eLearning Roadtrip

AAAS and the March for Science

Today's post is a PSA for the American Association for the Advancement of Science (AAAS). I would like to share the letter I received this morning from Rush Holt, the chief executive officer of the AAAS, talking about the upcoming April 22 March for Science. If you like science, you might want to get involved.  Here it the letter: Now more than ever, we must demonstrate the importance of science to policymakers and the public. As you may know, there is a great deal of energy associated with the March for Science, which will take place in public spaces around the world on April 22, 2017. Activities will include marches and rallies, open houses, children's science festivals and teach-ins. AAAS has now signed a Memorandum of Understanding to partner with and support the March for Science, and we are developing a wide array of activities for before, during and following the March. We believe it is incumbent on AAAS to help ensure the March has a positive outcome. We want the March to remain a nonpartisan, educational, diverse, and ethical expression of the value of science to society, and we want to make sure the momentum doesn’t disappear after the March takes place. Over my entire career, I do not believe I have ever seen such a large, spontaneous outpouring of support for the idea of science. We think it is perhaps the best opportunity in many years to communicate a positive message to the public and public officials about the value, utility, beauty, and power of science, engineering, and technology. It is an important opportunity to advocate for the maintenance, and even the defense, of the conditions necessary for science to thrive. If you are interested in learning more, please visit www.marchforscience.com. However, we also need your help. Here are four things you can do right now: Visit our Force for Science website to stay up-to-date on the issues, understand the role that science plays in policy, follow AAAS advocacy activities, and find ways to engage and take action. Share your thoughts and ideas on our MemberCentral Trellis group. A team of AAAS staff meets every day to plan our advocacy activities, and we frequently take these discussions into account. Help make AAAS a stronger Force for Science by making a tax-deductible donation to our Flexible Action Fund. Tell others about AAAS, what we’re doing and why you’re a member of AAAS. You will be hearing more from us soon about new advocacy efforts and how you can get involved. Thank you for being a part of AAAS and for standing with us at this critical time for science. Sincerely, Rush D. Holt Chief Executive Officer, AAAS Executive Publisher of the Science family of journals

(tap, tap) Hey, is this thing still on?

During the past 6 years I have had the remarkable experience of taking an idea about predictive analytics all the way from brain-storming with smart, crazy people who wanted to try something new and different with big data techniques, to getting multiple grants from the Gates Foundation to see if our idea worked. It involved founding a not-for-profit organization to validate our ideas in the commercial ed tech marketplace, to being acquired by Hobsons, an international commercial software provider.  

Not bad for someone who had really just wanted a voice in the learning analytics conversation that was more focused on students than VC.

Now that I've had a chance to get settled in a new research job at Hobsons,  I've been reflecting on whether or not this work I've been doing in analytics still counts as elearning.  I'm thinking that it does.  I'm thinking that now, more than ever, using digital tools to extend out expectations of learning is exactly the next phase in learning innovation we elearning advocates have been waiting for.

There are some who continue to describe elearning as the activities utilizing electronic technologies to access educational curriculum outside of a traditional classroom. In these cases, people are typically referring to an online course, program or degree. I get that. It helps define elearning as something distinct from other cognitive or pedagogical pursuits. Some people think of elearning as authoring, or collections of webcasts.  I've seen some people say that elearning is dying because mobile learning is about to replace it. 

For me, elearning is about leveraging technologies in support of designing, creating, producing, evaluating, distributing and managing learning experiences that engage and inspire, regardless of physical location or time. It's a much bigger tent than content or courseware.

My new job at Hobsons is a research job. I'm exploring learning impact and efficacy in the P-20 learning systems we depend on in the US to prepare our citizenry for college and and career success. I've been working on reports describing the impact of dual credit for accelerating college completion, on the impact of upward transfers from community colleges to completion institutions, on online learning efficacy, on the value of exploration and engagement in early childhood, the things we can do to support learning success among non-traditional students. 

I'm amazed to see, in all these discussions, the degree to which elearning has evolved beyond content and courseware. Today elearning is more about personalized, interactive, responsive experience.  If ed tech is where we concentrate on better tech tools, these days elearning encapsulates all we have learned to do to extend digitalization into our teaching and learning, education and training practices, constructs and values.

