• Is ed tech really working? 5 core tenets to rethink how we buy, use, and measure new tools

    by Todd Bloom, David Deschryver, Pam Moran, Chrisandra Richardson, Joseph South, Katrina Stevens

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    This is the fifth in a series of essays surrounding the EdTech Efficacy Research Symposium, a gathering of 275 researchers, teachers, entrepreneurs, professors, administrators, and philanthropists to discuss the role efficacy research should play in guiding the development and implementation of education technologies. This series was produced in partnership with Pearson, a co-sponsor of the symposium co-hosted by the University of Virginia’s Curry School of Education, Digital Promise, and the Jefferson Education Accelerator. Click through to read the firstsecondthird, and fourth pieces.

    Education technology plays an essential role in our schools today. Whether the technology supports instructional intervention, personalized learning, or school administration, the successful application of that technology can dramatically improve productivity and student learning.

    That said, too many school leaders lack the support they need to ensure that educational technology investment and related activities, strategies, or interventions are evidence-based and effective. This gap between opportunity and capacity is undermining the ability of school leaders to move the needle on educational equity and to execute on the goals of today’s K-16 policies. The education community needs to clearly understand this gap and take some immediate steps to close it.

    The time is ripe

    The new federal K-12 law, the Every Students Succeeds Act, elevates the importance of evidence-based practices in school purchasing and implementation practices. The use of the state’s allocation for school support and improvement illustrates the point. Schools that receive these funds must invest only in activities, strategies, or interventions that demonstrate a statistically significant effect on improving student outcomes or other relevant outcomes.

    That determination must rely on research that is well designed and well implemented, as defined in the law. And once implementation begins, the U.S. Department of Education asks schools to focus on continuous improvement by collecting information about the implementation and making necessary changes to advance the goals of equity and educational opportunity for at-risk students. The law, in short, links compliance with evidence-based procurement and implementation that is guided by continuous improvement.

    New instructional models in higher education rely on evidence-based practices if they are to take root. School leaders are under intense pressure to find ways to make programs more affordable, student-centered, and valuable to a rapidly changing labor market. Competency-based education (the unbundling of certificates and degrees into discrete skills and competencies) is one of the better-known responses to the challenge, but the model will likely stay experimental until there is more evidence of success.

    “We are still just beginning to understand CBE,” Southern New Hampshire University President Paul LeBlanc said. “Project-based learning, authentic learning, well-done assessment rubrics — those are all good efforts, but do we have the evidence to pass muster with a real assessment expert? Almost none of higher ed would.”

    It is easy to forget that the abundance of educational technology is a relatively new thing for schools and higher ed institutions. Back in the early 2000s, the question was how to make new educational technologies viable instructional and management tools. Education data was largely just a lagging measure used for school accountability and reporting.

    Today, the data can provide strong, real-time signals that advance productivity through, for example, predictive analytics, personalized learning, curriculum curating and delivery, and enabling the direct investigation into educational practices that work in specific contexts. The challenge is how to control and channel the deluge of bytes and information streaming from the estimated $25.4 billion K-16 education technology industry.

    “It’s [now] too easy to go to a conference and load up at the buffet of innovations. That’s something we try hard not to do,” said Chad Ratliff, director of instructional programs for Virginia’s Albemarle County Schools. The information has to be filtered and vetted, which takes time and expertise.

    Improving educational equity is the focus of ESSA, the Higher Education Act, and a key reason many school leaders chose to work in education. Moving the needle increasingly relies on evidence-based practices. As the Aspen Institute and Council of Chief State School Officers point out in a recent report, equity means — at the very least — that “every student has access to the resources and educational rigor they need at the right moment in their education despite race, gender, ethnicity, language, disability, family background, or family income.”

    Embedded in this is the presumption that the activities, strategies, or interventions actually work for the populations they intend to benefit.

    Educators cannot afford to invest in ineffective activities. At the federal K-12 level, President Donald Trump is proposing that, next year, Congress cut spending for the Education Department and eliminate many programs, including $2.3 billion for professional development programs, $1.2 billion for after-school funds, and the new Title IV grant that explicitly supports evidence-based and effective technology practices in our schools.

    Higher education is also in a tight spot. The president seeks to cut spending in half for Federal Work-Study programs, eliminate Supplemental Educational Opportunity grants, and take nearly $4 million from the Pell Grant surplus for other government spending. At the same time, Education Secretary Betsy DeVos is reviewing all programs to explore which can be eliminated, reduced, consolidated, or privatized.

    These proposed cuts and reductions increase the urgency for school leaders to tell better stories about the ways they use the funds to improve educational opportunities and learning outcomes. And these stories are more compelling (and protected from budget politics) when they are built upon evidence.

    Too few resources

    While this is a critical time for evidence-based and effective program practices, here is the rub: The education sector is just beginning to build out this body of knowledge, so school leaders are often forging ahead without the kind of guidance and research they need to succeed.

    The challenges are significant and evident throughout the education technology life cycle. For example, it is clear that evidence should influence procurement standards, but that is rarely the case. The issue of “procurement standards” is linked to cost thresholds and related competitive and transparent bidding requirements. It is seldom connected with measures of prior success and research related to implementation and program efficacy. Those types of standards are foreign to most state and local educational agencies, left to “innovative” educational agencies and organizations, like Digital Promise’s League of Innovative Schools, to explore.

    Once the trials of implementation begin, school leaders and their vendors typically act without clear models of success and in isolation. There just are not good data on efficacy for most products and implementation practices, which means that leaders cannot avail themselves of models of success and networks of practical experience. Some schools and institutions with the financial wherewithal, like Virginia’s Albemarle and Fairfax County Public Schools, have created their own research process to produce their own evidence.

    In Albemarle, for example, learning technology staff test-bed solutions to instructional and enterprise needs. Staff spend time observing students and staff using new devices and cloud-based services. They seek feedback and performance data from both teachers and students in response to questions about the efficacy of the solution. They will begin with questions like “If a service is designed to support literacy development, what variable are we attempting to affect? What information do we need to validate significant impact?” Yet, like the “innovators” of procurement standards, these are the exceptions to the rule.

