Rex, Prithvi, and Shreya (Photo by Josh Robinson)

Dvora and Adam (Photo by Josh Robinson)

Knewton welcomed five new Knerds this month.

Rex Gibson is a Senior Engineer on the Analytics team. Rex is originally from Nebraska, attended Webster University in St. Louis, and has been living in New York for the past 12 years. Before joining Knewton, he was the Data Lead at opensky.com. Rex’s favorite book is Dune (“quite a remarkable insight into the world’s geopolitical landscape”). In his free time, he enjoys music — he’s a trained music producer and audio engineer (he once worked pro in NYC) and also has a passion for designing musical instruments for surround sound. His all-time favorite travel destination is Wind River Range in Wyoming.

Dvora Inwood joins Knewton as the Content Curation Manager. Originally from Los Angeles, Dvora received her BA from Harvard and her MA in Education from Stanford. In the past, Dvora ran a charter school network in LA and designed innovative schools in NY. Dvora says that in her free time she “used to rock climb, play bridge and Celebrity, and camp. Now, I just work, play Scramble with Friends, and enjoy my kids.” Like Rex, the best place Dvora has ever traveled to is Wyoming. This is her favorite Wikipedia page.

Originally from New City, NY, Adam Granat joins Knewton as an Accountant on the Finance team. Adam graduated from Tulane University with a BS in Finance and an MS in Accounting, and previously worked at Grant Thornton Financial Services Group. Outside of work, Adam sits on the board of a charity called Mission: Restore. Game of Thrones is Adam’s favorite book and TV show. His favorite travel destination is Punta Cana, Dominican Republic.

Prithvi Raj joins the API team as a Software Engineer. Originally from India, Prithvi graduated from the University of Florida with an MS in Computer Engineering. His favorite movie is Lord of the Rings and his favorite TV show is Friends. In his free time, he enjoys photography, cooking, music, and exploring places. He likes this Wikipedia page a lot. Though almost no one agrees with him, Prithvi maintains that a person can survive briefly in space unprotected.

Shreya Amin is Knewton’s newest Adaptive Instruction Analyst. She graduated from the University of Chicago, and before coming to Knewton was completing an analyst fellowship in the education sector. Her current favorite books are Childhood’s End, Hitchhiker’s Guide to the Galaxy, End of Eternity, Demian, and The Plague. These days Shreya enjoys reading Russell and Madulin, eating diverse cuisions, and trying every nearby brewery she can find. This is her favorite Wikipedia page.

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In 2006, my old Harvard Business School classmate Andy Jassy realized that all computing would ultimately move to the cloud. A senior manager at Amazon, he got approval from Jeff Bezos to launch Amazon Web Services, which is today the leader in cloud computing infrastructure. Startups, and even many big companies like Netflix, outsource their hosting infrastructure to AWS.

Knewton’s goal is to be like AWS for education. We’ve created a shared data infrastructure platform that makes it fast and easy for anyone to build extremely powerful adaptive learning applications with Knewton. As our platform gets stronger over time, with more features and more data, every product built using our platform automatically gets stronger too.

Despite our constant protestations to the contrary, observers often confuse Knewton with the many adaptive learning app makers who are now popping up. Or they confuse app makers with platforms. Or they think we’re all competitors.

In fact, it is Knewton’s mission to help all these adaptive learning app makers.

Due probably to Google and Facebook, it’s become fashionable in tech circles to describe oneself as a platform despite the word’s actual meaning. To be a platform simply means that one’s technology is not an end-to-end solution but instead powers other applications and businesses. There’s nothing innately glamorous about platforms. Most platforms are largely anonymous and pretty boring.

There will soon be lots of wonderful adaptive learning apps: adaptive quizzing apps, flashcard apps, textbook apps, simulation apps — if you can imagine it, someone will make it. In a few years, every education app will be adaptive. Everyone will be an adaptive learning app maker.

Knewton doesn’t create these apps — we work with partners to help them create their own. We create no content, nor do we claim unique expertise in instructional design, cognitive science, or pedagogical approach. But we do help make everything from your content to your pedagogy better, by optimizing them with deep student proficiency data.

