed they learnt new information ( Figure 4). Although the quiz scores showed objectively that they didn't learn more than other pupils, the fact that they found the risk-involving workshops more interesting may have resulted in discussion and the sharing of workshop material outside of the classroom, thereby increasing the overall scores one week later. Thus, an indirect effect of risk-based learning games may have predominated over more direct effects of the learning game on memory consolidation and later recall.

Involvement in the risk-based game in all 'Risk' workshops was incentivised by the promise of a 'brain-related prize' for the winning team, which was only revealed at the end of the workshop. The use of external rewards in education is controversial but common [31], [32] and it is not yet certain whether rewards can motivate pupils to work harder or undermine intrinsic motivation by encouraging pupils to focus too much on the reward itself [33], [34]. It is likely that outcomes are dependent on both the particular paradigm utilised and the circumstances in which rewards are offered [33] but individual differences in emotional reactions to a given reward are also a vitally important variable and can ultimately determine whether a piece of information is either remembered or forgotten [35]. The presence of reward in the current study may have contributed to the increase in quiz score one week after the workshop by reminding the class of the workshop and prompting discussion of what was learned; the use of rewards to incentivise participation in the risk-based learning game is therefore one limitation of the current study. This raises the question whether a non-tangible reward would have had the same effect; current studies in our laboratory are hoping to shed light onto this by examining learning reinforcement strategies such as how tangible rewards or praise differentially influence pupil engagement and learning. A further limitation of this study is the necessary use of pupils' self-report for measures such as levels of interest in, and their belief that they learned new information from, the workshops. Such measures were not the primary focus of the study however and used as supporting evidence to probe the differences between workshop style on the more reliable and quantitative assessment of pupil information retention.

The cognitive and emotional states of learners during learning has, unsurprisingly, been shown to influence later recall (e.g. [36]) and, in response to calls for greater evidence-based practices in education [37], [38], [39], [40], the fledgling field of educational neuroscience is attempting to understand the conditions that are optimal for learning to take place and to extricate the different factors that contribute to an effective learning environment [41], [42], [43], [44], [45]. Regardless of the mechanisms of action, the investigation into particular learning games that exploit one or more neural processes in the developing brain, or are effective simply because they introduce novelty to a classroom, may constitute a fruitful avenue of educational neuroscience research if explored using appropriately controlled experimental designs such as the one utilised here.

Conclusions

We have shown that the inclusion of risk-based learning games is an effective means by which pupils can be engaged and retention of information improved. Whilst a focus of future studies will be to elucidate the precise mechanisms by which the learning games promote retention, we have demonstrated a practical approach to evaluate topical questions in educational neuroscience and contribute to the growing evidence base in education. In so doing, we have also validated and fully described a new model that combines PE, undergraduate science communication training and classroom research.

Acknowledgments

Recruitment of schools was performed by Lisa Owen (Widening Participation Team, University of Nottingham) and Simon Collins (IntoUniversity). Prizes awarded to school pupils were kindly donated by The Dana Foundation.

Author Contributions

Conceived and designed the experiments: IMD GJH. Performed the experiments: IMD JD SF LH CT AW RW GJH. Analyzed the data: IMD JD SF LH CT AW RW GJH. Contributed reagents/materials/analysis tools: IMD GJH. Wrote the paper: IMD GJH.

References

1. 1. Pearson Education (2012) A literature review of gaming in education. Available: http://researchnetwork.pearson.com/wp-content/uploads/Lit_Review_of_Gaming_in_Education.pdf. Accessed 18th Jun 2014.

2. 6. Hays RT (2005) The effectiveness of instructional games: a literature review and discussion. Orlando, FL, USA: Naval Air Warfare Training Systems Division.

3. 17. Dennison C, Coleman JC (2000) Young people and gender: a review of research. London: HMSO.

4. 19. Galdman-Rakic P (1987) Circuitry of the prefrontal cortex and the regulation of representational knowledge. In: Plum F, Mountcastle V, editors. Handbook of Physiology. Bethesda, MD, USA: American Physiology Society.