    And as schools make headway and immerse themselves in new technologies and services, the bytes of data and useful information multiply, but the time and capacity necessary to make them useful remains scarce. Most schools are not like Fairfax and Albemarle counties. They do not have the staff and experts required to parse the data and uncover meaningful insights into what’s working and what’s not. That kind of work and expertise isn’t something that can be simply layered onto existing responsibilities without overloading and possibly burning out staff.

    “Many schools will have clear goals, a well-defined action plan that includes professional learning opportunities, mentoring, and a monitoring timeline,” said Chrisandra Richardson, a former associate superintendent for Montgomery County Public Schools in Maryland. “But too few schools know how to exercise a continuous improvement mindset, how to continuously ask: ‘Are we doing what we said we would do — and how do we course-correct if we are not?’ ”

    Immediate next steps

    So what needs to be done? Here are five specific issues that the education community (philanthropies, universities, vendors, and agencies) should rally around.

    • Set common standards for procurement. If every leader must reinvent the wheel when it comes to identifying key elements of the technology evaluation rubric, we will ensure we make little progress — and do so slowly. The sector should collectively secure consensus on the baseline procurement standards for evidence-based and research practices and provide them to leaders through free or open-source evaluative rubrics or “look fors” they can easily access and employ.
    • Make evidence-based practice a core skill for school leadership. Every few years, leaders in the field try to pin down exactly what core competencies every school leader should possess (or endeavor to develop). If we are to achieve a field in which leaders know what evidence-based decision-making looks like, we must incorporate it into professional standards and include it among our evaluative criteria.
    • Find and elevate exemplars. As Charles Duhigg points out in his recent best seller Smarter Faster Better, productive and effective people do their work with clear and frequently rehearsed mental models of how something should work. Without them, decision-making can become unmoored, wasteful, and sometimes even dangerous. Our school leaders need to know what successful evidence-based practices look like. We cannot anticipate that leader or educator training will incorporate good decision-making strategies around education technologies in the immediate future, so we should find alternative ways of showcasing these models.
    • Define “best practice” in technology evaluation and adoption. Rather than force every school leader to develop and struggle to find funds to support their own processes, we can develop models that can alleviate the need for schools to develop and invest in their own research and evidence departments. Not all school districts enjoy resources to investigate their own tools, but different contexts demand differing considerations. Best practices help leaders navigate variation within the confines of their resources. The Ed Tech RCE Coach is one example of a set of free, open-source tools available to help schools embed best practices in their decision-making.
    • Promote continuous evaluation and improvement. Decisions, even the best ones, have a shelf life. They may seem appropriate until evidence proves otherwise. But without a process to gather information and assess decision-making efficacy, it’s difficult to learn from any decisions (good or bad). Together, we should promote school practices that embrace continuous research and improvement practices within and across financial and program divisions to increase the likelihood of finding and keeping the best technologies.

    The urgency to learn about and apply evidence to buying, using, and measuring success with ed tech is pressing, but the resources and protocols they need to make it happen are scarce. These are conditions that position our school leaders for failure — unless the education community and its stakeholders get together to take some immediate actions.

    This series is produced in partnership with Pearson. The 74 originally published this article on September 11th, 2017, and it was re-posted here with permission.

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  • Communicate often and better: How to make education research more meaningful

    by Jay Lynch, PhD and Nathan Martin, Pearson

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    Question: What do we learn from a study that shows a technique or technology likely has affected an educational outcome?

    Answer: Not nearly enough.

    Despite widespread criticism, the field of education research continues to emphasize statistical significance—rejecting the conclusion that chance is a plausible explanation for an observed effect—while largely neglecting questions of precision and practical importance. Sure, a study may show that an intervention likely has an effect on learning, but so what? Even researchers’ recent efforts to estimate the size of an effect don’t answer key questions. What is the real-world impact on learners? How precisely is the effect estimated? Is the effect credible and reliable?

    Yet it’s the practical significance of research findings that educators, administrators, parents and students really care about when it comes to evaluating educational interventions. This has led to what Russ Whitehurst has called a “mismatch between what education decision makers want from the education research and what the education research community is providing.”

    Unfortunately, education researchers are not expected to interpret the practical significance of their findings or acknowledge the often embarrassingly large degree of uncertainty associated with their observations. So, education research literature is filled with results that are almost always statistically significant but rarely informative.

    Early evidence suggests that many edtech companies are following the same path. But we believe that they have the opportunity to change course and adopt more meaningful ways of interpreting and communicating research that will provide education decision makers with the information they need to help learners succeed.

    Admitting What You Don’t Know

    For educational research to be more meaningful, researchers will have to acknowledge its limits. Although published research often projects a sense of objectivity and certainty about study findings, accepting subjectivity and uncertainty is a critical element of the scientific process.

    On the positive side, some researchers have begun to report what is known as standardized effect sizes, a calculation that helps compare outcomes in different groups on a common scale. But researchers rarely interpret the meaning of these figures. And the figures can be confusing. A ‘large’ effect actually may be quite small when compared to available alternatives or when factoring in the length of treatment, and a ‘small’ effect may be highly impactful because it is simple to implement or cumulative in nature.

    Confused? Imagine the plight of a teacher trying to decide what products to use, based on evidence—an issue of increased importance since the Every Student Succeeds Act (ESSA) promotes the use of federal funds for certain programs, based upon evidence of effectiveness. The newly-launched Evidence for ESSA admirably tries to help support that process, complementing the What Works Clearinghouse and pointing to programs that have been deemed “effective.” But when that teacher starts comparing products, say Math in Focus (effect size: +0.18) and Pirate Math (effect size: +0.37), the best choice isn’t readily apparent.

    It’s also important to note that every intervention’s observed “effect” is associated with a quantifiable degree of uncertainty. By glossing over this fact, researchers risk promoting a false sense of precision and making it harder to craft useful data-driven solutions. While acknowledging uncertainty is likely to temper excitement about many research findings, in the end it will support more honest evaluations of an intervention’s likely effectiveness.

    Communicate Better, Not Just More

    In addition to faithfully describing the practical significance and uncertainty around a finding, there also is a need to clearly communicate information regarding research quality, in ways that are accessible to non-specialists. There has been a notable unwillingness in the broader educational research community to tackle the challenge of discriminating between high quality research and quackery for educators and other non-specialists. As such, there is a long overdue need for educational researchers to be forthcoming about the quality and reliability of interventions in ways that educational practitioners can understand and trust.

