November 2011 Archives -

Nominate Knewton for The Crunchies Biggest Social Impact Award!

November 30th, 2011

Photo by Susan Hobbs

The world is facing a full-fledged education crisis. About 80% of school-aged children do not get the basic education they need.

Here at Knewton, we believe that this is a preventable tragedy — and we’re doing everything in our power to fix it. Our mission is to provide high-quality, personalized education to every individual, everywhere.

We’re excited about our work, and we want other people to be too. Which is why we’re asking you to nominate Knewton for The Crunchies Biggest Social Impact Award.

The Crunchie Awards, co-hosted by GigaOm, VentureBeat, and TechCrunch, “recognize and celebrate the most compelling start-ups, Internet and technology innovations of the year.”

Biggest Social Impact is a brand-new award for 2011, and we’d be beyond honored to nab the first title.

Here’s what you can do:

1. Nominate Knewton for the award at this link: http://crunchies2011.techcrunch.com/nominate/?MTQ6S25ld3Rvbg==

2. Then nominate us again! You can nominate us at the link above once a day between now and the deadline of December 13th.

3. Send the link to your network of friends, family, and colleagues, explaining why they should nominate Knewton and why they should tell their network to nominate Knewton.

Thanks for your help!

Posted in Inside Knewton, Knewton | No Comments »

How to Design an Educational Game, Part 2

November 29th, 2011

Designing an educational game that teaches while it entertains may be a lofty goal, but if you follow the steps in this series, you should be able to make your vision a reality — even if you don’t have a huge budget and a cutting-edge creative team. Along the way, I’ll also tell you about our own experience here at Knewton designing an educational course inspired by the principles of gamification.

If you missed the first part of the series, be sure to check it out here.

Step 5: Design your game to yield both quick wins and long-term rewards.

Keep players hooked by providing quick, satisfying wins. At the same time, you shouldn’t give away all your cards at once. Build in an incentive for long-term commitment by teasing players with a sense of what’s to come, building a compelling story line or giving players some reason to take pride in their progress (whether that’s by conferring expert status or some badge of success).

Here are Knewton, we built the Math Readiness Course for CollegeTM with these gaming mechanics in mind. Our points and badges system is designed to provide students with a tangible sense of progress. As students work through the material, more arenas of academic work are “unlocked,” much in the way new levels are unveiled in a game.

Step 6: Embrace the social possibilities.

Encouraging interaction between players of different levels will promote a positive, inclusive sensibility.

Many critics of traditional schooling point to the artificiality of segregating students by age, arguing that this practice does not prepare students for the real world, where people of different abilities often work side by side towards a common goal. Educational gaming expert Kurt Squire articulates the potential for games to illustrate a new model for learning: “Games excel at promoting different levels of expertise, and educators might embrace, rather than apologize for, this capacity.”

Think about how can you design your game to promote this kind of interaction. You might, for example, build in opportunities for players to be deemed “experts” and then insert opportunities for these experts to share their wisdom. This might be achieved through a “hall of fame” board or by structuring expert advice as a reward for novice players. Why exactly does this work so well? In gaming, advice tends to come in action-specific terms (“stay close to the ground to avoid predators” or “save your bonuses for the end”), which is inherently more productive than focusing on intrinsic ability (how smart or slow you are).

We built the Math Readiness Course for CollegeTM with these ideas in mind. The advice and feedback we give students are all encouraging and action-specific to keep students focused on the work at hand. The course also provides a dashboard that allows teachers to create groups of students whose abilities complement each other. For more on how you can bring group work into the twenty-first century by orchestrating meaningful classroom experiences, check out my post on the subject (link here).

Step 7: Encourage critique and design.

If you can stimulate players to talk about your game, you know you’ve done your job as a designer and that you’ve captured the imagination of your players. Take discussion one step further by letting your game universe become self-sustaining and allowing players to produce badges, prizes, or other items that get woven into the game world.

Design itself is cognitively challenging and productive work, so, if you have the technical chops, you might want to consider making design itself an end goal for students. Allow students to tinker with design variables themselves and produce their own versions of the game.

The benefit here is that the work appeals to students of different learning styles. Students who are aesthetically minded might work on enhancing the graphical presentation of the characters and the environment, while students who think like storytellers might reflect on the suspense and mystery in the game and help rewrite any fictional elements.

Here at Knewton we’ve given a lot of thought to the notion of providing a smooth ramp from novice work to expert work. A lot of the research around adaptive learning concerns this very issue. We accomplish this by helping students chunk and organize information differently in their minds (and thus excel towards expert work more quickly) and helping them develop communities where they can showcase original work and receive feedback. Our research in this area is constantly evolving and is inspired by successful examples of gamified learning.

Posted in Education & Technology, Knewton | No Comments »

How to Design an Educational Game, Part 1

November 28th, 2011

Games, like novels and films, are cultural products that inspire loyal fans and followers. The appeal is clear: games are fictional universes that seduce players into alternate realities.

Designing a game that teaches while it entertains may be a lofty goal, but if you follow the steps outlined below, you should be able to make your vision a reality — even if you don’t have a huge budget and a cutting-edge creative team. Along the way, I’ll also tell you about our own experience here at Knewton designing an educational course inspired by the principles of gamification.

Step 1: Align game goals with cognitive work.

