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Saturday, March 3, 2018

House PROSPER Act eliminates the concept of distance education from the law.

By: Alana Dunagan

Feb 27, 2018

Distance vs. correspondence? Where federal policy stands today

Distance education was first defined by the Higher Education Act in 1992. At the time, the primary aim of lawmakers was to address waste, fraud and abuse by “correspondence programs”, which sent course materials mainly through the mail. In the 1980s, these had been responsible for an outsized share of student loan defaults. The language developed in 1992—which permissively noted that transmission by microwave was acceptable—excluded correspondence programs from receiving federal funds, but did not anticipate how online learning would develop, and therefore  have proved wholly inadequate to regulate online education.

The key variable separating distance education from correspondence programs was the concept of “regular and substantive interaction” between students and instructors. This has led to a series of Department of Education regulations on what is “regular”, what is “substantive”, and who is an “instructor”. These regulations constrain the ability of online programs to innovate around the instructional model, but they do nothing to ensure strong outcomes for students.

In 1992, online programs were just emerging, but today over 30% of students are learning online. Regulations which encourage innovation—while protecting students—have never been more important for online programs.

[Tweet “Regulations which encourage innovation—while protecting students—have never been more important for online programs.” @alanadunagan @christenseninst]

The normalization of online learning?

The two houses of Congress are taking different approaches to drafting legislation to reauthorize the HEA. The Senate is taking a slower approach and is leaving the door open to drafting a bill with bipartisan support by holding committee hearings every few weeks that focus on affordability and federal financial aid programs. In contrast, with little public debate, the House Committee on Education and the Workforce approved the PROSPER Act on a party line vote in December.

PROSPER eliminates the concept of distance education (and microwaves) from the law. Online learning is addressed throughout the law as a normal means of conducting education. PROSPER doesn’t create any new or different requirements for online programs. The law also eliminates Department of Education regulations that schools seek authorization in every state in which they serve students, and instead proposes that schools only be authorized in the state in which they are physically located. States had largely resolved this issue themselves through SARA, the State Authorization Reciprocity Agreement, but this workaround would no longer be necessary if PROSPER became law.

Innovation is guided by incentives

PROSPER removes a complex and burdensome layer of regulation from online programs and allows schools the freedom to design instructional models that take advantage of advances in technology. But for innovation to thrive in ways that benefit students, the workforce, and society, all colleges need to be incentivized to provide affordable, high quality programs that are aligned to workforce needs.

No one, on either side of the aisle, wants to see the federal government dump cash into low-quality programs that are more focused on revenues than on providing an education that helps students succeed. PROSPER’s authors aim to unleash innovation in higher education, but the guardrails the bill places on the industry are simply too weak. Measures in the bill touted as “risk-sharing” are likely to have adverse consequences, but are unlikely to change institutional behavior in ways that protect students. The bill also requires programs to demonstrate that their graduates maintain a 45% student loan repayment rate, or else be ineligible to continue receiving funds. This is an improvement on prior metrics, but is still a laughably low bar, and fails to take into account the outcomes of students who don’t graduate.

Incentives for student success

Organizations design their business models around incentives. In higher education, institutions are paid to enroll students—they have incentives to expand access, but not to achieve outcomes like completion or career success. As a result, money has flowed relatively freely, quality assurance has been a thorny problem, and affordability is an increasingly pressing issue.

Instead, Congress should adopt regulatory mechanisms that focus on outcomes. Changing the way colleges are funded by creating meaningful alignment with student outcomes could improve quality for the entire industry, not just online programs. This could take the form of meaningful risk-sharing, whereby colleges have to repay some financial aid dollars if students default. It could also include increasing the role of income-sharing agreements, whereby some revenues become contingent on a student’s future earnings. These funding models would incentivize colleges to ensure that their programs are adequately preparing students to succeed in today’s labor market.

Using outcomes to create guardrails against waste, fraud, and abuse is preferable to complex, clunky federal definitions of what is meant by online education. Higher education providers will continue to innovate; the authors of the next HEA reauthorization can’t reasonably be expected to create definitions that will remain relevant through the next decade of technological change and business model evolution. Relying on outcomes gives institutions the flexibility to innovate, while still protecting students and taxpayers.