    Trust is the key. Whatever issues might surround the reporting of research results, educators are suspicious of people who have never been in the classroom. If a result or debunked academic fad (e.g. learning styles) doesn’t match their experience, they will be tempted to dismiss it. As education research becomes more rigorous, relevant, and understandable, we hope that trust will grow. Even simply categorizing research as either “replicated” or “unchallenged” would be a powerful initial filtering technique given the paucity of replication research in education. The alternative is to leave educators and policy-makers intellectually adrift, susceptible to whatever educational fad is popular at the moment.

    At the same time, we have to improve our understanding of how consumers of education research understand research claims. For instance, surveys reveal that even academic researchers commonly misinterpret the meaning of common concepts like statistical significance and confidence intervals. As a result, there is a pressing need to understand how those involved in education interpret (rightly or wrongly) common statistical ideas and decipher research claims.

    A Blueprint For Change

    So, how can the education technology community help address these issues?

    Despite the money and time spent conducting efficacy studies on their products, surveys reveal that research often plays a minor role in edtech consumer purchasing decisions. The opaqueness and perceived irrelevance of edtech research studies, which mirror the reporting conventions typically found in academia, no doubt contribute to this unfortunate fact. Educators and administrators rarely possess the research and statistical literacy to interpret the meaning and implications of research focused on claims of statistical significance and measuring indirect proxies for learning. This might help explain why even well-meaning educators fall victim to “learning myths.”

    And when nearly every edtech company is amassing troves of research studies, all ostensibly supporting the efficacy of their products (with the quality and reliability of this research varying widely), it is understandable that edtech consumers treat them all with equal incredulity.

    So, if the current edtech emphasis on efficacy is going to amount to more than a passing fad and avoid devolving into a costly marketing scheme, edtech companies might start by taking the following actions:

    • Edtech researchers should interpret the practical significance and uncertainty associated with their study findings. The researchers conducting an experiment are best qualified to answer interpretive questions around the real-world value of study findings and we should expect that they make an effort to do so.
    • As an industry, edtech needs to work toward adopting standardized ways to communicate the quality and strength of evidence as it relates to efficacy research. The What Works Clearinghouse has made important steps, but it is critical that relevant information is brought to the point of decision for educators. This work could resemble something like food labels for edtech products.
    • Researchers should increasingly use data visualizations to make complex findings more intuitive while making additional efforts to understand how non-specialists interpret and understand frequently reported statistical ideas.
    • Finally, researchers should employ direct measures of learning whenever possible rather than relying on misleading proxies (e.g., grades or student perceptions of learning) to ensure that the findings reflect what educators really care about. This also includes using validated assessments and focusing on long-term learning gains rather than short-term performance improvement.

    This series is produced in partnership with Pearson. EdSurge originally published this article on April 1, 2017, and it was re-posted here with permission.

     

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  • Technical & human problems with anthropomorphism & technopomorphism

    by Denis Hurley, Director of Future Technologies, Pearson

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    Anthropomorphism is the attribution of human traits, emotions, and intentions to non-human entities (OED). It has been used in storytelling from Aesop to Zootopia, and people debate its impact on how we view gods in religion and animals in the wild. This is out of scope for this short piece.

    When it comes to technology, anthropomorphism is certainly more problematic than it is useful. Here are three examples:

    1. Consider how artificial intelligence is described like a human brain, which is not how AI works. This results in people misunderstanding its potential uses, attempting to apply it in inappropriate ways, and failing to consider applications where it could provide more value. Ines Montani has written an excellent summary on AI’s PR problem.
    2. More importantly, anthropomorphism contributes to our fear of progress, which often leads to full-blown technopanics. We are currently in a technopanic brought about by the explosion of development in automation and data science. Physically, these machines are often depicted as bipedal killing machines, which is not even the most effective form of mobility for a killing machine. Regarding intent, superintelligent machines are thought of as a threat not just to employment but our survival as a species. This assumes that these machines will treat homo sapiens similar to how homo sapiens have treated other species on this planet.
    3. Pearson colleague Paul Del Signore asked via Twitter, “Would you say making AI speak more human-like is a successful form of anthropomorphism?” This brings to mind a third major problem with anthropomorphism: the uncanny valley. While adding humanlike interactions can contribute to good UX, too much (but not quite enough) similarity to a human can result in frustration, discomfort, and even revulsion.

    Historically, we have used technology to achieve both selfish and altruistic goals. Overwhelmingly, however, technology has helped us reach a point in human civilization in which we are the most peaceful and healthy in history. In order to continue on this path, we must design machines to function in ways that utilize their best machine-like abilities.

    Technopomorphism is the attribution of technological characteristics to human traits, emotions, intentions, or biological functions. Think of how people may describe a thought process like cogs in a machine or someone’s capacity for work may be described with bandwidth.

    A Google search for the term “technopomorphism” only returns 40 results, and it is not listed in any online dictionary. However, I think the term is useful because it helps us to be mindful of our difference from machines.

    It’s natural for humans to use imagery that we do understand to try to describe things we don’t yet understand, like consciousness. Combined with our innate fear of dying, we imagine ways of deconstructing and reconstructing ourselves as immortal or as one with technology (singularity). This is problematic for at least two reasons:

    1. It restricts the ways in which we may understand new discoveries about ourselves to very limited forms.
    2. It often leads to teaching and training humans to function as machines, which is not the best use of our potential as humans.

    It is increasingly important that we understand how humans can best work with technology for the sake learning. In the age of exponential technologies, that which makes us most human will be most highly valued for employment and is often used for personal enrichment.

    There may be some similarities, but we’re not machines. At least, not yet. In the meantime, I advocate for “centaur mentality.”

     

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  • Can Edtech support - and even save - educational research?

    by Jay Lynch, PhD and Nathan Martin, Pearson

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    There is a crisis engulfing the social sciences. What was thought to be known about psychology—based on published results and research—is being called into question by new findings and the efforts of individual groups like the Reproducibility Project. What we know is under question and so is how we come to know. Long institutionalized practices of scientific inquiry in the social sciences are being actively questioned, proposals put forth for needed reforms.

    While the fields of academia burn with this discussion, education results have remained largely untouched. But education is not immune to problems endemic in fields like psychology and medicine. In fact, there’s a strong case that the problems emerging in other fields are even worse in educational research. External or internal critical scrutiny has been lacking. A recent review of the top 100 education journals found that only 0.13% of published articles were replication studies. Education waits for its own crusading Brian Nosek to disrupt the canon of findings. Winter is coming.