Knewton isn’t even, ultimately, an adaptive learning platform. Adaptive learning is merely one (completely awesome) feature that can be done with our platform. Knewton is an infrastructure platform that consolidates data science, statistics, psychometrics, content graphing, and tagging in one place, and allows for the consolidation and pooling of student proficiency data.

This infrastructure unlocks for the first time the vast quantities of data that students have always produced — data that make adaptive apps exponentially more powerful. The infrastructure is also extremely complex and expensive. Sure — it’s straightforward enough to wire up a simple, self-contained adaptive app, based on a pre-determined, limited decision-tree. But how much better would that app be if it contained an effectively unlimited amount of back-end content? If all of its assessment items had been algorithmically “normed” so that they resulted in exact concept proficiency data for each student? Or if the app pre-acted to the learning modalities of each student? Or if it “started hot” so that from Day 1 of a student taking a new course, all her prior concept proficiencies and learning styles had been preloaded?

Knewton makes possible all these things and more. Today, Knewton functionality includes pinpoint student proficiency measurement, content efficacy measurement (yes, we can tell you how effective your content is), student engagement optimization, atomic-concept adaptive learning, and concept-level analytics. Next year we’re adding “adaptive tutoring,” which combines the wisdom of crowds with Knewton’s network to find the perfect people online right now to give you real-time help.

We also provide scalability, distribution (if you have a great app, we’ll promote it to our partners), and network effects (the combined power of all the data helps each student learn each concept). And we do it without storing any personally identifiable information (“PII”) unless a student wants us to have it.

All of this stuff is so costly and complicated that no one has ever, or would ever, try to build it all just to power one app. It would be like automobile manufacturers feeling like they also had to build and maintain all the nation’s highways. Knewton can accomplish it only by amortizing, over every app we support, the extraordinary burden of creating these features.

Besides, student proficiency data are much stronger together than apart. A closed, isolated app can by definition never have more than a fraction of the proficiency data that Knewton’s open platform has. Even that assumes that the app contains “normed” assessment items (which Knewton does for free); otherwise it generates no proficiency data at all.

To make all this stuff work requires hundreds of millions in financial capital and an unreal degree of human capital. Knewton is lucky enough to attract top data scientists, psychometricians, and software engineers from around the world — people who choose Knewton over offers from incredible companies like Google, Palantir, and top Wall Street firms.

It’s been a struggle for us to get where we are today. Until recently, only large learning companies and university systems could use the Knewton platform. But now our enterprise API is flexible enough for a much wider audience. We’re happy to partner with anybody — even so-called “competitors.” We can’t quite say “yes” to everyone who wants to work with us yet, but our capacity is growing by leaps and bounds every day. We just issued our 200 millionth recommendation (suggesting the optimal next bit of real-time content for a student) and will be into the billions by the end of the year.

Ultimately, all learning materials will be digital and they will all be adaptive. Big companies, start-ups, schools, and individual teachers will make them. We hope to enable them, and can’t wait to see the amazing things people create. AWS made life a lot easier for everyone else on the Internet. If Knewton can accomplish something similar for education, we’ll feel like we did a pretty useful thing.

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From L-R: Andrew, Erion, Illya, Jon, David, Dan, and Giannis

Seven new people joined the Knewton team this month. We’re excited to have them all here.

Illya Bomash is a Data Analyst on the Adaptive Learning team. Illya was born in Russia but has been living in NYC for the past eight years. He went to college at Harvard, then did a Ph.D. at Weill Cornell Medical College in computational neuroscience, studying how cells in the retina represent visual inputs. In his spare time, Illya enjoys “going to MoMa and wishing I were an artist, biking around NYC and taking photos, and cooking a steady stream of CSA vegetables.” Tokyo and Kyoto top Illya’s list of travel destinations.

Jon Tascher is a Web Developer on the Marketing team. Originally from New York, Jon graduated from Vassar College. Before coming to Knewton, he was a freelance web developer. In his free time, he enjoys reading — his favorite books are Catch 22 and Stranger in a Strange Land. Though few people tend to agree with him, Jon maintains that the BeeGees are one of the greatest bands in the history of music. He says his high school yearbook should have named him “Most likely to have forgotten homework, books, pen, binder, etc.” The Dancing Plague of 1518 is his favorite Wikipedia article.