It may seem obvious, but a good educational game should be educational — that is, success or progress in the game should be aligned with productive mental work. To score points, move on to higher levels, acquire badges, or gain status, players should be required to solve puzzles, demonstrate mastery of some skill, or better yet, demonstrate a sophisticated understanding of relationships between different parts of a system. Why are systems so important? Because no matter what the subject (whether it’s English, math, or science), mastery depends on understanding how details fit into the whole.

In the course of playing a good educational game, users should grasp, for instance, that an increase in taxes may upset certain fictional constituents but increase the amount available to spend on city infrastructure — or that a decrease in the number of wolves in a wildlife reserve will lead to an explosion of rodents and an imbalance in the ecosystem. In other words, it isn’t enough that the game be about something educational — the Civil War, the Renaissance, or the digestive system. In the pursuit of game goals, players should be encouraged to assess the relationship between action and feedback, and this sort of analysis should facilitate a systemic understanding of information.

Step 2: Make your game adaptive.

A game wouldn’t be that much fun if the outcome of the game didn’t vary depending on the decisions you made. The Oregon Trail, for example, would hardly be compelling if it didn’t matter whether you had more doctors than farmers on board, or began with 50 or 30 pounds of food. Part of what makes the game entertaining is that players get to observe what happens if they tinker with the variables. Not only is this fun (because you get to make decisions), but it also encourages systemic thinking, which is at the heart of productive cognitive work.

This step of the game-building process may be the most challenging. After you’ve sketched out a rough vision of the game world as a “system,” you need to develop hundreds if not thousands (or more) of potential paths for players. You can achieve this by building in many opportunities for players to demonstrate skill, make decisions, and reflect on the relationship between action and feedback.

A useful approach is to think about your game as an adaptive system. At Knewton, we thought a lot about the kind of specificity described above when designing our college readiness course to yield personalized learning paths for each individual. Our adaptive learning system responds continuously to thousands of data points on performance, activity, preferences, and learning style. Not only does this ensure that problems are pitched at the right level, it also enhances the connection students feel to their progress and work in the system.

So remember: when you’re designing your game, engineer it so that each player encounters a stream of challenges that are perfectly calibrated to suit his or her levels. If the game is too easy or hard, players are likely to get bored or confused and put the game down.

Step 3: Build in opportunities for suspense, conflict, and complication.

A story without complications is hardly a story at all. Take The Great Gatsby. The plot is simple (poor man wants to gain the heart of rich girl) but what makes it an engaging story are the complications thrown in: whether or not Daisy truly loved Gatsby, Gatsby’s illegal activities, Daisy’s abusive husband and his relationship with a mistress.

In a story, complications tend to stem from character and from the idiosyncrasies of the environment (a storm or a war, for instance). In a game, complications can stem from these and other factors. After all, a game is much more than a workbook or problem set come to life; it should also generate suspense through unpredictable situations. The game may do this by throwing you a curveball (inflicting a natural disaster upon your city, for example) or by randomizing outcomes (so that every time you visit the “king,” you don’t know whether you’ll get thrown in the dungeon or given a thousand pounds).

Complications are also generated through what educational gaming expert Kurt Squire (pictured left) terms “overlapping goals.” Games are much more challenging and interesting when there are multiple goals present to seduce players and divert their attention. In the Knewton Math Readiness for College™ course, we incorporate the gaming principle of “overlapping goals” by providing multiple arenas of academic work for students to enter at any given point, so they never feel stuck.

Step 4: Make sure the game is simple in the right way.

As Squire notes in his book Video Games & Learning, games are often criticized by educators for being inaccurate or biased–that is, for leaving out certain perspectives or promoting a particular view of the world, whether it’s a Critical-Marxist orientation to power or a materialist theory of history. Even something we consider highly accurate, like an anatomical diagram of the human body in a biology textbook, for example, only shows one system at a time. Even diagrams that do show all the systems in one place are inherently simplified, since they do not show every blood vessel, tissue, or cell. And if they did, they would cease to be illustrative. As Squire argues, models, figures, and diagrams are useful in part because of what’s not there. Games are simplified for the same reason: so that the relationships between variables become apparent and so that after a certain amount of activity, players walk away having learned something.

Does this contradict Step #3? Not really.The key is to find the right balance of simplicity (for the sake of illustration) and complexity (for the sake of suspense-generating complication and conflict). Thinking of the game like a story might help. Reading a bare-bones outline of a story plot is hardly interesting. But pad a story with too much description and unnecessary action, and you bore and confuse the reader.

The story metaphor cuts in other directions as well. Just as you would not introduce too many subplots at once in a novel, you should not confuse the players by providing so many goals and opportunities that they are unable to focus. As Squire suggests, you should consider unveiling certain parts of the game only after players hit specified triggers: “A key [World of Warcraft] design decision may be not starting newbies in large, populated cities but instead waiting until they had experienced core game systems, such as combat, quests, and grouping, before lifting the veil and showing the game’s depth.” Not only does this design strategy focus the player’s attention, it also heightens suspense and investment in the game since players get more excited to enter a new world if it’s built up as a reward.

