Here at Knewton we have developers and designers, product managers and marketers, content developers and customer service specialists.
We also have data scientists.
Ever wonder what, exactly, data scientists do — or what their work has to do with sandcastles and Star Trek? Check out this video featuring Knewton’s own Jesse St. Charles for the answers to all these questions, plus a glimpse at why data scientists are such a key component of the Knewton team.
For more job profiles, click here.
In this week’s College Readiness News Roundup, read articles about the recent stats released by New York City, the intensive remediation program at CUNY, digital badges as measures of college readiness, and more.
1. Three of Four Students Not Prepared for College, City Says
Read more about the alarming stats in this article from the New York Times.
2. In College, Working Hard to Learn High School Material
CUNY now offers an intensive semester-long remediation program, part of an effort to increase graduation rates for incoming freshman. Read more in this article from the New York Times.
3. Digital Badges: The Great Equalizer?
Can digital badges help educators measure students’ competency and college readiness? Read more in this Huffington Post article.
4. Ohio Chancellor Wants to End Remedial Education at Public Universities
Jim Petro, Ohio’s Chancellor of Education, says that universities are not the place for remedial education. Others disagree. Read more in this article from Inside Higher Ed.
5. Community Colleges: A Partial Fix
Nearly half of Virginia’s high-school graduates entering community colleges in the state require remedial education. While efforts to improve remedial courses are inspiring, they remain a band-aid solution. Read more in this article from the Richmond Times-Dispatch.
6. College Readiness Hits Progress Reports but Doesn’t Sway Scores
The New York City Department of Education is adding three “college readiness” data points to its annual reports, which many interpret as an acknowledge of the fact that a high school diploma does not necessarily predict college success. Read more in this article from GothamSchools.
7. Common Core Found to Rank With Respected Standards
Researchers at the Educational Policy Improvement Center compared several other career- and college-readiness standards to those of the Common Core. Read more in this article from Education Week.
In this week’s EdTweet Show, hosts Jess and Dave recap another set of their favorite edtech tweets of the week. Check out news about the book of the future, college math preparation, and more.
Miss Part 1, 2 or 3 of the series? Check it out here.
I recently read Daniel T. Willingham’s Why Don’t Students Like School?: A Cognitive Scientist Answers Questions About How the Mind Works and What It Means for the Classroom.
As I was reading Willingham’s investigation, I noticed that most of the real reasons Willingham argues that students don’t like school can be eliminated or reduced through continuous adaptive learning technology. In my first three posts of the series, I discussed ten ways in which adaptive learning can improve the classroom experience.
Here is one more reason that students find school distasteful – along with an explanation of how adaptive learning can help.
In my previous post, I described how dissatisfying it is to students when they feel like the hoops and hurdles they face are essentially arbitrary and culminate in nothing. Another factor that contributes to the sense that schoolwork is meaningless is the degree to which it is removed from expert work (real history, mathematics, poetry, science). No matter what their skill or age level, students wants to feel like their work matters and requires skill and focus. As Willingham argues, this detachment from expert work concerns educators as well as students: “If we’re not giving students practice in doing the things that historians and scientists actually do, in what sense are we teaching them history and science?”
Adaptive learning can narrow the perceived gap between school work and expert work in a number of ways:
First and foremost, the difference between student and expert thinking is that for experts, space in what Willingham terms “working memory” is increased because experts have automatized many of their “routine, frequently used procedures.” This affords them cognitive energy to solve more complex problems. Professional physicists, for instance, don’t need to look up basic formulas, and professional ballet dancers can observe a complicated bit of choreography and immediately replicate the series of movements. This kind of automatization happens naturally when students receive enough practice: after hundreds of problems, students don’t think twice about the product of 7 and 7 or the order of operations in which an expression should be evaluated.
An adaptive learning system can speed up the process through which these basic and routine procedures are automatized, by determining each student’s exact needs and serving up problems designed to target weaknesses on a precise level. In other words, an adaptive system can help students use their time more efficiently, allowing them to see gains in their ability at a more satisfying pace.