5. 33. Cameron J, Pierce WD (2002) Rewards and intrinsic motivation: resolving the controversy. Westport, CT, USA: Bergin & Garvey.

6. 39. UK Government Cabinet Office (2012) Test, learn, adapt: developing public policy with randomised controlled trials. Available: https://www.gov.uk/government/publications/test-learn-adapt-developing-public-policy-with-randomised-controlled-trials. Accessed 18th Nov 2013.

7. 40. Alliance for Useful Evidence (2013) Evidence for the frontline. Available: http://www.alliance4usefulevidence.org/publication/evidence-for-the-frontline/. Accessed 20th November 2013.

8. 42. Education Endowment Foundation (2014) Neuroscience and education: a review of educational interventions and approaches informed by neuroscience. Available: http://educationendowmentfoundation.org.uk/uploads/pdf/NSED_LitReview_Final.pdf. Accessed 6 Apr 2014.

9. 43. Della Salla S, Anderson M, editors(2012) Neuroscience in education: The good, the bad and the ugly. Oxford: Oxford University Press.

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Risk-Based Learning Games Improve Long-Term Retention of Information among School Pupils

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Risk heightens motivation and, if used appropriately, may have the potential to improve engagement in the classroom. We have developed a risk-based learning game for school pupils in order to test whether such learning games can improve later recall of information. The study was performed during a series of public engagement workshops delivered by undergraduate students.

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Cohen, A. M. (2011). The gamification of education. (Links to an external site.) The Futurist, 45(5), 16.

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The Gamification of Education - ProQuest

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This is a short preview of the document. Your library or institution may give you access to the complete full text for this document in ProQuest.

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ChurchillClub (Links to an external site.). (2012).Technology in education: How will it change the game? [video]. (1:30:05 minutes)

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5.7.12 Technology in Education: How Will it Change the Game?

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Speakers: Linda Burch, Chief Education & Strategy Officer, Common Sense Media Ben Chun, Educator, Galileo Academy of Science & Technology Anthony Salcito, Vice President of Worldwide Education, Microsoft Lucien Vattel, Owner, Chizuru Games & Executive Director, GameDesk Noah Wardrip-Fruin, Associate Professor of Computer Science, UC Santa Cruz & Co-director, Expressive Intelligence Studio Moderator: Tony Wan, Associate Editor, EdSurge In March 2012, US Secretary of Education Arne Duncan expressed his conviction that "technology is a game-changer in the field of education."

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Boot, W. R., & Kramer, A. F. (2014, November). The brain-games conundrum: does cognitive training really sharpen the mind? (Links to an external site.). In Cerebrum: the Dana forum on brain science (Vol. 2014). Dana Foundation.

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Editor's Note:

Few topics in the world of neuroscience evoke as much debate as the effectiveness of cognitive training. Do you misplace your keys regularly? Forget appointments? Have trouble remembering names? No worries. A host of companies promise to "train" your brain with games designed to stave off mental decline. Regardless of their effectiveness, their advertising has convinced tens of thousands of people to open their wallets. As our authors review the research on cognitive-training products, they expose the science surrounding the benefits of brain games as sketchy at best.

The numbers are staggering. Brain-training products are a billion-dollar industry whose revenues are predicted to surpass $6 billion by 2020. 1 One of the more popular brain-training programs, Lumosity, recently reached the milestone of 50 million members, 2 likely in part due to an advertising campaign that spanned radio, television, and the Internet. Nintendo's Brain Age has sold millions of copies and is among the best-selling Nintendo DS games of all time. 3 These statistics suggest a belief that brain training produces meaningful benefits, and this belief does not appear to be restricted to individual consumers. The fact that health-insurance companies have begun making brain-training products available to their clients suggests a perception in the health-care industry that the products work.