    This should not be breaking news. Education research has long been criticized for its inability to generate a reliable and impactful evidence base. It has been derided for problematic statistical and methodological practices that hinder knowledge accumulation and encourage the adoption of unproven interventions. For its failure to communicate the uncertainty and relevance associated with research findings, like Value-Added Measures for teachers, in ways that practitioners can understand. And for struggling to impact educational habits (at least in the US) and how we develop, buy, and learn from (see Mike Petrilli’s summation) the best practices and tools.

    Unfortunately, decades of withering criticism have done little to change the methods and incentives of educational research in ways necessary to improve the reliability and usefulness of findings. The research community appears to be in no rush to alter its well-trodden path—even if the path is one of continued irrelevance. Something must change if educational research is to meaningfully impact teaching and learning. Yet history suggests the impetus for this change is unlikely to originate from within academia.

    Can edtech improve the quality and usefulness of educational research? We may be biased (as colleagues at a large and scrutinized edtech company), but we aren’t naïve. We know it might sound farcical to suggest technology companies may play a critical role in improving the quality of education research, given almost weekly revelations about corporations engaging in concerted efforts to distort and shape research results to fit their interests. It’s shocking to read efforts to warp public perception on the effects of sugar on heart disease or the effectiveness of antidepressants. It would be foolish not to view research conducted or paid for by corporations with a healthy degree of skepticism.

    Yet we believe there are signs of promise. The last few years has seen a movement of companies seeking to research and report on the efficacy of educational products. The movement benefited from the leadership of the Office of Education Technology, the Gates FoundationLearning AssemblyDigital Promise and countless others. Our own company has been on this road since 2013. (It’s not been easy!)

    These efforts represent opportunities to foment long-needed improvements in the practice of education research. A chance to redress education research’s most glaring weakness: its historical inability to appreciably impact the everyday activities of learning and teaching.

    Incentives for edtech companies to adopt better research practices already exist and there is early evidence of openness to change. Edtech companies possess a number of crucial advantages when it comes to conducting the types of research education desperately needs, including:

    • access to growing troves of digital learning data;
    • close partnerships with institutions, faculty, and students;
    • the resources necessary to conduct large and representative intervention studies;
    • in-house expertise in the diverse specialties (e.g., computer scientists, statisticians, research methodologists, educational psychologists, UX researchers, instructional designers, ed policy experts, etc.) that must increasingly collaborate to carry out more informative research;
    • a research audience consisting primarily of educators, students, and other non-specialists

    The real worry with edtech companies’ nascent efforts to conduct efficacy research is not that they will fail to conduct research with the same quality and objectivity typical of most educational research, but that they will fall into the same traps that currently plague such efforts. Rather than looking for what would be best for teachers and learners, entrepreneurs may focus on the wrong measures (p-values, for instance) that obfuscate people rather than enlighten them.

    If this growing edtech movement repeats the follies of the current paradigm of educational research, it will fail to seize the moment to adopt reforms that can significantly aid our efforts to understand how best to help people teach and learn. And we will miss an important opportunity to enact systemic changes in research practice across the edtech industry with the hope that academia follows suit.

    Our goal over the next three articles is to hold a mirror up, highlighting several crucial shortcomings of educational research. These institutionalized practices significantly limit its impact and informativeness.

    We argue that edtech is uniquely incentivized and positioned to realize long-needed research improvements through its efficacy efforts.

    Independent education research is a critical part of the learning world, but it needs improvement. It needs a new role model, its own George Washington Carver, a figure willing to test theories in the field, learn from them, and then to communicate them to back to practitioners. In particular, we will be focusing on three key ideas:

    Why ‘What Works’ Doesn’t: Education research needs to move beyond simply evaluating whether or not an effect exists; that is, whether an educational intervention ‘works’. The ubiquitous use of null hypothesis significance testing in educational research is an epistemic dead end. Instead, education researchers need to adopt more creative and flexible methods of data analysis, focus on identifying and explaining important variations hidden under mean scores, and devote themselves to developing robust theories capable of generating testable predictions that are refined and improved over time.

    Desperately Seeking Relevance: Education researchers are rarely expected to interpret the practical significance of their findings or report results in ways that are understandable to non-specialists making decisions based on their work. Although there has been progress in encouraging researchers to report standardized mean differences and correlation coefficients (i.e., effect sizes), this is not enough. In addition, researchers need to clearly communicate the importance of study findings within the context of alternative options and in relation to concrete benchmarks, openly acknowledge uncertainty and variation in their results, and refuse to be content measuring misleading proxies for what really matters.

    Embracing the Milieu: For research to meaningfully impact teaching and learning, it will need to expand beyond an emphasis on controlled intervention studies and prioritize the messy, real-life conditions facing teachers and students. More energy must be devoted to the creative and problem-solving work of translating research into useful and practical tools for practitioners, an intermediary function explicitly focused on inventing, exploring, and implementing research-based solutions that are responsive the needs and constraints of everyday teaching.

    Ultimately education research is about more than just publication. It’s about improving the lives of students and teachers. We don’t claim to have the complete answers but, as we expand these key principles over coming weeks, we want to offer steps edtech companies can take to improve the quality and value of educational research. These are things we’ve learned and things we are still learning.

    This series is produced in partnership with Pearson. EdSurge originally published this article on January 6, 2017, and it was re-posted here with permission.

     

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  • Learning through both physical and virtual discovery

    by Denis Hurley, Director of Future Technologies, Pearson

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    This morning, I read Bill McKibben’s “Pause! We Can Go Back!,” a review of David Sax’s The Revenge of Analog: Real Things and Why They Matter. My friend and mentor of twenty years, the filmmaker Jill Godmilow, emailed it to me. I immediately thought of Delicate Steve’s interview with Bob Boilen on “All Songs Considered,” and then I mentally time-traveled to 2011…

    I was in Austin in 2011 for SXSW, learning from other startups, networking, and promoting my own digital products. The interactive component of the conference ended with a “surprise” performance at the enormous Stubb’s BBQ concert venue. I reluctantly waited in line with hundreds of others, hopeful to hear something like LCD Soundsystem, who had appeared in a previous year. Once we were all inside, The Foo Fighters took the stage. Considered by many to be “the last great American rock band,” they’re just not my thing. A traveling companion saw the boredom on my face and asked, “Do you want to hear something different?”