All this reflection made me realize I need a place to reflect on the transformative evolution of our practice as tech changes our expectations of our learning lives.

Looks like it's time to get back on the eLearning Roadtrip.

 

 

 

Big Data, Learner Analytics and Reflections on Student Success, Redux

This blog was first published at https://wcetblog.wordpress.com/2014/11/06/par1/

 

It Begins With Big Data

One of the surest signs that a technology trigger is starting its roller-coaster ride through the (Gartner) Hype cycle of innovation is when the name we all call that trigger becomes a part of the public lexicon.

Today, Big Data is an all-encompassing term used to describe data sets so large and complex that it becomes difficult to process using traditional data processing applications. A quick search of Google Trends shows that references to Big Data started to appear in web references, literature, and popular press back in 2007, after Tom Davenport and Jeanne Harris published their book Competing on Analytics: The New Science of Winning (Harvard Business Review, 2007). Today, after only seven years, a Google search on Big Data will result in more than 857 million search results being surfaced. Big Data is so pervasive as an idea that it has become a meme, standing for something even bigger and more transformative than the data themselves. The term has come to stand for the idea that the records of activity that we leave in the path of our various digital engagements is just waiting to be mined by service providers, beacons, and embedded code strings, all in the service of optimized, personalized experience.

This Big Data Landscape 3.0 graphic by Matt Turck et al provides a high level visual representation of the breadth and complexity of the Big Data landscape.  What is notable about this particular depiction is that very few of the companies in this graphic have very much to do with providing products and services for the post-secondary educational market in the United States. Some of the larger firms do have education lines of business, but these exist as part of a product company’s vertical market strategy rather than being explicitly aimed at big data services in education. The explanation for this apparent oversight is a simple one.  Although Big Data has certainly ramped up expectations of accountability and transparency in higher education settings, most of the data driving decision-making in higher education comes to us in columns and rows. Data sets that present in columns and rows can certainly be massive in number; however, data reported in columns and rows is still too small to be considered a true Big Data asset.

Nevertheless, meme of Big Data has been an effective catalyst to help people start to imagine what it will take to move away from authority-driven decision-making in post-secondary education and to establish a culture of evidence-based decision-making. But at a more systemic level, the ability to leverage insights to anticipate opportunities for optimizing effectiveness will be one of the key attributes demonstrated by data-savvy organizations and enterprises. How we license our digital textbooks will have everything to do with data related to use and student performance; the design of adaptive and personalized experiences will all depend upon data to filter, aggregate, assemble and exchange content, assessments and engagements.

 

Learner Analytics

So it should be no surprise that interest in learner analytics – predictive, inferential and descriptive alike – has grown steadily in recent years. The Learning Analytics and Knowledge conferences provided a venue for exploring dimensions of learning analytics research. The Society for Learning Analytics Research (SoLAR) helps explore the

role and impact of analytics on teaching, learning training and development. Purdue University’s Signals was among the first examples of using predictive analytics to identify students at risk, using a simple green – orange – red color scheme to flag students according to their risk probabilities. Sinclair College’s Student Success Plan provided early predictive case management support in the category for what is now emerging as Integrated Planning and Advising Systems (IPAS) tools and platforms. Rio Salado College used their PACE system to anticipate students at risk. Austin Peay University’s Degree Compass gave people an Amazon-like experience for course selection. Institutions including the American Public University System and the University of Phoenix made significant advancements in building sophisticated predictive analytics models to find students at risk. But it was the Bill & Melinda Gates Foundation’s investments in action analytics which helped jump-start and sustain multiple initiatives focused on building capacity to support using data to support and enable decision-making. In the post-secondary educational arena, these include Achieving the Dream ,Completion by Design, as well as multiple waves of Next Generation Learning Challenges awards.  PAR, the Predictive Analytics Reporting Framework , received its first of several round of funding from the Gates Foundation in May, 2011.