But the issue of what does and doesn't work is complex. The basic assumption behind almost all commercial brain-training programs is that practicing one or more tasks leads to improved performance of other, untrained tasks. The programs often present individuals with a series of simple games that might require the player to remember the properties of briefly presented pictures, to keep track of multiple moving objects, to recognize complex patterns, or to rapidly detect the presence of target objects in the visual periphery. With practice, players become faster and more accurate at performing these tasks. These products would be of little value if players improved only on the trained games, however. The critical question is whether transfer of training occurs. Does extended practice of the trained games result in general perceptual and cognitive improvements that boost performance of meaningful, real-life tasks such as driving, remembering names and faces, and keeping track of finances?

Where It Began

Psychologists have systematically studied the issue of transfer of training for over a century. The early work of American psychologist Edward Thorndike (1874-1949) set the stage for much of the research that followed. Thorndike, a professor at Columbia University's Teachers College, conducted a series of influential studies in which participants practiced one task (for example, estimating the area of a rectangle) repetitively for an extended period of time. After participants demonstrated improvement, he would ask them to perform a different, so-called transfer task (such as estimating the area of a triangle). Thorndike consistently observed large gains on practiced tasks, but these gains were weakly (if at all) associated with improved performance on transfer tasks. Thorndike also tested the idea that training in Latin would result in a more disciplined mind, which would improve performance in a variety of other subjects. He observed no such advantage.

Thorndike's research led him to conclude that transfer of training occurred only if the practiced task and the transfer tasks shared "identical elements," 4 and that the elements of most tasks are different enough from those of other tasks that transfer of training was rare. He believed that "the mind is so specialized into a multitude of independent capacities that we alter human nature only in small spots, and any special school training has a much narrower influence upon the mind as a whole than has commonly been supposed." 5

Thorndike's conclusions are consistent with modern theories proposing that practice results in cognitive adaptations that develop over time and are specific to the practiced task, 6 as well as with theories of learning that link improved task performance to the retrieval from memory of specific instances of the same task encountered previously. 7 The implication of Thorndike's empirical findings and theoretical views is that attempts to train a person on one task and thus bring about improvements in tasks other than the trained task are likely to fail unless the tasks are similar in terms of their elements or components.

Other scholarly views, however, allow for greater possibilities when it comes to transfer of training. Some research suggests that important moderators influence the degree of transfer resulting from training. One moderator is the degree of variability encountered during training, such that more variable training leads to greater transfer. 8, 9 Transfer may also be more likely to occur when performance of the practiced task and the transfer task depend on overlapping neural circuits. For example, some researchers observed transfer between a trained and an untrained task to the extent that both tasks activated a region of the brain called the striatum, while they observed no transfer when this region was not activated during an unpracticed task. 10 Finally, certain types of training may sharpen abilities that are so fundamental to a wide variety of tasks that performance of additional untrained tasks improves. For example, the performance of all tasks requires some degree of learning. If cognitive training helps individuals make better use of statistical/probabilistic information within a task, it could account for superior performance across a variety of untrained tasks. (For discussion of the "learning to learn" hypothesis of transfer, see endnote 11.)

While many theoretical accounts of learning reflect skepticism regarding the ability of cognitive training to improve the performance of untrained tasks, under certain conditions and with certain types of training, these effects may be observable. These theoretical accounts make it clear that it is not safe to assume that all types of cognitive training will produce meaningful benefits affecting important everyday tasks.

Empirical evidence that certain software packages and digital games are capable of improving perceptual and cognitive abilities that transfer to untrained tasks is mixed. Some studies had positive results, while others did not. And even in studies with positive results, interpretations of transfer effects aren't always straightforward. This is still a very active area of research.