The House bill drops the outdated distance education definition, but doesn’t sufficiently improve risk-sharing or other mechanisms to align institutional incentives with student outcomes. We hope the Senate bill truly modernizes higher education regulation, not just for online education, but for all programs.

Alana Dunagan

Alana leads the Institute’s higher education research and works to find solutions for a more affordable system that better serves both students and employers. In this role, Alana analyzes disruptive forces changing the higher education landscape

Thursday, March 1, 2018

Why Is Accountability Always About Teachers?

By Mark Dynarski 02/21/2018 - Education Next

Most education reform efforts focus on what teachers are doing — professional development, new curricula, bonuses and incentives to raise scores, and so on. All are based on the belief that teachers can teach more effectively if their skills can be improved, their tools can be better, and their efforts can be more energetic.

Teachers are the largest group of staff within the K-12 system, and their skills matter for its performance. But they do not manage or direct the system. Do organizations wanting to improve expect that they can get it done by upskilling only their line-level staff? If Walmart were losing money, would it conclude that management was doing a great job but the floor staff needed professional development? The more natural focus would be on decisions and actions of executives, managers, and senior administrators.

An average teacher is highly experienced
The du jour focus in education reform (currently personalized learning, differentiation, and hybrid learning are topical) typically presumes teachers have an appetite and willingness to change their classroom practices. But teachers are both highly experienced and work in highly constrained settings.
An average K-12 teacher has been teaching for about 14 years. [1] A typical school year is 180 days, a typical school day is 6.5 hours—so average teachers have taught more than 16,000 hours. During those hours they have worked with hundreds of children. If they teach in middle schools or high schools, it may be thousands of children. From those many hours, teachers have amassed pedagogical practices they believe work for their students. These practices may be effective or flawed or plain wrong, but the point is that teachers might not be easily separated from their practices.

And these teachers face a lot of constraints in classrooms. Teachers are assigned to grade levels, their students are assigned to classrooms, their textbooks and supplies, including software and computers, are chosen for them, and the entire school or district is lockstep in a schedule that dictates how much time is spent on each subject. Teachers control how much time they invest outside the classroom in exploring new teaching approaches or learning about what others are doing that might work for them too. But any ideas they find in this kind of self-study still need to fit within the constraints. A teacher who reads about an interesting approach for, say, teaching fractions, has to contend with a textbook and test materials that might focus on a different approach to teaching fractions.

Evidence is lacking on how teachers can be more effective
A group as large as teachers (there are about 3.1 million public school teachers) will include some who are more effective and some who are less effective, and ample evidence exists that teachers differ in their effectiveness. [2]With the exception of how many years a teacher has taught, however, what separates highly effective teachers from less effective teachers has proven to be a tough nut to crack, and, relatedly, far less evidence exists about how to move teachers from the lower side of the effectiveness curve to the higher side.

The New Teacher Project (TNTP) recently looked at professional development in large school districts and a charter school network and concluded that “We found no evidence that any particular kind or amount of professional development consistently helps teachers improve.” [3] It’s not for lack of spending to help teachers improve—TNTP estimated that large districts were spending more about $18,000 a year per teacher on professional development. TNTP also reviewed the broader research literature and commented on findings from the most rigorous studies that had been done by the Institute of Education Sciences: “teachers who received the best of the best [professional development] were no more likely to see large, lasting improvements in their practice, knowledge, or student learning.  In fact, many did not use the techniques they’d been trained to employ—even when researchers were in the room to observe them.” [4] This last point may relate to teacher experience noted above—a teacher who has been teaching a subject for years might not be easily convinced to teach it some other way based on a presentation at a workshop.

These ‘top down’ approaches to improve teaching have been complemented by ‘bottom up’ approaches that offer financial incentives for teachers to improve. The idea of financial incentives is based on logic that economists find eminently sensible—workers work harder when money is at stake, so giving teachers higher pay for higher test scores should cause test scores to go up.