Giannis Neokleous is a Software Engineer on the Adaptive Learning team. Originally from Cyprus, Giannis earned a BSc in Computer Science from Stony Brook University and an MS in Computer Science from Stanford University. Before he came to Knewton, he was “collecting and processing millions of people’s opinions online (in a non-creepy way).” Giannis enjoys biking, travelling, and music (“most variations of metal”) in his free time. His favorite place he’s traveled to is Venice.

David Hartunian is a Software Engineer originally from Belmont, MA. He graduated from Carnegie Mellon University and has spent the last five years working in finance on algorithmic investment strategies. His favorite TV show is Battlestar Galactica, and his hobbies include strategy board games and cooking. Timeline of the Far Future is his favorite Wikipedia article. Though few people agree with him, David maintains that getting up early is better than staying up late.

Erion Hasanbelliu is a Software Engineer on the Adaptive Learning team. He’s from “the SOUTH” (emphasis his) and attended Jacksonville State University and the University of Florida, where he earned his PhD in Electrical and Computer Engineering. His research work focused primarily on Machine Learning and Computer Vision. Erion’s hobbies include anything outdoorsy, and his favorite travel destination is Greece. You can find him at erionhasanbelliu.com.

Dan Risch is a Technical Recruiter. Originally from Milwaukee, Dan graduated from the University of Wisconsin-Milwaukee. Before coming to Knewton, he was recruiting for tech startups through an agency. His favorite movie is The Sandlot, and in his free time, he enjoys golf, hunting, fishing, camping, and baseball (“only spectating at this point”). Scuba diving in Hawaii or the Cayman Islands tops his list of favorite vacations.

Andrew Love is the Client Services Manager at Knewton. Originally from Los Angeles, Andrew previously worked as a business technology consultant, “helping small and medium-sized businesses plan and implement new products and services.” Andrew’s biggest hobby is product design — “I’m almost always cooking up a software or hardware concept in my free time.” Andrew’s high school yearbook should named him “Most likely to sell everything and live out of a backpack.” His favorite Wikipedia article is about “the story of Marvin Heemeyer and the Killdozer… The story has no happy ending, but still; bulldozer rampage!”

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Early efficacy reports reflect the success of Knewton Math Readiness at ASU. After four semesters of use with over 5,000 developmental math students at Arizona State University, withdrawal rates dropped by 58% and pass rates went from 64% to 78%.

Below is a visualization that showcases students going through one semester of Knewton Math Readiness at Arizona State University.

The colors reflect the subject area within the course (equations and expressions, statistics and probability, algebra, functions, ratios & proportions, geometry, the number system) and each tiny white dot represents a different student moving through the course. The smaller circles within the larger colored circles reflect lesson components within the subject areas. As more students move through this subject material and gain proficiency, the circles are gradually shaded in. The timeline at the bottom of the visualization reflects the length of the entire semester; every second in the video is equivalent to 20 hours.

To learn more about the science behind Knewton Adaptive Learning, check out our white paper.

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They spend six or more hours in bricks-and-mortar classrooms each day, listening to teachers, talking with peers, and working with textbooks/software/technology (collectively, “materials”). Then they spend a few more hours working through materials after school. Some students learn more in the classroom environment; others learn more by using materials to teach themselves.

Despite how huge and complex the education system is, students do primarily just those two things: attend classes and work with materials.

For all kinds of reasons I won’t go into here, it’s very difficult to innovate — especially at scale — the bricks-and-mortar classrooms side of the system. But it’s eminently possible to innovate the materials side. In fact, we’re currently in an innovation boom.

Just as this innovation boom has begun to proliferate, though, materials creators have received growing criticism over the price of their products. People have long complained about textbook prices, but over the last few years it has been increasingly argued in some quarters that the high costs of education ought to be addressed by lowering materials costs.

But materials are only around 1 or 2 percent of global education expenditure. Bricks-and-mortar classrooms — and all the costs associated with operating them — make up the rest.