Popular Approaches

There are three popular approaches to improving cognition: brain-training programs, working-memory training, and video-game training. The ACTIVE (Advanced Cognitive Training for Independent and Vital Elderly) clinical trial was the largest test of whether brain training can improve perceptual and cognitive abilities in older adults. 12 Over 2,800 participants were randomly assigned to one of four conditions: memory training, reasoning training, speed-of-processing training, or a no-contact control group. Intervention groups received 10 training sessions, each approximately 60 to 75 minutes long (some participants also received a few booster sessions in the years following training). Transfer tasks included laboratory-based tests of cognition (proximal outcomes) and self-reported and simulated performance-based measures of daily functioning (primary outcomes).

Immediately after training, researchers observed large improvements that were specific to each type of training intervention (for example, speed-of-processing trained participants improved on laboratory tasks measuring speed but not on tasks measuring memory or reasoning). Participants maintained most of these improvements even when they were tested 10 years later. However, researchers observed no improvements on measures of everyday functioning immediately after training, one year after training, or two years after training. However, tests of participants 5 and 10 years later indicated more promise. Compared to the no-contact control group, five years after training, the reasoning group self-reported fewer daily-living problems, the speed-of-processing group was less likely to cease driving, the speed-of-processing and reasoning groups were involved in fewer at-fault automobile crashes, and the speed-of-processing group reported less of a decline in health-related quality of life. Researchers attributed these delayed effects to the facts that (1) at the start of the intervention participants were cognitively healthy, and (2) a certain amount of decline was necessary in order to reveal transfer effects.

Other studies have had less encouraging results, however. For example, Adrian Owen and colleagues randomly assigned more than 11,000 online participants between the ages of 18 and 60 to receive six weeks of reasoning training, to receive six weeks of visuospatial/attention training, or to be part of an active control group that answered trivia questions. 13 Despite the tremendous sample size, neither training group demonstrated improved general ability on a battery of neuropsychological tests.

In general, it is hard to draw straightforward conclusions from the current body of literature on brain training, even when significant effects are observed. With the notable exception of the ACTIVE trial, these studies generally focus on outcome measures based on abstract neuropsychological tests and utilize weak control groups. A recent test of a popular commercial cognitive-training program, for example, assessed transfer with an abstract digit/tone categorization task. 14 While researchers observed some evidence of transfer to neuropsychological tests of alertness and distraction, the extent to which transfer to the performance of important everyday tasks was unclear. As with any intervention, brain-training studies need to prove convincingly that transfer-task improvement cannot be accounted for by a placebo effect. 15 That is, researchers need to rule out any possibility that the group receiving brain training didn't improve more than the control group did simply because their treatment caused them to expect this outcome. 16 Julia Mayas et al. compared a group that received intense brain training to a control group that participated in discussion groups. 14 It is unclear whether participants who merely discussed issues related to aging would expect as much improvement on the transfer task compared to participants who received challenging and adaptive cognitive training.

Much of the recent cognitive-training literature has focused on the potential of working-memory training to improve IQ and, specifically, fluid intelligence (the ability to reason and to solve novel problems). Susanne Jaeggi, Martin Buschkuehl, John Jonides, and Walter Perrig first reported that training that involved juggling multiple pieces of information in the mind affected fluid intelligence. 17 Training was adaptive, as participants had to remember visual and auditory information on each trial and compare this information to the information heard and seen one, two, three, or N trials back (referred to as an N-back task). When participants were able to remember information more successfully, they were given more information to remember ( N was increased throughout training). Compared to participants who did not receive training, participants who received N-back training improved more on transfer assessments that included problems from standard measures of IQ.

Jaeggi and colleagues interpreted the adaptive nature of their training and the necessity of working memory to solve complex problems as being supportive of transfer to measures of intelligence. However, after this initial positive finding, other scholars raised a variety of methodological criticisms of this and other working-memory-training studies. 18 Furthermore, other studies could not replicate the effect of working-memory training on fluid intelligence. 19- 21 A recent meta-analysis found that when most existing studies were considered together, working-memory training appeared to have a small but reliable effect on measures of IQ. 22 But the most rigorous studies-those that included an active control group to help address the problem of placebo effects-found almost no effect at all. Given the mixed state of the literature, two problematic possibilities exist: (1) working-memory training may not improve fluid IQ, or if it has an effect, the effect may be small, and (2) important but unknown moderators may determine who benefits from this type of training and who does not. 23