An attractive feature of financial incentives is that teachers can plot their own paths to improvement. This is the ‘bottom up’ aspect. It’s an idea worth testing, and two recent studies have. Both were large and designed to the highest research standards. They are worth discussing at some length because both studies reveal insights about teachers and districts that add to the picture of how accountability might be better focused.

The first study was of incentive pay (bonuses) for middle-school math teachers in the Nashville school district. [5]The largest bonus was substantial, $15,000 a year, for teachers whose performance was in the top five percent of teachers based on historical district data. Currently, the district’s salary for a teacher with 14 years of experience (the US average) and a master’s degree is $56,000, so the bonus was about 25 percent of annual salary. Amounts of $5,000 and $10,000 were paid for teachers at the top 20 percent and top 10 percent. The constraints on teachers mentioned above were not relaxed by the incentive-pay program—teachers still were given their grade levels, their students, and their curricula.

But test scores did not improve. And two other interesting findings emerged suggesting why scores did not improve. One was that teachers reported on surveys that they did not do anything different in response to potential bonuses because they already were working as effectively as they could. A second was that teachers did not believe that a teacher who earned a bonus was a better teacher, or that teachers who did not earn bonuses needed to improve. It’s hard to expect bonuses to do much if teachers believed they already were redlined and did not agree with the logic of bonuses.
A second study measured effects of incentive pay (the federal ‘Teacher Incentive Fund’) in 10 districts and reported similar results. [6] Test scores barely moved (they improved by an amount roughly equivalent to one to two-tenths of an IQ point). The study also reported that districts did a terrible job explaining bonuses to their own teachers. In the fourth year of the program, forty percent of teachers who were eligible for bonuses did not know they were eligible. [7] Eligibility was by school, but even teachers in the same school differed on whether they thought they were eligible. And when asked to predict how much their bonus for increasing scores would be, their answers were far smaller than what the real program was going to pay them. Teachers reported they were eligible for a maximum bonus of about $3,000. [8] Districts reported paying maximum bonuses averaging about $9,000.
One of the program’s requirements was that districts create systems for awarding bonuses that differentiated between teachers—the whole idea of bonuses is to reward above-average performance. Yet seventy percent of teachers ultimately received bonuses. The bonuses averaged $2,000, about 4-5 percent of average teacher salaries. Knowingly or unknowingly, districts essentially converted their bonus programs into teacher raises.

Accountability needs to be more equitable
The findings suggest top-down and bottom-up approaches to improve teaching are unlikely to do much. Yet the last ten years have seen tremendous growth in teacher and principal evaluation systems that rely on test scores and observations to rate teachers. If sending teachers to professional-development workshops or paying them real money to improve does not yield results, it’s at best unclear why expending significant amounts to measure and observe their performance will yield results.

The systems focus their measurement and analytic machinery on teachers, who have the least ability to improve what they do. Senior leaders make decisions that affect every aspect of life for teachers in schools. Senior leaders hire teachers, using criteria they’ve chosen. They give tenure to teachers using criteria they’ve chosen or agreed to. Senior leaders assign teachers to grade levels, give them textbooks and curricula, buy and set up their technology, lay out their schedules, create disciplinary policies they need to follow, and choose programs for how they will work with students learning English, and students with disabilities, and students with reading difficulties, and students who are homeless. And senior leaders decide to change these –they adopt new curricula, set up new testing programs, roll out new technology, change schedules for subjects, modify discipline policies.
Teachers are not making these decisions. They might be asked for input on the decisions, but they do not make them.

A teacher does not declare that next year the school will be using this curriculum as their math series.
An important qualification is that some systems, such as the DC IMPACT system, provide a basis for firing ineffective teachers and rewarding highly effective teachers. [9] Eric Hanushek has written elsewhere about the high costs associated with ineffective teachers. [10] To date, these systems have reported large numbers of effective teachers, and, previously, I noted it is unlikely that 98 percent of teachers really are effective, if the word has any meaning. [11] But being able to identify the lowest-performing teachers at least provides administrators with a basis for removing them.