We would all like education to be more affordable, but focusing on reducing materials cost is pointless. It’s far too small an expenditure. In fact, it’s worse than pointless — it’s dangerous. It would deter investment in innovation in exactly that part of the education ecosystem where much of the innovation at scale is occurring.

Why, then, do some people insist on lowering systemic costs by focusing on materials? Perhaps they are unaware of the enormous skew in relative cost between these two sides of the education system. Perhaps they think the cost of bricks-and-mortar classrooms cannot possibly be changed (if so, keep an eye on Khan Academy, MOOCs, and online courses). Perhaps they reflexively dislike materials since their providers tend to be for-profit entities. Or they may do so for their own perceived self-interest, opposing innovation in order to protect the status quo.

On an ROI basis, the materials industry is fantastically productive. It produces or facilitates an extraordinary percentage of learning, yet accounts for only 1 percent or so of the cost. Materials creators operate efficient and relatively low-margin businesses. And with digitally delivered products, price per unit will likely decline (after a period of intense investment over the next few years).

The best way to lower systemic costs in education is to average down the cost of bricks-and-mortar classrooms with digital products and courses — exactly what the materials industry is focused on right now. The world’s major learning companies are now or will soon be making more revenue from technology and services than from printed textbooks. Online courses will, over time, tend to reduce the systemic costs of bricks-and-mortar classrooms. This will be true even in primary and secondary education, which tend to be very cost-efficient. It will be even more so in higher ed, which is not as efficient and, more importantly, is priced on the basis of scarcity rather than operating cost.

If anyone is hoping to see materials diminish as a percentage of total education expenditure, the next couple of decades are likely to be disappointing. Assuming a significant increase in technology-driven education, materials will instead increase its overall share of the global education system while tending to lower the overall cost for each student. For those of us who would like to see dramatic innovation in education, that is a very good thing.

This article originally appeared in the March issue of Knerd Dispatch, the Knewton newsletter. Subscribe here.

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Within the tech industry alone, edtech, adtech, healthtech, cleantech, and publishing tech all have their respective groups and subgroups. Some groups like the famously large NY Tech Meetup (30,000+ members) have even given rise to meetup groups that specialize in getting together before and after the larger meetup, so that group members can network and bond in a more intimate setting. The phenomenon is clearly a reflection of the strength of the NY tech community, which now rivals that of Silicon Valley.

As someone who’s been following the immersive media and “deep media” trends, I think the explosion of the New York meetup scene is also a reflection of a larger trend in society focused around the importance of the physical, sensory, and immediate. According to JWT Intelligence, a global trendspotting firm, the more time we spend in the digital world, the more we value time not spent in front of a screen. The more we digitize our lives, the more intensity we seek from our physical experiences. The more productive we are as a result of all this new technology, the more we want to feel and connect as well.

Take a look around you, and you’ll see evidence of this trend. On the home and lifestyle front, there’s the sudden ubiquity of design, which reflects a new desire to find beauty and meaning in the everyday and in one’s immediate environment (Fab.com started off as a gay design site until it became clear there was much broader appeal). Consider, also, the craftsmanship movement fueled by the rise of Etsy, as well as the Do-It-Yourself ethos rippling across every industry from food to education.

Everywhere you look, you’ll find romanticization and fetishization of the physical and sensory. With flash sales saturating the market, sites like Thrillist which offer curated site-exclusive experiences are all the rage as companies look to differentiate themselves by offering rare and distinct experiences on top of good deals. Outrageous, uniquely packaged experiences can now be bought on sites like Gilt (you can drive around in a luxury car, with paparazzi racing behind you), leading me to think that the next big thing will be day- and weekend-long immersive events, fashioned for increasingly specific and discriminating tastes — think interactive theater, mystery dinners, and spartan mud runs kicked up a level.