Over the past decade, some commercial and custom video games have also generated excitement about their potential to improve a variety of perceptual and cognitive abilities. This excitement has been heavily influenced by the groundbreaking work of C. Shawn Green and Daphne Bavelier. 24 Their initial study, which focused on the effects of fast-paced action video games (typically involving violent, first-person shooters), found not only that action gamers demonstrated superior visual and attentional abilities compared to nongamers, but also that nongamers could improve these abilities with just a small amount of action-game training. This finding led to dozens of additional investigations into other abilities that might be improved through action-game training. Researchers have linked superior attention, vision, processing speed, dual-tasking ability, and decision-making to action-game play through cross-sectional studies comparing gamers to nongamers, intervention studies training nongamers to play action games, or both. 25 Other studies have suggested that game training could ameliorate age-related cognitive decline. 26 Unlike focused N-back training, video games tap a variety of perceptual, cognitive, and motor processes, likely ensuring a greater degree of cognitive and neural overlap between trained and untrained tasks. This might explain the broad degree of transfer that seems to come from game training.

While this line of research is exciting, and it appears to indicate transfer that is much broader than that caused by any type of intervention investigated thus far, we must consider some important caveats when proposing to improve general cognitive abilities with video-game interventions. First, game effects do not always replicate, 27, 28 again suggesting either smaller effects on cognition than previously reported or the existence of moderators that determine whether an individual might benefit from game training. Second, scholars have raised a variety of methodological criticisms of the studies that provide evidence in support of game effects. 15, 29, 30 Finally, as with previous types of interventions discussed here, there is a dearth of studies linking video-game interventions to better performance of meaningful everyday tasks and meaningful activities such as avoiding crashes while driving, succeeding academically or professionally, and making complex life decisions such as those involved in the purchase of a new home.

Promises, Promises

What do the sellers of cognitive-training products promise? Should consumers purchase and use them? A careful inspection reveals that most commercial brain-training companies are relatively conservative with respect to their advertised claims, at least when explicitly discussing potential improvements on everyday tasks. It is exceedingly unlikely for a company to claim that its product could help a driver avoid a dangerous crash, a worker advance his or her career, or an older adult live independently longer. Instead, claims in these commercials and advertisements are vague. They highlight improvements to more abstract qualities, such as reaction time, attention, and memory. Few specify the exact nature of these improvements-for example, reaction to what? Memories of what? These vague claims are justified in that the products' training tasks involve these abilities, and performance on the training tasks improves with practice.

The critical question, however, is the degree to which these improvements transfer to more meaningful activities. Cognitive-training advertisements typically ignore this issue. These ads typically feature product users (or actors portraying users) discussing why they are using the product (for example, "to remember names of people I meet," "to get ahead at work"). The companies' websites also tend to feature user anecdotes, as well as a section explaining the science behind their product and referencing completed, peer-reviewed (but sometimes non-peer-reviewed) studies. In many instances, however, these studies examine something other than the program being advertised; they assess benefits with abstract laboratory tasks rather than everyday ones; and they lack critical control conditions necessary to link improvements to the product. While pharmaceutical advertisements are strictly regulated, this is not the case for brain-fitness program advertisements. This may partly be due to the companies' lack of explicit claims regarding improvements to everyday, meaningful activities, as well as the lack of claims that their products are intended to treat specific conditions, such as age-related brain diseases.

To Be Determined

Before confidently recommending the use of brain-training programs to improve cognition meaningfully and to address age-related cognitive decline, researchers must address the following questions and issues:

Comparative effectiveness. If brain-training programs and video games are in fact effective, researchers must determine the programs' comparative effectiveness. Per hour invested, how do brain-training programs and games compare to one another with respect to their ability to improve cognition meaningfully? How do they compare to other cognitively beneficial (and potentially more enjoyable) activities such as aerobic exercise, 31 digital photography, quilting, and volunteer work? 32- 34 Do certain activities transfer especially well to tasks such as driving, while other activities improve the memory functions that support medication adherence? Answers to these questions would help shape recommendations regarding the amount and type of brain-training activities a given individual should engage in.