    6th Street was dead for the first time all week (nearly all the conference attendees were at Stubb’s), and we popped into a small bar where about ten other patrons huddled near a wiry young man on a small stage. Delicate Steve began to play The Ballad of Speck and Pebble. My brain lit up. It was one of the most inspiring live performances I’ve ever heard.

    In my kitchen, six years later, while I was making applesauce with my earbuds in, Slate’s “Political Gabfest” ended, and Mr. Boilen’s voice came on to introduce Steve Marion, aka Delicate Steve, on “All Songs Considered.” Marion talked about being a “Napster kid” as well as how he was inspired to play music after his grandmother gave him a toy guitar.

    He dove into the rabbit holes of discovery that were available via the Internet to a kid living in northwestern New Jersey. Driven by curiosity and play, using the physical and virtual tools available to him, he began to create. Last year, he played slide guitar on Paul Simon’s new album, and next week, he’ll be at The Bowery Ballroom in New York City.

    In McKibbon’s review in The New York Review of Books, he comments, “Spotify’s playlists show people picking the same tunes over and over.” I believe the same was true when analogue music dominated. Virgin Megastore promoted the latest big release from one of the giant record labels.

    The difference now is that more tools — virtual and physical — are now available to us. How we use them is up to us. We need to ensure that everyone, especially young people are aware of them all and how to use them properly for discovery. Dig deep into that artists’s archive on Spotify. Flip through those old records on Bleeker Street.

    In the late 1990’s, Jill Godmilow taught me how to edit film and sound by hand while I was a student at The University of Notre Dame. I used an 8-plate Steenbeck. It was a lot of work to cut a film like that, but it helped me understand the value of a frame: 1/24 of a second.

    Now I have a child, and I try to help her understand how things work by making mechanical object available to her. She’ll pick up the hand-made kaleidoscope I brought back from London, or crank the Kikkerland music box to hear “Waltzing Matilda.” Together, we play both Minecraft and Clue. Her favorite Christmas present last month was a record player. She chooses to put on the Taylor Swift record “Red” over and over and over again. She also explores Minecraft videos made by other kids all over the world.

    Some of these interaction blend the virtual and the physical, like using the Osmo pizza game, learning math while playing, or programming Dash to wheel around the apartment, learning problem-solving.

    We can foster creativity and encourage exploration using whatever tools we have available to us. I am not advocating constant barrage of entertainment or toys — there is also value in escaping into a book or a tent in the woods — but new, digital tools are not necessarily a bad thing, and to many, they offer ways to learn and build, expanding their minds and enriching our culture.

    Explore, be weird, enjoy what you do, learn through what you enjoy. But do be careful not to lose yourself entirely into the virtual world. The physical world offers a nearly limitless amount of new experiences and adventures. These are thrilling to us because of our human nature, and even as we learn how to embrace the digital to a greater extent, we should do so to enrich our lives, not in an attempt to replace something that doesn’t need replacing.

    I will always be grateful to Jill Godmilow for showing me how to analyze the finest moving parts to a completed whole, which I often have to do in a purely digital format, where the individual elements are not so apparent. I appreciate the music from Delicate Steve, meticulously constructed with his mind and fingers through a medley of neuron-firings, Google searches, and guitar riffs.

    I am thankful that my daughter wonders at our Remington typewriter and miniature carousel, watches the interlocking pieces, and reconstructs some of these relationships with blocks on her iPad, with dominos on the table, and with her friends in the schoolyard.

     

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  • Why 'what works' doesn't: False positives in education research

    by Jay Lynch, PhD and Nathan Martin, Pearson

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    If edtech is to help improve education research it will need to kick a bad habit—focusing on whether or not an educational intervention ‘works’.

    Answering that question through null hypothesis significance testing (NHST), which explores whether an intervention or product has an effect on the average outcome, undermines the ability to make sustained progress in helping students learn. It provides little useful information and fails miserably as a method for accumulating knowledge about learning and teaching. For the sake of efficiency and learning gains, edtech companies need to understand the limits of this practice and adopt a more progressive research agenda that yields actionable data on which to build useful products.

    How does NHST look in action? A typical research question in education might be whether average test scores differ for students who use a new math game and those who don’t. Applying NHST, a researcher would assess whether a positive—i.e. non-zero—difference in scores is significant enough to conclude that the game has had an impact, or, in other words, that it ‘works’. Left unanswered is why and for whom.

    This approach pervades education research. It is reflected in the U.S. government-supported initiative to aggregate and evaluate educational research, aptly named the What Works Clearinghouse, and frequently serves as a litmus test for publication worthiness in education journals. Yet it has been subjected to scathing criticism almost since its inception, criticism that centers on two issues.

    False Positives And Other Pitfalls

    First, obtaining statistical evidence of an effect is shockingly easy in experimental research. One of the emerging realizations from the current crisis in psychology is that rather than serving as a responsible gatekeeper ensuring the trustworthiness of published findings, reliance on statistical significance has had the opposite effect of creating a literature filled with false positives, overestimated effect sizes, and grossly underpowered research designs.

    Assuming a proposed intervention involves students doing virtually anything more cognitively challenging than passively listening to lecturing-as-usual (the typical straw man control in education research), then a researcher is very likely to find a positive difference as long as the sample size is large enough. Showing that an educational intervention has a positive effect is quite a feeble hurdle to overcome. It isn’t at all shocking, therefore, that in education almost everything seems to work.

    But even if these methodological concerns with NHST were addressed, there is a second serious flaw undermining the NHST framework upon which most experimental educational research rests.

    Null hypothesis significance testing is an epistemic dead end. It obviates the need for researchers to put forward testable models of theories to predict and explain the effects that interventions have. In fact, the only hypothesis evaluated within the framework of NHST is a caricature, a hypothesis the researcher doesn’t believe—which is that an intervention has zero effect. A researcher’s own hypothesis is never directly tested. And yet with almost universal aplomb, education researchers falsely conclude that a rejection of the null hypothesis counts as strong evidence in favor of their preferred theory.