Accountability for administrators is complicated when organizations are not for profit. Private-sector organizations have profit as natural metric, and the market does the work of measuring it. School districts do not have a measure of profit to gauge their success. They need to decide which ‘interventions’ or processes to test, which outcomes to focus on, how outcomes will be measured, and who is responsible for them. For example, Whitehurst previously has written about the promise of selecting more effective textbooks and curricula. [12] Selecting a new math series, for example, should begin an evaluation cycle: Decide on outcomes, how they will be measured, and how much they should be expected to increase. Then assess outcomes and learn whether the series worked. If it seems hampered by implementation factors, adjust them and assess again. If outcomes improve, the improvement will be experienced both by teachers and by administrators who decided on trying the new math series. Equity in accountability is just as desirable in schools as it is in private-sector organizations.

Finding what works to improve involves risk—ideas might work out or they might not. Under the current system, administrators create the structures and administrators come up with the ideas about what might work. Teachers are then assessed on the results. We need to think about how to shift risks back to where they belong, which is with those who make the decisions.
— Mark Dynarski

Mark Dynarski is a Nonresident Senior Fellow at Economic Studies, Center on Children and Families, at Brookings.
This post originally appeared as part of Evidence Speaks, a weekly series of reports and notes by a standing panel of researchers under the editorship of Russ Whitehurst.

1. https://nces.ed.gov/surveys/sass/tables/sass1112_2013314_t12n_003.asp.
2. A recent study by the Institute of Education Sciences and Mathematica Policy Research reported that having a teacher at the 10th percentile of effectiveness compared to having a teacher at the 90th percentile of effectiveness is roughly equivalent to a student achieving 15 percentile points higher on a reading test and 19 percentile points higher on a math test. Differences of this size are rare in education research. https://ies.ed.gov/ncee/pubs/20174008.
3. https://tntp.org/publications/view/the-mirage-confronting-the-truth-about-our-quest-for-teacher-development
4. https://tntp.org/blog/post/what-does-the-research-say-on-professional-development-anyway
5. https://my.vanderbilt.edu/performanceincentives/files/2012/09/Executive-Summary-Final-Report-Experimental-Evidence-from-the-Project-on-Incentives-in-Teaching-2012.pdf.
6. https://ies.ed.gov/ncee/pubs/20184004/pdf/20184004.pdf
7. See table IV.9 on page 65. https://ies.ed.gov/ncee/pubs/20184004/pdf/20184004.pdf
8. Figure IV.11, page 67, ibid.
9. http://educationnext.org/a-lasting-impact-high-stakes-teacher-evaluations-student-success-washington-dc/
10. http://hanushek.stanford.edu/publications/low-performing-teachers-have-high-costs
11. https://www.brookings.edu/research/teacher-observations-have-been-a-waste-of-time-and-money/

12. https://www.brookings.edu/research/dont-forget-curriculum/

Thursday, February 22, 2018

Google Classroom Now Available to All

By Jack Wallen  | February 21, 2018
Tech Republic

Google Classroom used to be limited to educational institutions and those with G Suite accounts. That is no longer the case. Back in April, 2017, Google announced that Classrooms would be open to anyone with a Google account. Now everyone can take advantage of this remarkable tool that makes education, training, and even outreach possible. So your business, your consultancy, or your department can set up a classroom where clients, friends, family, staff, board of directors, or anyone you wish can join the class and learn.

Not only is Google Classrooms a very powerful tool, it's also incredibly easy to use.

I want to walk you through the process of creating a new Google Classroom that you can then use for myriad possibilities.
What you'll need

Obviously, you need a Google account. You'll also need to have thought out what your class is going to center on. Although you can throw something together for testing purposes, when it comes time to create your actual classroom, you'll need your ideas gathered together such as description of class, beginning assignments, materials, etc. However, in the early stages, it's fine to create a skeleton classroom that you can later retool to perfectly fit your needs.

You'll eventually need students. Students are invited, from within the classroom. This can be done later when you're ready to start the actual class.