Inspired by these insights into player psychology, we designed our Math Readiness for College™ course so that students can unlock academic work in a satisfying way and experience a visceral sense of progress as they master skills. The process of “unlocking” something doesn’t have to be flashy or complicated, but there should be at least something that signifies the change, whether it’s a sound, an animation, or the accumulation of points and badges, to show students (or players) that they are making progress.

For a lengthier treatment of ways that an adaptive system empowers students, check out 5 Ways to Make Students Smarter.

Stay tuned for Part 2 of the post!

Tags: educational game
Posted in Education & Technology, Knewton | 2 Comments »

How Adaptive Learning Can Help Students Think About Meaning

November 22nd, 2011

The following experience is common to most teachers: a meticulously planned class activity succeeds in capturing student interest for a few minutes, but attention evaporates quickly and afterward no one can remember the point of the lesson. Despite the flashy visualizations, the expensive 3-D models, the age-appropriate allusions (references to Justin Bieber and Lindsay Lohan), the clever asides, and sensational content, the material failed to stick. What went wrong?

According to Daniel Willingham, cognitive scientist and author of Why Don’t Students Like School?, the mystery of student engagement comes down largely to one thing: meaning. He asserts that it is the extent to which we get students to think about what everything means that determines whether or not we truly earn their attention and successfully transmit knowledge. He concludes that structuring lessons so that they emphasize (or bring into relief) the “meaning” of the material is the most effective way to ensure student engagement and retention of knowledge.

But what exactly does it mean in practice to get students to think about “meaning”? Though it may seem like an elusive concept, thinking about meaning generally involves thinking about structure, synthesizing information, and applying knowledge to new circumstances. In all these instances, students are engaged with both the idiosyncratic texture of the material they’re working with (whether that’s language, numbers, code, or clay) as well as deep structure and overarching process (abstractions and ideas).

With the advent of new technology, there are more ways than ever before to engage students in a deep, serious fashion. Adaptive learning, a teaching method premised on the idea that the curriculum should adapt to each user, can harness the power of data mining to provide a wealth of opportunities for students to think about meaning. Here’s a brief look at how:

A) Students think about meaning when they think about pattern and the bigger picture.

Whether the subject matter is poetry or earth science, students think about meaning when they start to recognize how details fit into the bigger picture–when they notice how a twist in phrase contributes to the rhythm of an entire stanza, or how the presence of certain rock in a region indicates that volcanic activity occurred thousands of years ago. The ability to grasp this kind of relationship generally signifies a level of cognitive maturity. It requires students to move back and forth between pattern and detail, the abstract and the concrete. An adaptive system can help students develop big picture and pattern-recognition ability by a) drawing concrete moments back to the abstract (asking students to compare and contrast details and comment on structure and process) b) drawing abstract moments back to the concrete by asking students to apply principles, theories, and formulas to the idiosyncrasies of new problems and situations and c) tracking the efficacy of these shifts to optimize the flow of cognitive work for each learner’s individual style.

B) Students think about meaning when they synthesize learning across domains and subject areas.

Students think about meaning when knowledge in one area shows up unexpectedly somewhere else–when they’re studying biology, and everything they learned in chemistry comes into play, or when they’re using writing skills they acquired in composition class to organize a history essay. In other words, students think about meaning when they encounter familiar material from an unfamiliar angle or through the lens of a new context. An adaptive system can facilitate these experiences by using an individual’s subject area strengths to remediate his weaknesses. How might this work with a subject like math? Someone who is, say, a naturally scientific thinker might develop his math ability by using math to conduct experiments and test hypotheses about the natural world, while someone who is “musical” might use math to grasp the science behind harmony. The benefit here is that you can use student curiosity in one area to fuel interest in every other.

C) Students think about meaning when they’re forced to reflect on their learning processes.

Studies show that the very act of reflecting on your process (whether or not the reflection is even read by an instructor) improves learning outcomes because it helps students become more self-aware. While developing greater self-awareness is a natural byproduct of learning, adaptive learning can stimulate and speed up the process by inserting “reinforcement” moments into cognitive work–moments that prompt a student to reflect on his particular solution, underscore the concept behind the solution, or describe the structure of some body of information. Even if a student happens to correctly guess the answer to a question, he will not be able to complete the lesson without proving his grasp of the underlying concept. This of course increases the chance he will experience repeat success with a similar problem. Any online learning program can achieve these aims in a basic way, but an adaptive system can bring reinforcement to a new level by evaluating how well such moments are working and by providing reflective moments (and even longer exercises) tailored for each learner’s style.

D) Students think about meaning when they’re judging/reacting to the work of others.

Many students engage deeply with their studies when they begin to develop a set of personal standards and aesthetics that pertain to academic work–when they know what they like and dislike and find impressive, effective, or compelling. How does this sense of standard and self evolve? When a student evaluates the work of others, he makes decisions that force him to define his own value system. He asks himself questions like, “What does it mean for an argument to be sound and logical? How can this essay be more persuasive? What would make this story more suspenseful?” When giving and accepting feedback in this respect, students also develop valuable interpersonal skills and the ability to accept criticism graciously.

The right social context is necessary to facilitate this kind of interaction. Because it processes thousands of data points on student activity and performance, an adaptive system can help students find like-minded peers and organize communities of learning. These communities might form within individual classes or schools or across districts and beyond. And depending on the aim of the class, teachers can use data regarding performance, learning style, and preferences, to create cohorts of students within classes who complement each other academically. By tapping into arenas where they can learn from peers, showcase their own work, and receive feedback, students get into the habit of professional exchange at an early age.