    As a result, NHST encourages and preserves hypotheses so vague, so lacking in predictive power and theoretical content, as to be nearly useless. As researchers in psychology are realizing, even well-regarded theories, ostensibly supported by hundreds of randomized controlled experiments, can start to evaporate under scrutiny because reliance on null hypothesis significance testing means a theory is never really tested at all. As long as educational research continues to rely on testing the null hypothesis of no difference as a universal foil for establishing whether an intervention or product ‘works,’ it will fail to improve our understanding of how to help students learn.

    As analysts Michael Horn and Julia Freeland have noted, this dominant paradigm of educational research is woefully incomplete and must change if we are going make progress in our understanding of how to help students learn:

    “An effective research agenda moves beyond merely identifying correlations of what works on average to articulate and test theories about how and why certain educational interventions work in different circumstances for different students.”

    Yet for academic researchers concerned primarily with producing publishable evidence of interventions that ‘work,’ the vapid nature of NHST has not been recognized as a serious issue. And because the NHST approach to educational research is relatively straightforward and safe to conduct (researchers have an excellent chance of getting the answer they want), a quick perusal of the efficacy pages at leading edtech companies shows that it holds as the dominant paradigm in edtech.

    Are there, however, reasons to think edtech companies might be incentivized to abandon the current NHST paradigm? We think there are.

    What About The Data You’re Not Capturing?

    Consider a product owner at an edtech company. Although evidence that an educational product has a positive effect is great for producing compelling marketing brochures, it provides little information regarding why a product works, how well it works in different circumstances, or really any guidance for how to make it more effective.

    • Are some product features useful and others not? Are some features actually detrimental to learners but masked by more effective elements?
    • Is the product more or less effective for different types of learners or levels of prior expertise?
    • What elements should be added, left alone or removed in future versions of the product?

    Testing whether a product works doesn’t provide answers to these questions. In fact, despite all the time, money, and resources spent conducting experimental research, a company actually learns very little about their product’s efficacy when evaluated using NHST. There is minimal ability to build on research of this sort. So product research becomes a game of efficacy roulette, with the company just hoping that findings show a positive effect each time it spins the NHST wheel. Companies truly committed to innovation and improving the effectiveness of their products should find this a very bitter pill to swallow.

    A Blueprint For Change

    We suggest edtech companies can vastly improve both their own product research as well as our understanding of how to help students learn by modifying their approach to research in several ways.

    • Recognize the limited information NHST can provide. As the primary statistical framework for moving our understanding of learning and teaching forward, it is misapplied because it ultimately tells us nothing that we actually want to know. Furthermore, it contributes to the proliferation of spurious findings in education by encouraging questionable research practices and the reporting of overestimated intervention effects.
    • Instead of relying on NHST, edtech researchers should focus on putting forward theoretically informed predictions and then designing experiments to test them against meaningful alternatives. Rather than rejecting the uninteresting hypothesis of “no-difference,” the primary goal of edtech research should be to improve our understanding of the impact that interventions have, and the best way to do this is to compare models that compete to describe observations that arise from experimentation.
    • Rather than dichotomous judgments about whether an intervention works on average, greater evaluative emphasis should be devoted to exploring the impact of interventions across subsets of students and conditions. No intervention works equally well for every student and it’s the creative and imaginative work of trying to understand why and where an intervention fails or succeeds that is most valuable.

    Returning to our original example, rather than relying on NHST to evaluate a math game, a company will learn more by trying to improve its estimates and measurements of important variables, looking beneath group mean differences to explore why the game worked better or worse for sub-groups of students, and directly testing competing theoretical mechanisms proposed to explain the game’s influence on learner achievement. It is in this way that practical, problem-solving tools will develop and evolve to improve the lives of all learners.

    This series is produced in partnership with Pearson. EdSurge originally published this article on February 12, 2017, and it was re-posted here with permission.

     

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  • Analysis: For ed tech that actually works, embrace the science of learning

    by Kristen DiCerbo, Aubrey Francisco, Bror Saxberg, Melina Uncapher

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    This is the second in a series of essays surrounding the EdTech Efficacy Research Symposium, a gathering of 275 researchers, teachers, entrepreneurs, professors, administrators, and philanthropists to discuss the role efficacy research should play in guiding the development and implementation of education technologies. This series was produced in partnership with Pearson, a co-sponsor of the symposium co-hosted by the University of Virginia’s Curry School of Education, Digital Promise, and the Jefferson Education Accelerator. Read the first piece here.

    As education technology gains an increasing presence in American schools, the big question being asked is, “Does it work?”

    But as curricula and learning tools are prepared for rigorous evaluation, we should think about how existing research on teaching and learning have informed their design. Building a movement around research and impact must include advocating for products based on learning research. Otherwise, we are essentially taking a “wait and hope” strategy to development: wait until we have something built and hope it works.

    When we make a meal, we want to at least have a theory about what each ingredient we include will contribute to the overall meal. How much salt do we put in to flavor it perfectly? When do we add it in? Similarly, when creating a curriculum or technology tool, we should be thinking about how each element impacts and optimizes overall learning. For example, how much and when do we add in a review of already-learned material to ensure memory retention? For this, we can turn to learning science as a guide.

    We know a lot about how people learn. Our understanding comes from fields as varied as cognitive and educational psychology, motivational psychology, neuroscience, behavioral economics, and computer science. There are research findings that have been replicated repeatedly across dozens of studies. If we want to create educational technology tools that ultimately demonstrate efficacy, these learning science findings should serve as the foundation, integrating the insights from decades of research into how people learn and how teachers teach into product design from the beginning.

    Existing research on learning

    So what do we know about how people learn? You could turn to foundational texts like Clark and Mayer’s e-Learning and the Science of Instruction, Dan Schwartz’s The ABCs of How We Learn, and Hattie and Yates’s Visible Learning for detail. Or you could look to the excellent summaries compiled by Deans for ImpactLearningScientists.org, and Digital Promise Global.

    Here are a few examples:

    Spaced practice: We know that extending practice over time is better than cramming all practice into the few days before an exam. Spaced practice strengthens information retention and keeps it fresh over time, interrupting the “forgetting curve.” Implementing spaced practice could be as simple as planning out review time. Technology can help implement spaced practice in at least two ways: 1) prompting students to make their own study calendars and 2) proactively presenting already-learned information for periodic review.