PAR – From a Big Audacious Idea to a Collaborative, Non-Profit Venture

For the past three years the PAR Framework core staff and institutional members have created one of the largest student outcomes data resources ever assembled, from the voluntary contributions of de-identified student record from each of our member institutions.  We learned very early on that the common data definitions created to facilitate the exchange of records also provided us with the lexicon required for talking with one another about student risk, persistence, and success between and within institutions. We provide members with comparative benchmark reports. We provide localized predictive models generate a risk score for each (de-identified) student in the sample for each of our member institutions, and with access to dashboards for student watch-lists for designated professional staff including advisors and faculty.

The PAR Student Success Matrix (SSM^X) then helps institutions comprehensively assess their student success policies, interventions and programs by organizing the wide variety of student supports – from orientation to mentoring to advising – into a systematic validated framework designed to quantify the impact of student success practices and determine the best support for students at the point of need.  The SSM^x also reveals gaps and overlaps in student support programs and gives institutions the tools to evaluate the efficacy of their investments at the program level.  The common PAR measures for assessing and predicting risk and the validated frameworks categorizing student support services create the mechanism to effectively measure the impact of student supports within and across institutions.  

Reflections

I was recently asked if I have been surprised by any of the things we have learned as PAR has evolved from a big audacious idea into being a learner analytics as a service provider for our members. I allowed that I have had three big surprises.

• One of my biggest surprises has been the realization that even the most finely honed predictions of student risk are of marginal value if predictions of risk are not directly tied to actions to mitigate risks before those risks become realities. I have come to understand that prediction is the first step in a virtuous cycle of evidence-informed decision-making. By starting with a prediction of risk one can identify essential success behaviors that have been shown to mitigate the diagnosed risk. From this second step in the cycle, and with a diagnosis in hand, it is possible to link students with interventions designed to address diagnosed risks before they becomes a problem. Measures for assessing the relative impact and efficacy of that intervention can be linked to predictions of risk, bringing the cycle to its completion.
• I have also been a little bit surprised that PAR’s common data definitions have turned out to be so strategically significant in our student success work. More to the point, I knew there were going to be essential for us to share data among multiple institutions. I just hadn’t realized that sharing our definitions would be useful for many others just getting started in analytics work. PAR’s common data definitions were recently identified as a key competitive advantage in this year’s Gartner Research Education Hype Cycle, 2014 Report . It is very satisfying to know that our efforts to create common data definitions have helped us communicate within and across data initiatives, with PAR’s openly published data definitions providing a stake in the ground for defining what we collectively mean when we talk about outcome measures and student success. More than 2,000 entities have downloaded our openly licensed definitions since we published it in 2013. PAR’s definitions have been cited in IMS Global’s Caliper specification,  and in Unizen’s organizing documentation. PAR’s common data definition gives data projects a foundation for interchange, operating as a Rosetta Stone of student success data.
• The third surprise, and perhaps the most satisfying one of all of my “big surprises” has been the degree to which educators, coming from all over the post-secondary ecosystem, will figure out ways to work together in the service of student success.We’ve seen that data of all shapes and sizes helps better inform the decisions we can make at ALL levels of the institution so that ALL education stakeholders –  students, faculty and administration are better prepared to succeed. Whether online, blended or on-the ground, whether state funded or publicly traded, whether we are two year or four year institutions, whether we are traditional or progressive – we know we can move the needle when student success is everyone’s passion.

The PAR Framework community is actively looking for forward-thinking institutional partners to join us in our efforts to launch a culture of evidence based decision-making in the service of student success. Please join us in Portland to learn more about becoming a part of the PAR community.

The #OEB14 Debate: Is Data Corrupting Education?

Online Educa Berlin is a major international gathering of online/elearning/distance learning/mobilized/datified learning innovators. This year I was a participant in the Online Educa Debate, an annual featured event of the conference. The house motion for 2014 was "Data is Corrupting Education". Inge deWaard and I were asked to speak in favor of the house motion; Dr. Viktor Mayer-Schönberger and Dr. George Siemens spoke in opposition. I have provided the full motion below, in italics, for your reading pleasure.

MOTION: The House believes that data is corrupting education.