Finally, you'll want to have a bit of creativity on your side. Teaching a class requires as much creativity as information. You need to keep those students engaged in the learning, even though this isn't a face to face class.

That's it. With everything in place, let's create a class.
Creating your class

The first thing to do is sign into your Google account and then head over to the main Google Classroom page (Figure A).

Figure A

The main Google Classroom page showing two classes I've started to set up.

From that page, click on the + button and then click Create Class from the drop-down. You must click the EULA checkbox and then click CONTINUE. In the resulting popup (Figure B), you must give the class a Name (required), a Section (optional), and a Subject (Optional).

Figure B

Naming your new classroom.
Once you've filled out the necessary information, click CREATE and your class will be created. When the classroom opens (Figure C), you can then set out to customize the classroom.

Figure C

Your classroom awaits you.

Within the main page of your classroom, you might consider taking care of a few options.

First you'll want to select a new theme for the classroom. To do this, click either Select them or Upload photo. With the look of the classroom taken care of, you'll want to give the class a description. To add a description, click on the ABOUT tab, and then in the resulting window, click the three vertical dots associated with the class title. Click Edit and then give your classroom a description (Figure D) and a meeting location, if applicable.

Figure D

Creating a description for your class.

After you fill out this information, click SAVE.

If you're planning on co-teaching the class, click the INVITE TEACHERS button and send invitations. If you'll be teaching the class by yourself, skip that step.

At this point, you'll want to manage the Class Drive Folder. This is where any assignment material will be stored. When you create a new assignment, any attachments to the assignment will be uploaded to that folder. If you go to Google Drive, and check out the sharing permissions of that folder, you will see that only teachers of the class will have access to the folder. You cannot (nor would you want to) give students access to the folder. The only way students can access files within the folder is through assignments. This folder is a good place to house resource materials for teachers of the class. I would suggest creating subfolders for that very purpose. To create subfolders, open up Google Drive, navigate into the Classroom folder and then into the folder for the class in question. Create subfolders in the same way you create folders within Google Drive.
Creating assignments, announcements, etc.

With your classroom ready, go back to the STREAM tab and click the + button at the bottom right corner. From the popup (Figure E), you can start creating announcements, assignments, questions, and reuse previous posts.

Figure E

Creating your first assignment is easy.
Sending invites to students

After you have everything set up, you can then invite students to your class. To do this, click on the STUDENTS tab and then click the INVITE STUDENTS button. You can then invite students by either name (if they are in your Google Contacts) or email address. You can also invite students using a class code. This code will be visible in the STUDENTS tab. You can send an email out to a list of students, giving them the URL for the class and the class code. Once they've joined the class, they can begin working on assignments and even interacting with their fellow students.
Easy online class creation

You'd be hard-pressed to find an easier way to create an online classroom. Google has done a stellar job of developing a tool that teachers of all kinds can take advantage of. If your business needs a means to teach clients, staff, customers, or just about anyone, you should give Google Classrooms a go. It won't let you down.

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Friday, December 8, 2017

We get to decide the future.

AlphaZero mastered chess in 4 hours and is now the best on the planet ever (link here to academic paper).  It is, perhaps, the first generalized artificial intelligence.  

Babies born today will never drive.  40% of the worlds jobs will be disrupted by automation. 

We get to decide what kind of future it will be.  Will we guarantee a minimum income and set up communities where less work is needed and more play?  Will the very rich hoard ever greater slices of the pie?

We get to decide the future.

AlphaZero Annihilates World’s Best Chess Bot After Just Four Hours of Practicing

George Dvorsky

A few months after demonstrating its dominance over the game of Go, DeepMind’s AlphaZero AI has trounced the world’s top-ranked chess engine—and it did so without any prior knowledge of the game and after just four hours of self-training.

AlphaZero is now the most dominant chess playing entity on the planet. In a one-on-one tournament against Stockfish 8, the reigning computer chess champion, the DeepMind-built system didn’t lose a single game, winning or drawing all of the 100 matches played.