    Retrieval practice: What should that practice look like? Rather than rereading or reading and highlighting, we know it is better for students to actually retrieve the information from memory because retrieving the information actually changes the nature of the memory for the information. It strengthens and solidifies the learning, as well as provides more paths to access the learning when you need it. Learners creating flashcards have known about this strategy for a long time. RetrievalPractice.org offers useful information and helpful applications building on this important principle. There is a potential danger point here for designers not familiar with learning literature. Since multiple-choice activities are easier to score with technology, it is tempting to create these kinds of easy questions for retrieval practice. However, learning will be stronger if students practice freely recalling the information rather than simply recognizing the answer from choices.

    Elaboration: Taking new information and expanding on it, linking it to other known information and personal experience, is another way to improve memory for new concepts. Linking new information to information that is already known can make it easy to recall later. In addition, simply expanding on information and explaining it in different ways can make retrieval easier. One way to practice this is to take main ideas and ask how they work and why. Another method is to have students draw or fill in concept maps, visually linking ideas and experiences together. There are a number of online tools that have been developed for creating concept maps, and current research is focusing on how to provide automated feedback on them.

    So how many educational technology products actually incorporate these known practices? How do they encourage students to engage in these activities in a systematic way?

    Existing research on instructional use of technology

    There is also significant research about how technology supports teaching practices, which should inform how a product is designed to be used in the classroom.

    For example, there is a solid research base on how to design activities that introduce new material prior to formal instruction. It suggests that students should initially be given a relatively difficult, open-ended problem that they are asked to solve. Students, of course, tend to struggle with this activity, with almost no students able to generate the “correct” approach. However, the effort students spend in this activity has been shown to build a better foundation for future instruction to build on as students have a better understanding of the problem to be solved (e.g., Wiedmann, Leach, Rummel & Wiley, 2012 Belenky & Nokes-Malach, 2012. It is clearly important that this type of activity be presented to students as a chance to explore and that failure is accepted, expected, and encouraged. In contrast, an activity meant to be part of practice following direct instruction would likely include more step-by-step feedback and hints. So, if someone wants to design activities to be used prior to instruction, they might 1) select a fundamental idea from a lesson, 2) create multiple cases for which students must find an all-encompassing rule, and 3) situate those cases in an engaging scenario.

    Schwartz of Stanford University tested this idea with students learning about ratios — without telling them they were learning about ratios. Three cases with different ratios were created based on the number of objects in a space. This was translated into the number of clowns in different-sized vehicles, and students were asked to develop a “crowded clowns index” to measure how crowded the clowns are in the vehicles. Students are not specifically told about ratios, but must uncover that concept themselves.

    Product developers should consider research like this when designing their ed tech tools, as well as when they’re devising professional development programs for educators who will use those technologies in the classroom.

    Product makers must consider these questions when designing ed tech: Will the activity the technology facilitates be done before direct instruction? Will it be core instruction? Will it be used to review? How much professional development needs to be provided to teachers to ensure the fidelity of implementation at scale?

    Too often, designers think there is a singular answer to this series of questions: “Yes.” But in trying to be everything, we are likely to end up being nothing. Existing research on instructional uses of technology can help developers choose the best approach and design for effective implementation.

    Going forward

    With this research as foundation, though, we still have to cook the dish and taste it. Ultimately, applying learning science at scale to real-world learning situations is an engineering activity. It may require repeated iterations and ongoing measurement to get the mix of ingredients “just right” for a given audience, or a given challenging learning outcome. We need to make sure to carefully understand and tweak our learning environments, using good piloting techniques to find out both whether our learners and teachers can actually execute what we intend as we intended it (Is the learning intervention usable? Are teachers and students able to implement it as intended?), and whether the intervention gives us the learning benefits we hoped for (effectiveness).

    The key is that research should be informing development from the very beginning of an idea for a product, and an evidence-based “learning engineering” orientation should continue to be used to monitor and iterate changes to optimize impact. If we are building from a foundation of research, we are greatly increasing the probability that, when we get to those iterated and controlled trials after the product is created, we will in fact see improvements over time in learning outcomes.

    Follow the conversation on social media with the hashtag #ShowTheEvidence.

    Authors:

    • Kristen DiCerbo, Vice President, Education Research, Pearson
    • Aubrey Francisco, Chief Research Officer, Digital Promise
    • Bror Saxberg, Chief Learning Officer, Kaplan
    • Melina Uncapher, Assistant Professor, Department of Neurology, UC San Francisco

    This series is produced in partnership with Pearson. The 74 originally published this article on June 5, 2017, and it was re-posted here with permission.

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  • #ShowTheEvidence: Building a movement around research, impact in ed tech

    by Aubrey Francisco, Bart Epstein, Gunnar Counselman, Katrina Stevens, Luyen Chou, Mahnaz Charania, Mark Grovic, Rahim Rajan, Robert Pianta, Rebecca Griffiths

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    This is the first in a series of essays surrounding the EdTech Efficacy Research Symposium, a gathering of 275 researchers, teachers, entrepreneurs, professors, administrators, and philanthropists to discuss the role efficacy research should play in guiding the development and implementation of education technologies. This series was produced in partnership with Pearson, a co-sponsor of the symposium co-hosted by the University of Virginia’s Curry School of Education, Digital Promise, and the Jefferson Education Accelerator.

    To improve education in America, we must improve how we develop and use education technology.

    Teachers and students are increasingly using digital tools and platforms to support learning inside and outside the classroom every day. There are 3.6 million teachers using ed tech, and approximately one in four college students take online courses — four times as many as a decade earlier. Technology will impact the 74 million children currently under the age of 18 as they progress through the pre-K–12 education system. The key question is: What can we do to make sure that the education technology being developed and deployed today fits the needs of 21st-century learners?

    Our teachers and students deserve high-quality tools that provide evidence of student learning, and that provide the right kind of evidence — evidence that can tell us whether the tool is influencing the intended learning outcomes.

    Evidence and efficacy can no longer be someone else’s problem to be solved at some uncertain point in the future. The stakes are too high. We all have a role to play in ensuring that the money spent in ed tech (estimated at $13.2 billion in 2016 for K-12) lives up to the promise of enabling more educators, schools, and colleges to genuinely improve outcomes for students and help close persistent equity gaps.