E) Students think about meaning when they’re exposed to expert work.

Students think about meaning when they know that the subject they’re studying is alive, evolving and not just an arbitrary segment of knowledge that unknown authorities dictate they must master. Students recognize this when exposed to work done by experts at the forefront of their field. The material stops being dead and static, and students begin to fathom the true ramifications of the knowledge they’re immersed in. Exposure to expert work also illuminates a path to expert-level work, should students be interested in becoming experts themselves.

A sophisticated adaptive learning system can not only use student performance and activity to identify weaknesses and serve up problems that eliminate them; it can motivate and enrich student learning in a way that is equally precise. Because an adaptive system is computerized and involves tagged content, it can be hooked up to enormous repositories of expert material that normally lie beyond the realm of school (if those repositories are tagged). When appropriate, such a system can direct students to specific articles, studies, reports and books created by experts, for experts. (In this way, students can function as “apprentices” to experts, just as they did centuries earlier.) Companies already employ similar recommendation engines to figure out consumer preferences and recommend purchases; an adaptive learning system harnesses the same kind of technology for intellectual endeavor.

Posted in Adaptive Learning, Knewton | 8 Comments »

Speed Up, Save Time: Why Your Website Needs a Pattern Library

November 21st, 2011

We recently did a major overhaul of knewton.com.

Old homepage:

New homepage:

As with any website change, there was plenty of work involved from our marketing team, developers, designers, copywriters, and others. And, in the midst of all the turnover turmoil, we also made the decision to add another task to our plate: creating a pattern library.

For those who aren’t familiar, a pattern library is an online space dedicated to sharing design elements of a product or website with a group of designers and developers. Yahoo! is widely credited with pioneering the pattern library in the early 1990s.

Our marketing site pattern library organizes and systematizes all the elements of our new site, and lives on a wiki which anyone in the company can access. (We also have a pattern library for all the design elements of our products).

Our pattern library has already proved helpful to us as we continue to iterate on our site design. If you’re considering implementing a pattern library, this post will give you some background on pattern libraries and why they’re useful.

What exactly is a pattern?

Patterns are ways to describe best practices, explain good designs, and capture experience. They originated as an architectural concept by Christopher Alexander. As Alexander put it in A Pattern Language, “each pattern describes a problem which occurs over and over again in our environment, and then describes the core of the solution to that problem, in such a way that you can use this solution a million times over… without ever doing it the same way twice.”

In the context of our marketing site pattern library, a pattern might be a button that appears in our buy flow, or a certain type of headline. The pattern library will define this element from a user interaction (UI) perspective, focusing not only on what the pattern is, but also on how it is being used and the specific problem it is solving.

Why is a pattern library helpful?

Even though we knew the pattern library would require an initial time investment, we also knew that in the long run it would:

Speed development
Ensure consistency
Aid in rapid prototyping
Maximize usability

In other words, it would save us time and make our product (in this case, our website) better.

For our designers, the pattern library gives them one place to see all the elements of the website. This allows them to more easily evaluate each pattern for consistency.

For developers, having each element in the pattern library gives them easy access to the element’s code whenever they might need it (the library automatically pulls in the code, so it is always up to date). In addition, the library contains each element’s visual specifications; developers no longer have any need to dig through PSDs for this information.

Other benefits of the pattern library include improved communications and knowledge-sharing between our design, copy, marketing, and development teams, as well as increased productivity. The pattern library is also a great way to help new hires or others in the company get up to speed on a particular project.

What does a post in the pattern library look like?

As outlined above, each entry focuses on a specific design element, and explains not only what the element is, but also how and why it is used.

Posts within the library are divided into two categories: “patterns” and “feature specs.” Feature specs are technically also patterns, but for our purposes, the distinction functions to separate more specific, developer-centric information (feature specs) from broader elements which focus primarily on interaction and usability (patterns). Under these rules, an example pattern might be “type styling,” while a feature spec might focus on the way in which type styling is used on a specific part of the website.
Entries for both patterns and feature specs follow the same structure:

What problem does this solve?
When to use this pattern
What’s the solution?
Screenshot
Spec (all the information a developer needs to translate designs to a working feature)
Code & Demo

Let’s take as a sample entry a video carousel that appears on our site. Here’s the screenshot of the carousel:

Here’s the way the post for this element would appear in the pattern library:

1. What problem does this solve?

The user must be able to digest important content. This content must be easily viewable… Explaining complex concepts without overcrowding the page with multiple paragraph breaks was the challenge.

2. When to use this pattern

There are multiple videos of similar subject-matter
Videos are playable on its current page and not in a separate window

3. What’s the solution?

Display a carousel with thumbnails to entice the user to click through at browse all of the videos. Also, provide left and right navigation arrows to “scroll” through each video and to leave room in the event that more than four videos are available within the carousel. A clear, large paragraph description that is limited to only two lines is used to ensure that the carousel supports rather than competes with its description.

4. Screenshot

(See above screenshot)

5. Spec

6. Code & Demo

This section of the post includes up-to-date snippets of source code from the developer’s implementation of the pattern. In addition to simple code samples, the developers also construct Pattern Demos. These are small-scale applications designed to showcase the look, feel, and functionality of a pattern.