AlphaZero is a modified version of AlphaGo Zero, the AI that recently won all 100 games of Go against its predecessor, AlphaGo. In addition to mastering chess, AlphaZero also developed a proficiency for shogi, a similar Japanese board game. This latest achievement underscores the system’s versatility and ability to acquire superhuman levels of competency in rule-based domains.

Writing in Chess.com, Mike Klein put it this way: “Chess changed forever today. And maybe the rest of the world did, too. A little more than a year after AlphaGo sensationally won against the top Go player, the artificial-intelligence program AlphaZero has obliterated the highest-rated chess engine.”

The system works nearly identically to AlphaGo Zero, but instead of playing Go, the machine is programmed to play chess and shogi. Impressively, AlphaZero acquired its expertise with no outside help, and with no prior empirical data, such as a database of archived chess games, or well-known chess strategies and openings. Essentially, AlphaZero acquired 1,400 years of human chess knowledge—and then some—on its own, and in a ludicrously short amount of time. AlphaZero acquired the expertise to defeat Stockfish 8 in four hours, and AlphaGo in eight hours, according to the accompanying paper, which has yet to go through peer view.

Some publications are reporting that AlphaZero “taught itself how to play [chess] in under four hours,” but that’s not entirely accurate. Rather, AlphaZero learned how to dominate chess in just a few hours. When the exercise starts, AlphaZero already knows how to play chess—just not very well. The system is armed with the rules to the game, but it has zero chess playing experience. Starting from a blank slate, and armed with nothing more than a reinforcement learning algorithm, a neural net, and the pieces on the board for input, AlphaZero plays itself over and over again, refining its skills with each passing match. The system can churn out 800,000 positions each second, as compared to Stockfish 8's 70 million moves a second.

In its one-on-one tournament against Stockfish 8, AlphaZero won 25 games and tied 25 when it played as white, while winning three and drawing 72 games when playing as black—a fascinating result for chess theorists who have long known about white’s supreme first mover advantage. AlphaZero’s favorite openings included the English Opening, the Queen’s Gambit (my personal favorite), and the Queen Pawn Game.

AlphaZero was also pitted against its sibling, AlphaGo, which was also modified to play chess. After eight-hours of self-play, it amassed a record of 60 wins and 40 losses against the digital old-timer.

“After reading the paper, but especially seeing the games, I thought, well, I always wondered how it would be if a superior species landed on Earth and showed us how they play chess,” grandmaster Peter Heine Nielsen told Chess.com. “I feel now I know.”

AlphaZero’s victory over Stockfish 8 has rocked chess experts, who are now wondering if traditional “minimax” chess engines, such as Stockfish 8 and Elmo (another chess engine that got trounced by AlphaZero), are now obsolete. Only time will tell.

Perhaps obviously, AlphaZero’s dominance in chess is less impressive than its mastery over Go—a game that’s significantly more complex. Indeed, expert chess bots have been defeating the best human players ever since IBM’s Deep Blue supercomputer defeated Garry Kasparov in 1997. But this achievement is impressive in that the same system and computational architecture used to win at Go was leveraged for use in other domains, namely chess and shogi.

This is an important point because AlphaGo has been criticized for being too narrow. Unlike a more generalized intelligence, this expert system is really good at doing one thing and one thing only—a far cry from how human intelligence works. But by adapting the system to learn a new set of rules for an entirely new game, the DeepMind developers demonstrated the flexibility of the system and (possibly) its potential to work outside of mere gameplay. Eventually, this system, and others like it, could be used in more “real world” settings, where it could master a number of rule-based domains, such as finance and scientific discovery.

Tuesday, September 19, 2017

Personalized, Competency-Based

“Personalized, Competency-Based.” These three words (maybe two words, depending on how you count) most succinctly define an approach to education that puts each learner at the center of the learning ecosystem.  An alternative term often used for this movement, “blended learning,”  while it has advantages, evoking nuance and hybrid approach, instead of false dichotomies (such as digital and physical, testing and anti-testing, whole language and phonics, new math and old), loses the imperative to move past antiquated approaches to teaching and learning, built from 18th century Prussian model, and brought to scale under Horace Mann in America as universal public education.