• Is the invasive extensive profiling and rigorous achievement test, backed by for-profit organizations, skewing the means of gathering information and determining relevance?

• Will a focus on data corrupt the processes of learning, training and development or will it reveal new unimagined insights that will make way for streamlines, personalized and effective learning

• Is big data destined to be in the hand of a few, or will openness prevail for the benefit of all?

At first, I was completely nonplussed by this challenge. But the more I got my head wrapped around ways to support it, the more I stretched my thinking, which of course is the whole reason one does these things. Collaborating with Inge was delightful; having the opportunity to present with Viktor and George was a real pleasure. I am very pleased to have participated.

The video of the Debate will be available soon, and I will share it when available. It was lively discussion, among we debaters and with participants from the audience. For a while I even thought the crowd was with Inge and me,  but I have to admit, even I would have had a tough time voting in favor of the motion as written.

While we lost the vote, I think we helped remind people that we all need to be mindful about the implications for unexpected consequences that innovative work may bring to practice. In the sprit of sharing have posted my notes of my opening argument, below:

Higher education has typically been organized around authority-driven models of decision-making; that is, the more senior among us are generally afforded positions of authority based on experience and expertise. The emergence of analytics tools and platforms are making it very clear that data can provide an even more complete, and certainly a more nuanced picture than expertise alone. 

Educators are just beginning to understand the IMPLICATIONS of data analytics, where mining data for insight to support decision-making is the focus of exploration. While it is clear that there are great opportunities for gleaning heretofore unimagined insights, the fascination with data makes many uncomfortable. We have come to realize that the patterns of our data generation, our online engagements, transactions and interactions, tell people things about us or things we are doing that that we never could have anticipated or expected.  It means that intelligence about us can be gleaned by those who have access to our various data streams. Are you aware of all the stories that your data are telling other people about you?

Our current fascination seems to be about data as a meme – an idea that spreads from person to person within a culture. Data-as-meme, especially BIG data, has come to stand for something bigger that the data themselves. It stands all of our excitement about the possibilities that they offer for risk mitigation and personalization. However it also stands for the doubts and fears about how all these data are going to affect how we do our work, how we go to school, how we live our lives, how we engage as citizens. 

Data without context has marginal value. However, data WITH context is information. Information is power.  And as the British historian Lord Acton reminded us, Power corrupts. Absolute power corrupts absolutely.  http://www.acton.org/pub/religion-liberty/volume-2-number-6/power-corrupts

Simply stated, corruption is the self-serving deviation from an ideal. If one accepts this simple description, then one CAN make a case that data may very well be used to less-than-ideal ends. It might mean that we actively exclude some learners at the expense of others. We may push people of varying predicted skill levels into programs of study based upon test results, rather than on personal passions and interest. Will data have an effect the way that we approach our pedagogies? Absolutely. It this going to change how educational professional approach our work? Absolutely. How do YOU feel about instructional design as a data driven algorithm? How about licensing digital content where the price point is determined in part to how well students do in the class where the content is used? How do faculty feel about the prospect of expectations that we will be lurking in the lives of our students to see what and how they are doing at every single moment? How do YOU feel knowing that students aren’t going to be the only ones who are going to be tracked and measured in these ways? 

I don’t think that data, by themselves, have corrupted, are corrupting or will corrupt education.  I do worry about uninformed, naïve or even nefarious uses of data. I worry about data being used to punish, to limit access, constrain or restrict participation.

While I am excited about the possibilities for personalized, customized learning experiences, I am also fairly certain that educational practitioners who use data badly, non-transparently, or naively are very likely going to give data a very bad name.

Who Owns Your Data?

Who actually owns your data?  At first the answer to this question may seem obvious - of COURSE we own our personal health records, education records, financial records... 

But do we, really? Data ownership presumes use rights, who can share what, with whom. What about those education records that reside in someone else's learning management system? Judging from the recent news story about student records being sold as part of ConnectED's bankrupcy settlement, it is clear that some companies believed these records were theirs to sell.

Stories like this, and of InBloom's implosion over the prospect of sharing student data with commercial service providers, have even resulted in bans on data sharing that have emerged in some states. But data sharing according to what?