Intervention adherence. As with physical exercise and pharmaceutical treatments, brain-training programs yield little to no benefit unless people adhere to them. A recent study found that digital game-based training associated with a variety of perceptual and cognitive improvements resulted in no benefit in a sample of older adults, likely due to the fact that adherence was poor for the intervention expected to produce the largest effect. 35 The challenges of ensuring cognitive-intervention adherence may be most analogous to the challenges of promoting adherence to hypertension treatments. Given that hypertension is typically asymptomatic, treatment benefits are not readily apparent, such that the costs (e.g., drug side effects) become more salient than the real but unseen benefits. Similarly, cognitive training may not result in immediate, perceptible benefits, but it might reduce cognitive problems years in the future. 36 Thus it is important that researchers examine individual differences that predict adherence (for such an attempt with exercise, see endnote 37) as they determine how to promote adherence to brain-training games and programs.

Moderating variables. Currently we know little about who benefits most from brain training. However, researchers have begun to use data from the ACTIVE trial in an attempt to answer this question. George W. Rebok et al. found that memory-training benefits were greater for participants with higher levels of education and better self-reported health. The discovery of moderating variables may help health-care professionals prescribe either general cognitive training or specific types of cognitive training. However, answering these questions will require fairly large samples to tease out the cognitive, environmental, disease, and genetic factors that make an individual more or less susceptible to the benefits of cognitive training.

Methodological rigor and replication. Scholars have leveled a variety of criticisms against studies that report evidence of transfer of training from video games and brain-training programs to other tasks. These criticisms should be addressed before practitioners make strong recommendations that individuals engage in these activities. In addressing the potential of placebo effects, expectations for improvement on transfer tasks can be assessed upon completion of the intervention, 16 or in a separate group of individuals. 39 When expectations for improvement are equal for intervention and control groups, but actual transfer effects differ, placebo effects are unlikely. In addition to addressing methodological concerns, researchers should also note that the brain-training literature contains few direct replications. This is understandable because these types of studies are difficult and expensive to run. However, replication studies would be of tremendous value in answering the question of whether reliable transfer gains can be expected to result from any specific type of training. These types of studies should be incentivized.

Meaningful Measures, Outcomes, and Questions

It's a no-brainer that individuals purchase and engage in brain-training programs because they wish to perform better on certain tasks that are meaningful to them. Yet the majority of studies in the literature use relatively simple, process-pure laboratory tasks to assess transfer of benefit. 40 Few studies assess performance on simulated everyday tasks (for example, through a driving simulation), and far fewer assess real-world outcomes (e.g., automobile crash rate, loss of independence, or loss of wealth due to fraud). These types of important and meaningful outcomes can be assessed only in large-scale longitudinal studies that follow cognitively trained individuals over a decade or more.

Other important questions relate to when cognitive training should begin, how much an individual should train, and how long training gains might last. If brain training is judged to be effective, should it begin when someone is in his 20s? In her 60s? Should individuals train every day? Most days of the week? Is it better to engage in long, spaced-out training sessions or fewer, shorter training sessions? Does an individual need to continue training in order to maintain gains, or do training gains persist long after training has ceased? Is there a point of diminishing returns at which the training task becomes so automated that it no longer exercises the abilities it was designed to improve? Can people enhance the potential benefits of cognitive training if they pair it with physical activity and/or social interaction? Only a handful, if any, studies have addressed these important issues.