Although this assembly line model for education made universal public education possible, it no longer serves our diverse population to prepare them for the careers of the future.  Since the 90s, all 50 states have worked towards clarifying academic standards in core academic areas that are aligned with preparation for college and careers.  Whether based on Common Core, or not, each state has published a taxonomy of 50-100 competency statements for each grade and subject.   In an effort to ensure all young people stay on track to these standards, ability-grouped tracking has lost favor and been replaced by a commitment to all kids.  While an understandable reaction to the demonstrated stigma of hard tracking, this approach has resulted in classrooms that by middle grades where a math teacher may be challenged by students whose math abilities may range from one or more grade behind to one or more grade ahead, therefore, spanning perhaps 600 skill differential between highest and lowest performing students.  From this perspective, “teaching to the middle,” makes little sense.

Instead, as Sal Kahn says, we can “flip the classroom,” and provide anytime, any place, any pace access to learning experiences that target the “zone of proximal learning” for each student.  Instead of moving with the herd of learners by age and grade, each learner can “move on when ready” to the content and depth optimized to their needs. In these new learning environments, teachers become elevated from that of lab technician, working nights and weekends in a near futile attempt to provide timely feedback, to learning doctors, synthesizing self-grading learning materials into personalized, constructive feedback.

We stand at the brink of a new era in education; one in which learners demonstrate skills in diverse settings both within and outside traditional schools.  For less than 1% of the average US K12 budget, every student in America can have access to a learning device like a Chromebook.  New skills, like computational thinking, add to the foundational skills of numeracy and literacy that are rapidly becoming indispensable for participating in the new economy.  Open source, not-for-profit, and philanthropic learning tool providers like EnageNY, Open Up, Kahn Academy, Code.org, EdX are providing a rapidly growing set of high-quality free resources.  While many public schools remain on the treadmill of letter grades on transcripts targeting college admissions, elite independent schools across the nation are shifting the conversation by coming together through the Mastery Transcript Consortium to replace letter grades on transcripts within five years.  Although the federal government is no longer providing meaningful leadership, US global tech titans Apple through XQ Super Schools and FaceBook through the Chan Zuckerberg Initiative have joined foundations funded by Gates, Dell, and Hewlett to help spur the innovation needed for these changes to occur.

What does it look like?  A classroom that has been “flipped” provides learners with online videos and reading material they can watch and read at their own pace, and often in their own language, in their home or any other informal learning environment.  When in school, in the presence of a trained educator, learners typically benefit most from learning while doing, practicing skills like writing and problem solving under the supervision and support of their teacher.  The classroom is flipped because the type of active learning traditionally assigned to homework is done in the classroom and the more passive reading and viewing are done outside of class.  In this environment, learners are freer to move at their own pace, practicing each skill until mastery is evidenced and then moving on instead of waiting for classmates.  Taken to its logical extreme, the concepts of “class” and “course” break down to learning modules that group students more dynamically and avoid the negative consequences of rigid tracking.

What should states do to support personalized, competency-based learning?  The evolution of the learning ecosystem occurs at all levels, individual, class, school, district, and state.  States have a critical role, establishing the conditions to support local innovation.  Most importantly, as I described in my prior post, state-supported summative assessments must evolve to take less instructional time and separate faster accountability instruments from embedded diagnostic tools.  In addition to that, states need to support technology and policies that enable learners to pursue pathways to future careers through an open system of micro-credential badges.  

States like Georgia are leading the charge.  Since 2015, under the leadership of State Superintendent of Schools, Richard Woods, the Georgia Department of Education has been committed to leading the Nation in use of open standards, open source technology, open education resources, and free educational resources to create personalized learning pathways for the next generation of Georgia learners.  GA DOE will begin this work with a focus on computer science and computational thinking and will expand to all other subjects.  They are developing partnerships from the National Science Foundation, Department of Defense, and leading education foundations to establish a micro-credential pathway from K-12 to military enlistment/training, employment/occupational licenses, and postsecondary learning.  When complete, Georgia will become the first state in the nation to implement scalable policies to support personalized, competency-based learning.