According to Willingham, expert thinking is characterized by an ability to transfer knowledge between domains, “access the right information” in a swift and accurate way, and formulate productive questions and hypotheses about new information. What exactly allows experts to do this? As Willingham points out, “it’s not just that students know less than experts; it’s also that what they know is organized differently in their memory.” Experts store knowledge in a way that emphasizes deep, functional, abstract relationships. So, instead of thinking of things in surface terms, experts think about pattern and structure, how each part relates to the whole. This allows them to do all the things experts do: use acquired skills in new contexts, locate and retrieve stored information, and process new material in a productive way.
A sophisticated adaptive learning system can identify students’ “blind spots” and get them to organize information in ways that were previously alien to them. For instance, a student who has trouble seeing the big picture can receive questions or activities that guide him to think in larger terms; another student who has difficulty memorizing what seem like isolated facts can be shown how those facts relate to overarching ideas.
Many students simply don’t know what real scientists, mathematicians, writers, and historians are working on. In schools, we place a mild emphasis on helping students consider future career options (such as “doctor,” “teacher,” “judge,” etc), but we do little to expose them to mature manifestations of the academic work they’re actually doing. We tend to think that expert territory is too complex or niche-oriented for students, so when students ask us the point of school work, we appeal to them on an economic, vocational, and practical level. We say they’ll end up on the lower rungs of society if they don’t master algebra or try to persuade them that studying trigonometry will help them pursue their dream of becoming a lawyer. Why not stimulate student interest by trusting the “interestingness” of the subject itself to engage students, by answering questions like “what’s the point?” with a real, robust answer? Why not show them that the fields they’re immersed in are so interesting that adults are working on them, too, and that their work infiltrates our lives on a daily basis? (The iPad they’re holding, for instance, employs some technology developed and patented by working physicists.)
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.) 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.
A student who, say, demonstrates a facility with language can be introduced to the work of certain contemporary, practicing poets, and based on his preferences, be introduced to another set of poets and so forth. This experience is much more mature and individualized than working through a static, printed anthology that features a limited number of canonical poets. 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.
For a more detailed article on how we can teach subjects in a more mature and meaningful way, check out my post, Teaching Math Maturely.
The most salient difference between student and expert work is the fact that experts produce original work in their field. While we can’t expect students to match the quality of such work (though we may be surprised with what they produce given the right stimulation), we can guide them in a productive direction by exposing them to expert work and directing them to opportunities for them to produce and showcase their own work.
Because it processes thousands of data points on student performance, an adaptive system can help students find like-minded peers and organize communities of learning (just as it can organize cohorts in the classroom). These communities might form within individual classes or schools or across districts and beyond. By tapping into arenas where they can learn from peers, showcase their own work, and receive feedback (from those who have no interest in grading them), students get into the habit of professional academic exchange at an early age.
Realizing that their work has an audience beyond the teacher is enough to motivate some students to engage deeply with their studies. And, oddly enough, participating in this sort of community is a more realistic vision of real academic work than the model we currently provide. It happens to be more satisfying and more productive as well.
In this week’s College Readiness News Roundup, check out our new infographic about college readiness, as well as articles about community college placement, college retention, and more.
1. The Unprepared Nation [INFOGRAPHIC]
The stats in our college readiness infographic are pretty sobering. Check out why preparedness matters — not only for individual students but for our country as a whole.
2. Community college placement falters
Jay Maheny weighs in on remediation requirements and placement in D.C.-area two year colleges, in this blog post from the Washington Post.
3. The Self-Absorbed Higher Ed System
Jeff Selingo thinks that American higher-ed institutions need to stop congratulating themselves on the fact that they’re in high demand, and start paying attention to how they’re serving American society.
This four-part webcast posted by AdLit.org covers the ins and outs of college readiness and discusses ways in which high schools, colleges, and parents can collaborate to ensure students are prepared for post-secondary education.
5. “Unlocking the Secrets of College Retention” Webinar
On Tuesday EdWeek held an interactive webinar about how nonprofits and colleges are aiming to improve college retention. The webinar was moderated by Caralee Adams, a contributing writer to EdWeek, and featured presenters Julie Kashan and Carla Wood. Check out the on-demand version here.
In the latest EdTweet Show, hosts Jen and Jess run through four of this week’s most interesting favorite edtech-related tweets — in less than 2 minutes!