These are only some of the unanswered questions regarding brain training. What, if anything, can today's doctors recommend to those who wish to enhance (or to maintain) their cognitive abilities? At this point, any blanket endorsement of a certain brain-training program would be premature. Yet there appears to be enough accumulated evidence that being cognitively inactive is not a good strategy for maintaining cognitive health. Doing something to remain active and engaged is likely an investment worth making. Cognitive activity takes many forms, and there is currently little evidence suggesting that any particular software package is best at improving cognition, or that any brain-training product is better than other engaging activities, such as learning a new language or instrument, creative writing, or learning to dance.

These latter alternatives have the advantages of being inexpensive, being especially enjoyable, and providing a useful and valuable skill-even if there were no general cognitive benefits associated with them. Aerobic exercise may be one of the safest bets for those wishing to improve their cognition, as animal models and human cross-sectional and intervention studies all indicate benefits to brain function, structure, and cognition. 41 This option may be particularly beneficial because it also comes with a host of physical health benefits. Exercise would be a worthwhile investment even if it had no effect on cognition.

In the future, more precise recommendations will be possible as more evidence accumulates and the methodological rigor of intervention studies continues to advance. Large-sample studies that include real or simulated performance on important everyday tasks, extended post-training testing and observation periods (similar to those used in the ACTIVE study), and large individual-difference batteries (cognitive, genetic, neurophysiological) that assess moderators of transfer effects will be especially valuable in informing these recommendations.

Bios

Arthur F. Kramer, Ph.D., is the director of the Beckman Institute for Advanced Science & Technology and the Swanlund Chair and professor of psychology and neuroscience at the University of Illinois. Kramer's research projects include topics in cognitive psychology, cognitive neuroscience, aging, and human factors. A major focus of his lab's recent research is the understanding and enhancement of cognitive and neural plasticity across the lifespan. He is a former associate editor of Perception and Psychophysics and is currently a member of six editorial boards. Kramer, who received his Ph.D. in cognitive/experimental psychology from the University of Illinois in 1984, is also a fellow of the American Psychological Association, American Psychological Society, a former member of the executive committee of the International Society of Attention and Performance, and a recipient of an NIH Ten Year MERIT Award. His research has been featured in the New York Times, Wall Street Journal, Washington Post, Chicago Tribune, and on CBS Evening News, Today Show, NPR and Saturday Night Live.

Walter R. Boot, Ph.D., is an associate professor of psychology at Florida State University. His research interests include how humans perform and learn to master complex tasks (especially tasks with safety-critical consequences), how age influences perceptual and cognitive abilities vital to the performance of these tasks, and how technological interventions can improve the well-being and cognitive functioning of older adults. Boot has published extensively on the topic of technology-based interventions involving digital games, and is one of six principal investigators of the Center for Research and Education on Aging and Technology Enhancement. He has also been funded by the Florida Department of Transportation since 2011 to conduct studies of aging road users; specifically examining countermeasures to protect older adults as they navigate roadways as drivers, cyclists, and pedestrians. Boot received his Ph.D. in psychology from the University of Illinois at Urbana-Champaign in 2007.

References

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The Brain-Games Conundrum: Does Cognitive Training Really Sharpen the Mind?

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Few topics in the world of neuroscience evoke as much debate as the effectiveness of cognitive training. Do you misplace your keys regularly? Forget appointments? Have trouble remembering names? No worries. A host of companies promise to "train" your brain with games designed to stave off mental decline.

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AmpliVox Sound Systems. (2011).Top 10 reasons to use technology in education: iPad, Tablet, Computer, Listening Centers. [video]. (5:29 minutes).

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Top 10 Reasons to Use Technology in Education: iPad, Tablet, Computer, Listening Centers

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Adbobe. (2012). How is technology transforming education? Sir Ken Robinson Video Series from Adobe Education. [video]. (2:20 minutes)

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How is Technology Transforming Education? Sir Ken Robinson Video Series from Adobe Education

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Technology is changing the world rapidly, impacting the way students learn and opening new possibilities for educators. Take a look what Sir Ken Robinson had to say when asked about the role of technology in education. Follow the series at http://adobe.ly/YT121R How is technology is changing your classroom?