As the “age of abundance” ushered in by the advent of big data brings basic goods and services to more and more, the fashionable and trendy will search for the rare and exclusive in crafted experiences they can “collect” and showcase (social energy which companies like Facebook and Pinterest will appreciate and encourage). Some pundits like William Bainbridge even say that LARPing (live action role play) and all its varieties may become mainstream: “With ubiquitous Internet connectivity, and mobile access through cellphones or PDAs, it is possible this genre could grow into a major industry, possibly integrated with traditional activities such as history-oriented tourism, nature trail hiking, or stock market trading.”

These trends and movements should allay some widespread fears about the concern with efficiency taking over our lives. Human beings are emotional and physical creatures who will seek to feel and connect no matter what — and who will use new technology to feel and connect in increasingly sophisticated ways.

All these cultural shifts have become so pronounced that renowned theorist, Daniel Pink goes as far to say in his fascinating book, A Whole New Mind, that right-brained people will rule the future. With the amount of knowledge and content proliferating (there have never been as many books, articles, sites, magazines, and blogs in existence), curation, aggregation, commentary, personalization, and context will start generating serious value since people will need some way to digest, absorb, and process the flood of content out there.

Back to meetups. Curious? Don’t know where to start? Check out the monthly meetups hosted by Knewton in our office near Union Square (our livestream provides a sense of what goes down during these happenings). A rundown of some of our recent events:

• NY Open Statistical Programming Meetup: Advanced Sports Analytics in Broadcasting
• NY Software Engineers Meetup: Machine Learning + NuoDB
• NY Tech Women Meetup: Birthday/Holiday Party
• NYEdTech Meetup: I Like It When You Call me “Big Data”
• NY Open Statistical Programming Meetup: SciDB as a Data Backend for R
• NYEdTech Meetup: Education APIs: We’re Gonna Need a Bigger Boat
• NY Software Engineers Meetup: Big Data | Machine Learning | RoR
• NYEdTech Meetup: Spring EdTech Showcase

Check out our events calendar for the most up to date information on events. Mark your calendar! Next NYEdTech Meetup we’re hosting is April 16, 2013.

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Here’s an example of how this works. Suppose that a student at the beginning of her first Knewton-enhanced course is struggling with a word problem which involves calculating the area of a triangle. Assume we know nothing about the student aside from this fact. (This is an uncommon scenario — students who have made any progress in a Knewton-enhanced course or who have taken previous Knewton-enhanced courses will have already generated proficiency data that can help inform recommendations.)  Knewton must determine why the student is getting this exercise wrong, so that we can recommend content that helps her learn the skills and concepts required to solve this problem.

Specifically, we must ask the following question: what is preventing the student from solving the triangle problem? There are several possibilities. It may be the case that she doesn’t know how to calculate the area of a triangle. Perhaps she struggles to read and interpret word problems. Maybe the base and the height of the triangle are given as decimals and she doesn’t know how to multiply decimals. There is even the possibility that she doesn’t know how to multiply integers!

It might also be the case that she can find the area of a hundred triangles with her eyes closed while she taps her head, rubs her belly and hops on one foot, and that she’s simply distracted by a computer game that she’s toggling to and from as her teacher wanders in and out of eyeshot of her computer screen. This last possibility is an important one. However, for the purposes of this example, let’s assume that the student is engaged and that her difficulty stems from the fact that she doesn’t understand one or more of the skills or concepts I described above. As you’ll recall if you’ve read one of our posts on knowledge graphs or checked out our white paper, we refer to these concepts as prerequisites.

Let’s list the prerequisites for the triangle word problem again:

1. Multiply integers
2. Multiply decimals
3. Calculate the area of a triangle
4. Read and interpret word problems

Using circles to represent the concepts and arrows to represent the prerequisite relationships, we can draw a diagram:

After we’ve identified the prerequisites, we must then assess the student’s proficiency in these areas so that we can recommend content that helps her learn the necessary concepts to solve the triangle word problem. For example, if she does poorly in assessments on calculating the area of a triangle, we can recommend additional content that helps her master this concept. But where do we start? Should we start by giving her an assessment on prerequisite 3 (Calculate the area of a triangle), or should we start with something more basic, like prerequisite 1 (Multiply integers)?