Check out the video, and then read the full list of EdTech Tweets You May Have Missed below!
EDUCAUSE 2011 is finally here… and along with it, our final Speaker Spotlight post.
Bill Allison is the Director of Campus Technology Services at the University of California, Berkeley. He’s leading a session tomorrow (Thursday, October 20) entitled You Need to Go Mobile Now, but How? The UC/UCLA Mobile Web Framework. Bill was kind enough to answer our questions about the intersection of higher ed and mobile frameworks, EDUCAUSE, and more.
Missed our previous Speaker Spotlight and other EDUCAUSE-related posts? Find them here.
1. What is driving the need for higher education institutions to go mobile?
It’s simple – that’s the platform our constituents are moving to, in droves. Just this week Response magazine reported that mobile traffic increased 153% last year – with 87% of traffic coming from small screen devices. We see these findings mirrored in our own traffic and survey data as well.
Second, as the name suggests – mobile means conveniently sized, networked computing that travels with people everywhere. As an entire ecosystem of consumer services from Amazon to Zipcar has migrated to mobile platforms, people expect to do all their business there. For a university like Cal, going mobile enables us to effectively reach and engage with students, faculty and staff.
2. What are the biggest hurdles universities face in taking on mobile web framework implementation?
The good news is that implementing technology like the UC mobile web framework or commercial mobile services isn’t that hard in and of itself. Like most IT, the real barriers arise when a team sets out to enable features that require information and data from other systems. Integrating multiple data sets is required to deploy many of the more useful mobile applications. University mobile initiatives languish or drag on because of immature data governance models (read: politics), institutional risk aversion, and because many core systems, especially legacy applications, aren’t hooked up to modern data messaging and middleware technologies. These challenges are magnified for mobile applications that *write* to systems such as course enrollment as well as reading from them.
Most universities can implement a basic mobile platform itself in a matter of weeks (or even days) – especially for the basic elements of a campus primary mobile presence, such as http://m.berkeley.edu that offer campus maps, campus directory, bus schedules, news, events, links to university videos. After that, the next wave of capability will bring applications that provide on-demand information with minimal customization. One of the advantages I’ve seen with the UC mobile web framework is that it is designed so that the current maintainers of any web-based system can modify their application’s presentation layer and logic to bring mobility into their existing applications relatively easily. The framework itself is run centrally, but the implementation of mobile applications is distributed, and in parallel.
3. What have been your greatest lessons in developing the mobile infrastructure at UC Berkeley?
The power of a framework in our environment has been its appeal to a widely distributed set of stakeholders and developers, focusing them on being productive on a common technology. The risk before we had this common approach was that the developers and teams were starting to fragment into many divergent approaches. Now we have a common strategy that has rolled out across UC Berkeley (and now even more widely across the UC system), and we have focused a single team on improving that framework to leverage the investment widely to benefit everyone. The framework provides a lot of leverage in that it is a single service that solves a lot of developers’ needs in providing robust mobile content and applications – but the design lets distributed developers work in their existing programming languages with significant control over their content.
Another insight came out as we sought to get more widespread adoption of the mobile framework at UC Berkeley. We took an approach to training that centered around free classes and workshops in how to use the mobile web framework, coupled with incentives for class participants to take concrete and immediate action with what they learned in class. We hold contests for class attendees where they can win iPads as prizes. Response has been great, and staff are better retaining what they learn since they immediately practice what they learned in class. The net result is that the University has made rapidly accelerating progress in enabling mobile applications compared to where we were a year ago.
4. How do you envision a fully mobile-enabled future for higher education? How far away are we from that future?
We are much closer to this future than you’d think. With Google’s Chromebook and Android strategy, Amazon’s new Kindle with their family of “cloud” readers and players, and Apple’s iCloud the clear trend is for mobile devices as the tool to use cloud services, and the decoupling of the mobile device from the computer, which has been the paradigm since the Palm was introduced more than 10 years ago. The criticality of a particular physical device, including concerns like on-device encryption will diminish rapidly as convenience and the benefits of abstraction of these problems prevail. The expectation is that storage on devices will be volatile. While not common yet, there will be a diminishing need to have a separate computer and mobile device.