What is copy’s role in the pattern library?

In the same way that design elements can be systematized for ease and consistency of use, copywriting can also benefit from this type of organization.

The “patterns” and “specs” that are used to define design practices are slightly different in the context of copy. Patterns define the tone or voice that the copy takes. The are usually general guidelines for how to create pieces of copy. On the other hand, specs are specific pieces of copy that should appear routinely throughout the product or interface.

Benefits of copy patterns:

Many of the benefits that apply to design elements also carry over to copy. Having established patterns makes copywriting fast, consistent, and scalable.

For a startup, copy patterns also offer other benefits. By establishing the language that is used to describe a product, its features, or its functionality, consistency can be established both internally and externally as to how people discuss it. A universally recognized set of guidelines for how employees talk about their work strengthens branding and marketing and positions the company for success in the long run.

How to incorporate creating a pattern library into your work flow

The standard work flow for creating a new piece of software is roughly: Spec, Design, Development, Review. A product team will define the specifications of the new feature, and hand those off to the design team. The design team translates those specs into design documents. Those documents are delivered to developers for implementation. The application is then reviewed to see if it meets all necessary requirements.

A pattern library does not change the standard work flow. It speeds it up. In the design phase, the user experience team does not need to create numerous PSDs to show the feature and its states. Rather, they just develop the Specs for a library post, which takes far less time. It is the developers, however, who glean the real benefit from this. Coding up a pattern is orders of magnitude easier than developing from a PSD. Using a pattern library turns months of work into weeks of work.

Once the underlying patterns of the library have been spec’d and implemented, the effect required for new feature development drops to nearly zero. Work can be done quickly and with little overhead, as there’s no longer a need for discussion. In the long run, the system pays for itself: the more time spent enriching the library, the less time needed to maintain both the library itself and the software it defines. There’s no downside to using a pattern library.

Posted in Inside Knewton, Knewton | No Comments »

How Analytics Are Transforming the Art of Teaching

November 19th, 2011

New technology is transforming the art of teaching. It is reducing administrative burden and enabling teachers to orchestrate activities more effectively and coach individual students with a precise attention to their needs. In particular, adaptive learning–a teaching method premised on the idea that a curriculum should adapt to each individual–can harness the power of data mining to give teachers new freedom in the classroom and deeper insight into their students.

1) Reduce Administrative Burden

As many teachers know, the administrative aspect of running a class can make high-minded goals seem completely disconnected from reality. Online “grade books” that allow teachers to organize information regarding grades, absences, and tardiness have existed for years. But imagine a sophisticated reporting dashboard that gives teachers insight into the learning process itself–engagement, effort, retention of information, etc. Imagine, also, the flexibility of scope that such a dashboard could provide–teachers could grasp patterns in student activity and performance across the whole class or drill down into individual student profiles to determine exactly why a student is struggling.

Instead of spending a great deal of time scoring and organizing material, teachers can spend more time analyzing data, determining actionable steps for their students to take, and fine-tuning their lectures and curriculum. The end result is more time spent on teaching and learning rather than updating the grade book.

2) Address the Diverse Needs of Students

One of the biggest challenges facing schools and administrators today is the growing diversity of the students within their population. A greater diversity of students means a greater diversity of needs to consider. Some struggle because English is not their first language; others have difficulty with focus or organization. Others may be particularly weak in some area but possess unusual strengths in another (which the existing curriculum may not take into account).

As every teacher knows, classroom management is a consummate juggling act. To remain attentive to the needs of all students, teachers must engage the more advanced students while helping the struggling ones catch up. At any given point in a lesson, a teacher must decide whether to move through the material aggressively and add more challenges and twists to the problems presented, or build in more of cushion for those who are confused. Any one of these strategies, including “teaching to the middle,” is bound to leave some students feeling bored or confused.

Blended learning solutions that offer a sophisticated analytics dashboard give both students and teachers more freedom in this respect: students move through coursework at their own pace and teachers retain control over the classroom while gaining insight into the learning process. A teacher might discover through analytics that a student who is weak with math word problems is struggling because he has difficulty with reading comprehension; that teacher can then direct him to material that improves his grasp of syntax and vocabulary. Another student who understands mathematical concepts but has trouble with carelessness in arithmetic can receive feedback about how to develop stronger estimation abilities or check work once completed.

For more on how computerized learning offers more flexibility than ever, check out this article on mastery-based learning.

3) Improve Engagement Levels

Academic success hinges on engagement. With the joblessness and disillusionment felt by many young people due to the current economy, a general culture that undervalues school, and the competing attractions of video games and TV, low engagement is a serious problem in classrooms. Nationwide, only 56 percent of students who begin post-secondary education receive a degree within 6 years.

How can analytics help solve this problem? Just as marketers use A/B testing to determine the most effective content strategies, so teachers can use data analytics to perfect their curriculum. A reporting dashboard that measures the efficacy of content in a computerized system can help teachers determine the strongest and weakest aspects of their teaching materials. This ensures that content can be analyzed for fine-tuned improvements from year to year and that students are never stuck with a dull or outdated textbook.