As you ponder this question, you might notice that the prerequisites in this example are not necessarily independent of one another. For example, it is unlikely that the student can multiply decimals if she cannot multiply integers. In fact, the content in this course that is associated with multiplying decimals expects and assumes that the student is able to multiply integers. In other words, 1 is a prerequisite for 2! Furthermore, prerequisite 1 (Multiply integers) is only important to the triangle problem as it relates to prerequisite 2 (Multiply decimals). In this case, we say that prerequisite 2 subsumes prerequisite 1 because it transmits the knowledge from 1 that is required to solve the triangle problem.

We can adjust our diagram to reflect this as follows:

 How does this observation about the relationship between prerequisites 1 and 2 help us determine what the student does and does not know? Let’s imagine that we give the student an assessment on prerequisite 1 (Multiply integers) and she aces it. All we can say is that she’s proficient in prerequisite 1. However, what if we give her an assessment on prerequisite 2 (Multiply decimals) and she aces that? Since we know that the assessments for prerequisite 2 expect and assume that the student is proficient in prerequisite 1, then based on her performance in prerequisite 2, we can be fairly confident that she is proficient in both 1 and 2. Conversely, if she fails prerequisite 1 (Multiply integers), it’s probably safe to say that she is not proficient in prerequisite 2 (Multiply decimals) either. In other words, we can estimate her proficiency in certain concepts without having to directly assess her on them.

There is also the case that the student knows how to multiply integers but does not know how to multiply decimals. We can only determine this by assessing her on both concepts, and therefore, the subsumption relationship does not help us in this scenario. We can, however, use information about how similar students performed in the past to help us identify this scenario. (In a future blog post, we’ll expand on network effects, or how we utilize data about other students’ activity to inform the recommendations we generate for each individual student.)

The example above involves just a few concepts, but for a typical Knewton-enhanced course, we map out the relationships between hundreds of concepts. Knowing what concepts subsume other concepts allows us to eliminate concepts that we think students are already proficient in and more quickly hone in on what they should study to meet their goals. It’s like when the blocks disappear in Tetris!

To summarize: rather than assessing a student on every single prerequisite concept, we can use our understanding of the content  — specifically, the relationships that exist between the concepts — to make intelligent inferences about what the student does and does not know. As a result, we can generate recommendations that lead to a more efficient use of the student’s time and energy.

In this post, I’ve described one way that we use our understanding of content to make learning more efficient and effective for our students. In a future blog post, I’ll talk about how we use goals that are defined by students and instructors to help us generate better recommendations.

For more from the Knewton Adaptive Instruction team, check out Jesse Sternberg’s post on the cross-disciplinary approach of the Knewton knowledge graph and Matt Busick’s post on the power of a knowledge graph.

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Regardless of industry, innovation is by its nature nearly always incremental. Tectonic innovation is extremely rare. It is only by adding myriad small innovations that most industries see steady product improvement over time. But, until recently, education has been one of the world’s most difficult industries to measure and, of course, it is especially difficult to measure small changes. Education is also one of the most expensive industries per capita. Especially in so huge and crucial an industry, there has never been and never will be any appetite for widespread adoption of small improvements with little or no data to recommend them.

Historically, this has stifled innovation — both from within the industry and from outside entrepreneurs. Last week, I was fortunate enough to sit down with a small working group of education entrepreneurs and policy makers in DC to discuss ways to promote positive change. It was organized by AOL Co-Founder and Revolution CEO Steve Case. In addition to U.S. Secretary of Education Arne Duncan, Under Secretary Martha Kanter, and Asst. Deputy Secretary Jim Shelton, the group included 2U’s Jeremy Johnson, StraighterLine’s Burck Smith, Chegg’s Ann Dwane, Echo360’s Fred Singer, Blackboard founder Michael Chasen, Knewton COO David Liu, Ujjwal Gupta from BenchPrep, and Donna Harris from Startup America.

During the conversation, I advocated strongly for the government to play a prominent role in encouraging institutions at all levels to expand their credit acceptance policies to accept — without friction — online courses from any other legitimate school. I am convinced that no other one policy would do more to accelerate education innovation.

Right now, nearly all higher education and K-12 schools will readily accept credits from in-person courses that their students have taken at other schools. As the number of online courses from accredited schools continues to grow, it only makes sense that schools accept credits from these courses as well.