With that in mind – let’s step away from the technology side of the equation. A mobile enabled future means that once challenging problems – like making clickers available to everyone in classrooms, enabling a student to maximize their time by watching a video for a course on their bus ride home, or eliminating the strain of lugging around twenty pounds of traditional printed overpriced textbooks – go away. The most exciting part of the mobile future is that the focus will be less on the technology itself, and more on creative use of the devices’ capabilities — connected with the disciplines of the faculty and students using them. We’re now seeing the beginning of academic applications we never even dreamed of. One UCLA professor built an Android mobile application to crowd-source identification and tagging of native trees now being ported to the UC mobile framework. The volume of information she got from volunteers with mobile phones would have required an army of paid grad students in the past, and the IT resources required to make this all happen? Tiny.
5. Are there any sessions or speakers you’re particularly excited about seeing at this year’s Educause conference?
Yes – there are many. What I’m most excited about though is the amazing deep conversations I’ll have with colleagues from all over the country and the world. People in higher ed IT today work in a very collaborative field with some crazy challenges. Most of us are extremely energetic and committed to continually improving ourselves and the field. Our industry is quite different than the corporate world (where I came from originally), and we benefit from sharing and collaborating. One of my teams, for example, created the Kuali Ready business continuity tool for the University of California. At first we ran it just for Berkeley for about $100k a year, then we ran it for the 10 UC campuses for a significant savings. We are now a service provider through the Kuali Consortium, and run Kuali Ready on a SaaS model for over 130 institutions that benefit from lower insurance premiums and dramatically lower costs than doing it themselves – somewhere around $4k a year. In the current climate we get to be very creative, and nowhere is this more focused than when we all get together to compare notes.
You heard us right.
Attending EDUCAUSE 2011, and want a shot at a free iPad?
All you have to do is this:
1) Find one of our team members in the exhibit hall on Wednesday or Thursday. We’ll either be manning booth 1258, or walking around the hall carrying Polaroid cameras and small whiteboards.
2) Write down your answer to one simple question: What do you see as the future of education?
3) Let us take your picture. We’ll snap a Polaroid of you posing with your answer, then display it on a bulletin board at our booth.
We’ll automatically enter every person pictured into a drawing for an iPad, which will be held on Thursday afternoon. Afterward, we hope you’ll take your Polaroid home as an EDUCAUSE keepsake.
See you in Philly!
For all our EDUCAUSE blog posts, please click here.
Miss Part I or Part II of the series? Check it out here.
I recently read Daniel T. Willingham’s Why Don’t Students Like School?: A Cognitive Scientist Answers Questions About How the Mind Works and What It Means for the Classroom.
As I was reading Willingham’s investigation, I noticed that most of the real reasons Willingham argues that students don’t like school can be eliminated or reduced through continuous adaptive learning technology. In my first two posts of the series, I discussed seven ways in which adaptive learning can improve the classroom experience.
Here are three more reasons students find school distasteful – along with explanations of how adaptive learning can help.
If the aim of school is to make students independent from it (so they can apply what they learn in school to real-life situations), the processes of learning, problem-solving, and synthesizing matter just as much as the factual knowledge used to transfer ability in these areas. It generally works like this: having encountered a range of material, students start to recognize patterns, in both the subject matter and in their own learning. Of course, this abstract pattern recognition isn’t the whole point of learning; the ability to be concrete (to recall facts, execute plans, and work with different materials) is just as fundamental to the educational experience. What matters ultimately, then, is the ability to move seamlessly between pattern and detail, between the abstract and the concrete.
Students gain these skills, according to Willingham, by mastering detailed tasks (ex. revising a sentence in an essay), and then figuring out how these details fit into the whole (ex. understanding the way in which that revised sentence changes the essay’s overall argument). Willingham argues that teachers facilitate this cognitive “muscle-building” in several ways: they provide examples and ask students to compare them; they ask questions that prompt students to identify patterns and remark on structural qualities in the information.
Helping students gain these skills, however, isn’t always easy. As with most productive (and unfamiliar) work, there is often a general level of discomfort involved. The process can be slow and ineffective, especially when students in a class are at varied levels of understanding. Learners at either end of the spectrum are likely to be either bored or confused, and as a result are more likely to “check out” of the lesson and begin to harbor resentment for school in general.