4) Increase Flexibility

Analytics will allow teachers to allocate their time more efficiently; this in turn will enable teachers to focus on the aspects of teaching that appeal to them most. Those who are gifted “orchestrators” can experiment with class activities and ways of encouraging interaction and collaboration among students; and then use social media and other tools to publicize the results to other teachers. Those who have developed exceptional content over the years can work on fine-tuned improvements of their material. And those who excel at presentation might record their lectures and broadcast them to thousands.

Knewton Math Readiness for College™

We took all the above into consideration when designing the Knewton Math Readiness for College™ course dashboard. We wanted to “bucket” the information and limit the scope of presentation in helpful ways, though we wanted teachers to have the freedom to interpret the results and add value however they see fit. To accomplish this, we developed an “on-track/off-track” concept to measure student progress through the course. This concept functions as a binary indicator that helps teachers grasp information about his/her class efficiently. Using this tool and others, teachers can see reporting data from two perspectives:

A) The Whole Class. The dashboard includes a histogram which provides a big picture assessment of the whole class’ “track” status.

While this might not seem like a particularly helpful tool for a class of 10 or 15, it dramatically assists teachers of classes with 80, 100 or several hundred students who want to see if certain strategies are working across a whole student population. Using the dashboard, teachers can also see how students are performing in individual subject areas–and which segments of material are the most challenging for students, which are the least and what kind of patterns in both performance and activity emerge across the class. After multiple years of teaching the same course, teachers can compare the data from year to year.

B) Individual Students. While the presentation is streamlined so that teachers can focus on the big picture, the dashboard allows teachers to click into the interface and drill down to each student’s work in the system. Teachers can see how students have performed on specific quizzes and exams, and if a student isn’t grasping the material, teachers can use data to determine whether it’s due to carelessness, forgetfulness, or confusion, and where precisely such confusion occurs. Current research concerns soft factors like engagement and how to quantify them with specific metrics and feed that information into the dashboard.

Posted in EdTech, Education, Technology | No Comments »

STEM Education: Does America Have the Right Stuff? [INFOGRAPHIC]

November 18th, 2011

America was built on innovation and entrepreneurship. But recently, we’ve let our students fall behind. In a study of 65 industrialized nations, American students were ranked 31st in science and 23rd in math.

If we as a nation want to remain competitive in today’s globalized economy, a stronger focus on science, technology, engineering, and math (STEM) education is key. Check out this infographic for why STEM education is so important, and what steps we can take right now to improve the way we teach our students.

Click on the image to view the full STEM education infographic.

Tags: infographic
Posted in Education & Technology, Featured, Knewton | No Comments »

5 Myths about Mastery-Based Learning

November 18th, 2011

Whether or not you’ve heard the term “mastery-based learning,” you’ve probably encountered it in practice, in school or on the job. In any situation where you’re given a set of labs, problems, or activities where your progression is dependent on successful completion of various tasks rather than seat time, you’re engaging in mastery-based learning–a teaching method premised on the idea that student progression through a course should be dependent on proficiency as opposed to amount of time spent on academic work.

As every teacher knows, classroom management is a consummate juggling act. To remain attentive to the needs of all students, teachers must engage the more advanced students while helping the struggling ones catch up. At any given point in a lesson, a teacher must decide whether to move through the material aggressively and add more challenges and twists to the problems presented, or build in more of cushion for those who are confused. Any one of these strategies is bound to leave some students feeling bored or confused. Mastery-based learning aims to help teachers in this respect by allowing students to move through coursework at their own pace.

Key features of mastery-based learning (MBL):

1. Curriculum design hinges on assessments
2. Assessments may take any form as long as they determine proficiency
3. Graduation to the next grade/level/topic is contingent upon successful completion of prerequisite assessment.
4. Curriculum is committed to the success of all students; students are not “allowed” to give up.

It turns out that there are quite a few misconceptions about mastery-based learning. Given new technology that can help us reimagine mastery-based learning, it’s prime time to debunk these myths.

Myth 1: Mastery-based learning is difficult to implement.

Mastery-based learning was first introduced in the 1920s through the Winnetka Plan, an educational experiment engineered by district superintendent Carleton Washburne of Winnetka, Illinois. The experiment was inspired by John Dewey’s research in the University of Chicago Laboratory School and designed to expand the focus of education to include creativity and emotional and social development. Under early implementations of mastery-based learning, a teacher could provide students with the same labs, quizzes, and tests (through which they could move at their own pace by demonstrating proficiency and having the work checked off) but the teacher still had to evaluate assessments and coach students individually on top of delivering lectures that transmitted the knowledge in the first place.

While the plan received widespread attention, efforts to promote mastery-based learning stagnated after a few decades, given the absence of a technology to help implement it. During this period, it was difficult to conceive of how students might move forward at their own pace and still function within the existing structures of school (classrooms, grades defined by age, rigidly defined schedules) which had evolved by mid-20th century America to seem fairly incontrovertible. Mastery-based learning also created some administrative burden for teachers who had to track students through their self-paced courses and offer remediation when necessary.