    Still, education is complex. Regardless of the quality of a learning tool, there will be no singular, foolproof ed tech solution that will work for every student and teacher across the nation. Context matters. Implementation matters. Technology will always only be one element of an instructional intervention, which will also include instructor practices, student experiences, and multiple other contextual factors.

    Figuring out what actually works and why it works requires intentional planning, dedicated professional development, thoughtful implementation, and appropriate evaluation. This all occurs within a context of inconsistent and shifting incentives and, in the U.S., involves a particularly complex ecosystem of stakeholders. And unfortunately, despite the deep and vested interest of improving the system, the current ecosystem is many times better at supporting the status quo than introducing a potentially better-suited learning tool.

    That’s the challenge to be taken up by the EdTech Efficacy Research Symposium in Washington, D.C., this week, and the work underway as part of the initiative convened by the University of Virginia’s Curry School of Education, Digital Promise, and the Jefferson Education Accelerator. People like us rarely have the opportunity to collaborate, but this issue is too important to go it alone.

    Over the past six months, 10 working groups consisting of approximately 150 people spent valuable hours together learning about the challenges associated with improving efficacy and exploring opportunities to address these challenges. We’ve looked at issues such as how ed tech decisions are made in K-12 and higher education, what philanthropy can do to encourage more evidence-based decision-making, as well as what will be necessary to make the focus on efficacy and transparency of outcomes core to how ed tech companies operate.

    Over the next six weeks, we’ll explore these themes here, sharing findings and recommendations from the working groups. Our hope is to stimulate not just discussion but also practical action and concrete progress.

    Action and progress might look like new ways to use research in decision-making such as informational site Evidence for ESSA or tools that make it easier for education researchers to connect with teachers, districts, and ed tech companies, like the forthcoming National Education Researcher Database. Collaboration is critical to improving how we use research in ed tech, but it’s not easy. Building a common framework takes time. Acting on that framework is harder.

    So, as a starting point, here are three broader issues that we’ve learned about efficacy and evidence from our work so far.

    Everyone wants research and implementation analysis done, but nobody wants to pay more for it

    We know it’s not realistic to expect that the adoption of each ed tech product or curricular innovation will be backed up by a randomized control trial.

    Investors are reticent to fund these studies, while schools or developers rarely want to pick up the price tag for expensive studies. When Richard Culatta and Katrina Stevens were still at the U.S. Department of Education’s Office of Educational Technology, they pointed out that “it wouldn’t be economically feasible for most app creators (or schools) to spend $250k (a low price tag for traditional educational research) to evaluate the effectiveness of an app that only cost a total of $50k to build.”

    We could spend more efficiently, leveraging the 15,000 tiny pilots and decisions underway into new work and new insights without spending more money. This could look like a few well-designed initiatives to gather and share relevant information about implementations and efficacy. Critically, we’ll need to find a sustainability model for that type of rigorous evaluation to ensure this becomes a key feature in how adoption decisions are made.

    We need to recognize that evidence exists on a continuum

    Different types of evidence can support different purposes. What is important is that each decision is supported by an appropriate level of evidence. This guide by Mathematica provides a useful reference for educators on different evidence types and how they should be viewed. For educators, it would be wise to look at the scale and cost of the decision and determine the appropriate type of evidence.

    Tools like the Ed Tech Rapid Cycle Evaluation CoachLearn Platform, and Edustar can provide useful support in making decisions and evaluating the use of technology.

    It’s important to remember that researchers and philanthropists may use education research for different purposes than would a college, university system, or districts. Academic researchers may be looking to identify causal connections, learning gains, or retention rates, while a district is often focused on a specific context and implementation (what works for schools similar to mine).

    When possible, traditional randomized control trials provide useful information, but they’re often not affordable, feasible, or even necessarily appropriate. For example, many districts, schools, or colleges are not accustomed to or well versed in undertaking this type of research themselves.

    It’s easy to blame other actors for the current lack of evidence-driven decisions in education

    Everyone we spoke to agrees that decisions about ed tech should be made on the basis of merit and fit, not marketing or spin. But nearly everyone thinks that this problem is caused by other actors in the ecosystem, and this means that progress here will require hard work and coordination.

    For example, investors often don’t screen their investments for efficacy, nor do they promote their portfolio companies to necessarily undertake sufficient research. Not surprisingly, this tends to be because such research is costly and doesn’t necessarily drive market growth. It’s also because market demand is not driven by evidence. It’s simply not the case that selection choices for tools or technologies are most often driven by learning impact or efficacy research. That may be shifting slowly, but much more needs to be done.

    Entrepreneurs and organizations whose products are of the highest quality are frustrated that schools are too often swayed by their competitors’ flashy sales tactics. Researchers feel that their work is underappreciated and underutilized. Educators feel overwhelmed by volume and claims, and are frustrated by a lack of independent information and professional support. We have multiple moving pieces that must be brought together in order to improve our system.

    Ensuring that ed tech investments truly help close achievement gaps and expand student opportunity will require engagement and commitments from a disparate group of stakeholders to help invent a new normal so that our collective progress is directional and meaningful. To make progress on this, we must bring the conversation of efficacy and the use of evidence to center stage.

    That’s what we’re hoping to help continue with this symposium. We’ve learned much, but we know that the journey is just beginning. We can’t do it alone. Feel free to follow and join the conversation on Twitter with #ShowTheEvidence.


    Authors:

    • Aubrey Francisco, Chief Research Officer, Digital Promise
    • Bart Epstein, Founding CEO, Jefferson Education Accelerator
    • Gunnar Counselman, Chief Executive Officer, Fidelis Education
    • Katrina Stevens, former Deputy Director, Office of Educational Technology, U.S. Department of Education
    • Luyen Chou, Chief Product Officer, Pearson
    • Mahnaz Charania, Director, Strategic Planning and Evaluation, Fulton County Schools, Georgia
    • Mark Grovic, Co-Founder and General Partner, New Markets Venture Partners
    • Rahim Rajan, Senior Program Officer, Bill & Melinda Gates Foundation
    • Robert Pianta, Dean, University of Virginia Curry School of Education
    • Rebecca Griffiths, Senior Researcher, Center for Technology in Learning, SRI International

    This series is produced in partnership with Pearson. The 74 originally published this article on May 1, 2017, and it was re-posted here with permission.

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