How can adaptive technology help? By tailoring questions and examples to each individual’s level of understanding and learning style, an adaptive system can improve engagement and facilitate success. Specifically, an adaptive system is able to: a) insert reinforcement moments that prompt students to think about meaning, structure, and process b) draw abstract moments back to the concrete by requiring students to apply principles, theories, and formulas to the contexts of new problems, and c) track through data the efficacy of these shifts to optimize the flow of cognitive work for each learner’s individual style. Ultimately, students walk away not only having learned more and in a deeper way – but also having become more confident and engaged in their learning.
The controversial writer, John Gatto, famously posited that public school as we know it, with its rigidly segmented class day and byzantine rules, teaches students that no subject really matters beyond the forty minutes during which it is taught and that the lack of continuity between subjects and grade levels teaches students to accept “confusion” as their destiny. Regardless of whether you agree with Gatto’s assessment of public schools, student engagement can be strengthened if academic work is imbued with a sense of continuity and meaning. After all, as Willingham suggests, the hardest part of many cognitive tasks is getting geared up to start over or start up again. Nothing is more dissatisfying to students than feeling like the hoops and hurdles they face are essentially arbitrary and culminate in nothing.
Adaptive learning can assist in knowledge recovery and transfer, reducing the extent to which students feel overwhelmed by the introduction of a new type of problem, skill, or knowledge area. A finely tuned adaptive system can accomplish this by quickly reminding students what they learned previously (in the form that sticks with them the best), highlighting certain patterns in the material (or nudging students to grasp them) or bringing certain structures into relief (so that students are guided to what they should be focusing on), or maybe even re-introducing a student’s past notes and commentary at a later point. (Imagine that you were given eternal access to all the notes you ever composed and all the material you ever underlined–how would this change your learning?) The message this sends students is that their learning extends in unfathomable ways beyond the assessment at hand–that what they’re learning today will form the foundation of what they learn tomorrow.
As mentioned earlier, a lack of continuity between different learning episodes creates a sense of meaninglessness and implicitly teaches students that “nothing really matters.” What if, however, you could use student curiosity in one area to fuel interest in every other? What if the positive effects of every learning experience were capitalized upon exponentially?
In his book Disrupting Class, Clayton Christensen identifies the self-perpetuating cycle through which the curriculum and methods of instruction for various subjects are tailored for those who are gifted in them. Math classes, for instance, are taught by those who are gifted at math and through texts written by those who are gifted in the subject as well; and class itself is shaped by the questions and comments of gifted math students. (This leaves those who are not gifted at math feeling excluded and turns them off from the subject.) Imagine an alternative: what if you could use the confidence students develop in the areas in which they excel to help them learn in subjects for which they have less proclivity?
For the purposes of this discussion, I’ll introduce the “7 types of intelligence” that Willingham and other writers and researchers have identified:
How might an adaptive learning system allow individuals of the above intelligence types to harness their strengths to approach the study of, say, math differently?
An adaptive system could process each individual’s performance, activity, and preferences to deliver the same material in different ways. Someone who is, say, a “naturalist” might develop his math ability by using math to conduct experiments and test hypotheses about the natural world. Someone who excels in the “interpersonal” might learn by teaching others what he knows. And someone who is “musical” might use math to grasp the science behind harmony.
This differentiation is a fairly blunt-edged example of how an adaptive system might use a student’s strengths to remediate weaknesses (an idea that Willingham introduces and which adaptive learning can make a reality). It could happen in a more subtle fashion as well. If a student excels in rapid-learning problems but fails at projects that require long-term planning and study, an adaptive system might encourage him to segment the longer project into less-intimidating chunks. And vice versa: a student who has difficultly absorbing and processing material quickly might have more luck conceiving of the activity as part of a long-term project. The possibilities are endless.
The U.S. is facing a full-on college readiness crisis.
One-third of college students require remediation before enrolling in college-level classes. Of those students, one-half will never receive their bachelor’s degree.
What’s causing the problem — and what’s at stake? This infographic lays out the state of college readiness in the U.S. and explains why being prepared for college matters now more than ever.
Click the image below to go to the full infographic – The Unprepared Nation: College Readiness Today.