New technology allows us to re-envision mastery-based learning, so that it is far more flexible. By breaking up course materials into units of learning objectives and chunking those objectives into digestible modules, educators have developed self-paced courses of study that fit neatly in the most rigid schedules. Computerized adaptive systems bring this modularity to a new level, making the resulting courses both easier to implement and more effective for students. Because academic concepts can now be tagged at the atomic level, it is possible to conceive of corresponding academic work and assessments in smaller and smaller components. Since a computerized system can capture performance and activity on these components, it is possible to offer courses that adapt to student needs on the most precise level. This reduces the work load for teachers who, in previous versions of mastery-based learning, had to coach students individually through their respective courses of study.

Myth 2: Mastery-based learning is expensive.

In the past, MBL has been used in some districts to justify increased funding, increased testing requirements, and a great deal of energy investment from students, parents, and teachers. This does not mean that mastery-based learning is inherently expensive, however. If implemented through online adaptive technology (as described above), mastery-based learning can be introduced at minimal cost.

Mastery-based learning can also provide a fairly inexpensive solution to a number of challenges facing administrators–including the acceptance of an increased diversity of students and the expansion of curriculum knowledge for teachers to cover. Because an adaptive system responds to the exact needs of each individual, it can be used for a wide range of students. And because such a system is computerized and involves a precise tagging system, it is easy to organize large amounts of content and track performance on that content.

Myth 3: Mastery-based learning makes grading and reporting more difficult.

Because MBL requires that students be judged on their mastery of material in an absolute sense as opposed to their performance relative to others, proper reporting requires attention to a whole series of outcomes. In traditional schooling, students typically receive an “A” or a “B” as a grade that summarizes their achievements relative to others (while an “A” may always mean an “A” in some minds, it typically reflects a student’s performance in relation to the rest of the class he or she happens to be in). If MBL is strictly implemented, however, one must report that a student mastered “verbs,” “tenses,” and “parallel sentence structure” but not “idioms” instead of just issuing a “B.” This is understandably more difficult to report and reflect in a transcript.

Adaptive technology provides a ready solution. With a comprehensive dashboard served by an adaptive system, student outcomes can be aggregated and teachers can view all the concepts and skills a student has mastered in a single glance. Trends and patterns of mastery across the class (and even across a grade or district) also become apparent.

Myth 4: In mastery-based learning, too many students will fail (because standards are too high).

As described above, mastery-based learning is premised on the fact that no one is allowed to fail and that everyone (regardless of gender, race, or socioeconomic status) will succeed, given the right conditions. The emphasis on mastery or proficiency as opposed to effort and seat time, however, makes some nervous and raises some questions: what about those who do not pass, who do not demonstrate mastery? And who try and fail repeatedly?

Although it is true that MBL holds all students to the same high standards, the teaching method creates an environment that helps students meet those standards. Anyone who has ever struggled in a classroom knows that missing an insight everyone else experiences can be stressful. Feeling left out or slow in a public situation can indeed exacerbate the challenge, but the self-paced nature of a mastery-based approach allows students who are self-conscious to relax into their learning and make significant gains in a seamless and natural way.

As far as remediation is concerned, any mastery-based system can provide a wealth of triage opportunities, if enough quality content is available. And, with the development of adaptive technology, triage opportunities have the potential to be even more sophisticated than before. An adaptive system can determine the exact needs of each student and match him with learning objects and activities that bring him up to speed quickly.

Myth 5: In mastery-based learning, standards are too low and advanced students are not challenged.

In mastery-based learning, advanced students can progress through material at their own pace and remain engaged by pursuing more challenging work. The richer the content within the learning system, the more material a student can explore if he advances at a swifter pace than the others in his cohort. In this sense, the standards for such students are not low at all–they stretch to help each student maximize potential.

Because success is defined on an absolute and individualized basis, students cannot be satisfied with their achievements relative to others; they are encouraged to seek their own course and take responsibility for their own learning. A sophisticated adaptive learning system can take this to a new level. Because an adaptive system is computerized and involves tagged content, it can be hooked up to enormous repositories of expert material that normally lie beyond the realm of school. When appropriate, such a system can direct students to specific articles, studies, reports and books created by experts, for experts. Adding even a slight degree of adaptivity to the sheer amount of digital content available has the power to significantly amplify the learning experiences we are currently familiar with.

For a more detailed treatment of how adaptive learning can expose students to advanced work, check out my blog post, Why Students Don’t Like School Part IV.

Tags: mastery-based learning
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Spirit of Innovation Challenge: Enter by 11/29/11

November 11th, 2011

“Get your genius on.”

That’s the tagline behind the Conrad Foundation’s annual Spirit of Innovation Challenge, which invites high-school students to use their science, technology, engineering, and math (STEM) skills to create real-world, commercially viable, technology-based products in one of three broad categories: Aerospace Exploration, Clean Energy, and Health and Nutrition.

This is no high school science fair. Forget testing the permeability of soil or comparing the effect of various liquids on plant growth. Instead, the Challenge gives teams of 2-5 students the chance to apply their brainpower to real-world problems, gaining valuable entrepreneurial experience along the way. Last year, a team of two sisters developed a nutrition bar that has since been used on a NASA space shuttle. In 2009, another team that created a motionless thermal generator to make electricity from the ocean floor went on to receive two patents for their technology.

All teams are matched with mentors — world-renowned scientists, engineers, and entrepreneurs — to help them plan and execute their ideas.