I believe that widespread credit acceptance for reputable online courses is in fact inevitable within a decade. But who will get there first? The schools and colleges (and even nations) that do will have a long-term advantage over those who wait. If we encourage schools to start accepting these credits right now, without onerous terms and conditions, we will encourage more schools to produce high-quality online courses — which will encourage more students to take them and more innovators to power them, with ever better features.

So let’s first play out what effects this credit acceptance would have, and then look at how best to implement it.

Students would have access to an exponentially greater number of courses, in subjects they wouldn’t encounter otherwise. A community college student could experiment with courses in subjects her school doesn’t offer, or from four-year schools she’s interested in attending. An ambitious high-schooler lacking local A.P. course offerings could take them online. Elementary school kids would have a huge selection of language courses to choose from (currently, many elementary schools do not offer any on-ground language classes, even though this is the best time for kids to start a second language). A student who, due to her unique developmental path, should be in higher-level or lower-level courses than the rest of her cohort, would be able to do so online without stigma. Home-schooled kids, kids with special needs, kids who just want to do a little extra — all would have a huge online library of classes to choose from.

Schools offering online courses in what they teach best will earn high margin revenue they can put to good use in their on-ground operations. Schools buying those courses will provide increased academic opportunities for their students at relatively low cost. In some cases, those schools could charge the provider school a processing fee and turn the acceptance of online credits into a modest profit center. (Or perhaps they could charge parents and/or receive state financing assistance.) The schools accepting these credits would also free up additional capacity on campus. This would benefit all types of schools: colleges and private schools could accept more on-ground students and generate much more tuition, while public schools could reduce classroom-overcrowding problems. (In New York City, these problems have reached such proportions that students are commonly denied entrance at their neighborhood public school and bussed across town instead.)

This new credit “marketplace” would also solve many of the problems that currently impede innovators. For one thing, it would significantly improve measurability. Online courses are possible to data mine in a way that on-ground courses are not. Knewton can passively measure a student’s concept proficiency down to the percentile. We can also measure a student’s engagement, and see how it directly ties to learning. Other education technologies offer additional data and many other services. With hard student outcomes data, online learning technologies that work will spread quickly, as useful technologies do in every other Internet industry.

Another benefit of such an ecosystem is that it would consolidate activity and buying power. Right now, entrepreneurs are so daunted by selling to schools that many are dissuaded from entering the industry altogether. Of those who try, many cannot raise capital from leery investors, or find that their distribution model (selling to one school at a time, long sales cycles, many stakeholders with veto power…) is impossible to scale. An ecosystem in which the best ideas can be measured and easily distributed would also help the natural entrepreneurs already inside the system — teachers! — whose good ideas and best practices would spread.

The most important concern in promoting an ecosystem of ready online credit acceptance is maintaining quality standards for every online course. So let’s do it in stages. Public schools should accept online credits from other public schools. Colleges should accept them from other accredited colleges. The standard ought to be that if the school would accept another school’s credit from a transferring student, it ought to accept their online credit as well — or at least have a very good reason not to.

Perhaps the U.S. Department of Education could kick things off with a “Master List” of schools from which they encourage other schools to accept credits. It would be more bully pulpit than hard policy, but it’s a start. If desired, schools could at first accept the credit only — not the grade — to guard against students choosing online courses to game their GPA. Schools producing inferior online courses could be removed from the list if necessary — though negative ratings from other schools that had tried their classes would be a better policing mechanism. Eventually, non-traditional schools and for-profit companies could be added to the Master List, but only if they were able to demonstrate comparable quality (perhaps via exit exams).

As the educational landscape continues to evolve rapidly, it’s incumbent upon all of us — policy makers, entrepreneurs, teachers, administrators, students, and other stakeholders — to support the good while minimizing the bad. Encouraging widespread credit acceptance of online courses from reputable schools is the most immediate, and least disruptive, way to turbocharge innovation and demonstrably improve outcomes for students and schools.

This post was originally published in the February edition of Knerd Dispatch, the Knewton newsletter. Subscribe here.

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