The deadline to enter the contest is November 29, 2011, and there is no fee. For the general entry round, each team is required to complete a short, online abstract (found on the online submission portal) that includes team information and a short product description, and answers the following three questions: What problem does the product solve? How is it innovative? What are the key product features and how is it beneficial?

Selected semi-finalists will be asked to submit a full product proposal, and five finalists in each category will be invited to present their ideas at the 2012 Innovation Summit at the NASA-Ames Research Center in Silicon Valley, where they will vie for $5000 seed grants, patents and commercial opportunities. This year, three teams will also be invited to travel to Rio de Janeiro to participate in Rio+20, the United Nations Conference on Sustainable Development.

More about the Conrad Foundation

The Conrad Foundation was founded by Nancy Conrad, education activist, in memory of her late husband Pete Conrad, who was an astronaut, innovator, and entrepreneur. “The Conrad Foundation is dedicated to fundamentally shifting how science, technology, engineering and math (STEM) are taught in K-12 schools and across socioeconomic levels.”

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Tech Pioneer Schools

November 10th, 2011

Whether it’s because of cost, implementation challenges, or the fear that something newer and better will rapidly render the investment outdated, educational institutions are not known for being early adopters of technology. Some institutions, however, have pushed forward with initiatives that have broken ground in all facets of education–from classroom learning to campus life to administration. Here are some schools that have taken the plunge and are finding the deeper waters exceptionally rewarding.

Library Science & the University of Chicago

The University of Chicago’s new Joe and Rika Mansueto Library is like something out of science fiction, with its cranes, elevators, and underground labyrinth of enormous steel cases. While many academic libraries are digitizing their collections, Mansueto is taking it one step further by also pioneering an automated storage and retrieval system. New York Times writer, Jaywon Choe compares it to “the door-sorting machine” from Pixar’s “Monsters Inc.”

How does the system work? A user requests a book from the online catalog; several cranes run along parallel tracks and one locates the requested book using bar codes; another crane removes the appropriate container and transports it to an elevator which lifts it to the resource center; a human from the resource center retrieves the book, scans the bar code and lets the student know that the book has arrived.

The best part? All this occurs within 5 minutes.

Manseuto’s efforts to re-envision the modern university library extend to the laboratory as well: it has a lab for both conservation and digitization. The lab mends paper and rebinds old texts — some of them papyrus — when it’s not preparing materials for scanning and digitization.

Math Readiness & Arizona State University

Nationwide, only 56% of students who begin post-secondary education receive a degree within 6 years. Students who require remediation before beginning regular coursework have a lower chance of graduating than those who do not require remediation. After all, more than 50% of U.S college students who require remediation do not receive a bachelor’s degree.

Students at Arizona State University who are not college-ready in mathematics are now remediated with a self-paced, online developmental math course powered by the Knewton Adaptive Learning Platform™, which transforms educational content to uniquely personalize the individual learning experience. Students progress through the course by completing diagnostic exams. Depending on their performance on these tests, they either pass out or place into a given lesson. These lessons contain multiple learning items or “activities,” which are designed to be short and full of real-world examples. Meanwhile, game mechanics, micro-rewards and an intuitive interface ensure that students are engaged and working through the material in a productive way.

The Knewton Math Readiness for College™ course also features a rich reporting interface, providing instructors and tutors access to a plethora of student data. Instructors can view class lists to see which students are on or off track, or they can search for individual student performance metrics. Instructors can also view trends across an entire group of students to determine if there are particular concepts that are problematic across the board. The reporting interface also functions as a class management tool, enabling instructors to optimize class time by focusing lessons around exactly those concepts with which students need the most help.

Dorm Life & the University of Kentucky

A newly renovated dorm at the University of Kentucky offers students a dazzling immersion in technology. The recent $1 million renovations include 20 wireless access points in the basement and first floor (enough to serve 75 high-bandwidth users at the same time), 11 large-screen TVs, and two 82-inch interactive whiteboards in the dorm’s two smart classrooms which are fully equipped for international conferencing.

As part of the program, each of the 177 freshman housed in the dorm were given an iPad and required to enroll in a series of courses that incorporate technology in unexpected ways. The offerings are eclectic and include “Social Connections: The Sweet and the Bitter of Relationships,” “The Vietnam War,” and “The African-American Experience in Kentucky.” The aim of the high-tech dorm is to teach students “IT IQ,” or the ability to use technology for research and collaboration. In order to allow the rest of the university and education community to benefit from the experiment, faculty directors and social scientists will be watching closely to see what happens and whether any best practices can be gleaned from the program.

Distance Education & West Virginia state schools

A major improvement in West Virginia’s statewide broadband network will yield new opportunities in distance education to institutions which until now did not have reliable Internet access. In 2010, West Virginia was ranked 48th nationally in broadband penetration, according to the Federal Communications Commission. By 2012, however, West Virginia is expected to be among the top five most connected states in the country, due to a joint state and federal effort called the “West Virginia Statewide Broadband Infrastructure Project.”

The $126 million in federal stimulus money backing the project is already having a dramatic impact on enrollment numbers at several state schools. The new infrastructure project is also revitalizing the online offerings of many colleges and providing schools with thousands of new potential students, many of whom are working adults who reside in rural areas. Reliable online classes afford them flexible scheduling and also reduce travel time (some would otherwise have to drive 100 miles or more, over rough terrain, to attend classes).