What about the data captured on our mobile devices? Does it belong to the individual, the carrier or the device manufacturer? What about our social media engagements and interactions? Despite the number of well-intentioned postings of declarations of data ownership I've seen on Facebook, it seems clear that if one posts items on a free internet services site page to share, that we may not be the only one sharing them.What about all those data left behind when we complete a transaction on Amazon? Don't you ever wonder about how those companies from whom you buy your various consumer products know enough about you to send you a birthday card???

Have you every seen how many companies are sucking up the digital crumbs of your online interactions?  (Hint: Download the Ghostery, Inc. client to your browser. You might be very surprised to see just how many analytics, advertising and beacons links are paying very close attention to what you are doing.)

This brings me to two big observations:

(1) Privacy, as many of us have known it, is a thing of the past. It is extinct. When one considers the number of sensors, transponders, transmitters and cameras we encounter on a daily basis, it is very clear how easy it can be to reconstruct what one is up to, should one be a "person of interest". On the positive side, it will hopefully keep people honest in their dealings and transactions, since we can all check up on each other. On the continued positive side, it represents opportunities for convenience and customization, with services tailored to our uses and preferences.

(2) Nobody owns all our data. Not even us. It is data anarchy out there. While we can hope, we can't expect that some intra-global agency will make overarching rules that everyone will follow and all will live happily ever after. We are all going to need to know enough about the benefits and possible risks that sharing data brings to make smart decisions about the data we are willing to share, and what we expect to get in return. Most of us accept some exposure in exchange for the convenience that data sharing affords us; but most of us also get very uncomfortable at the prospect of a bunch of code jockeys deciding to hack a cloud service hosting our digital stuff and sharing our private information with the world.

Data-readiness means knowing enough to pay attention  - to security settings, to filters, to groups, to passwords and shared information - so that we know what is really going on with our digital data, knowing what and how we are sharing, and with whom. THEN we can make decisions based on evidence, because THAT is the whole point.

 

For my .EDU friends: Here's How Venture Capital Works

The education sector's current fascination with innovation, distruption and entrepreneurship is being fueled by the emergence of start-up commercial ventures that create new products and services as solutions for big hairy audacious problems plaguing today's schools and students. 

An April, 2014 post by CB Insights noted that investments in education technology hit a record high in Q1 2014 as investors poured more than $559 million across 103 deals in the first three months of this year.  Of note, Ed Tech funding in Q1 2014 represented nearly 45% of total funding to the sector in all of 2013. The record quarter comes after Ed Tech investments hit $1.25B across 378 deals in in 2013, the second-straight year of over $1B invested across the Ed Tech sector.  With everything from MOOCs to tutoring marketplaces to standardized APIs for K-12 schools getting funding, investors are clearly bullish on the education industry.

So what could possibly be wrong with getting rounds of other people's money to help you grow your big educational idea into a product that becomes the NEXT BIG THING?

Well, nothing...except you need to pay back all the money that everyone had given you. With interest and at a prenegotiated rate of return. That means that once you take someone else's money as an investment in your hot shot edu-preneurial company, EVERYTHING you do is going to be focused on paying your investors back. 

Aral Balkan recently blogged about his experience with a new social media platform called Ello. His post caught my eye because it included a pointed summary of how venture capital works. Having just spend the past several months at various educational technology meetings and trade shows where the buzz over acqusitions and investments is ever-present, I found myself sharing the following paragraphs with my friends from education who may not have had the experience of working with investors:

"Here’s how venture capital works: you go to an investor, before you’ve even built the thing you’re building and you tell them how you’re going to exit. It’s called an exit plan or exit strategy. You tell them, for example: “Hey, we’re going to get 100 million people using our new platform in two years time, how much will you give me for 100 million people?” And they go “Umm, we’ll give you this much for 100 million people because we’re pretty sure we can get that amount back several times over when we sell those 100 million people in an exit either to another company or in an IPO.”

"When you take venture capital, it is not a matter of if you’re going to sell your users, you already have. It’s called an exit plan. And no investor will give you venture capital without one. In the myopic and upside-down world of venture capital, exits precede the building of the actual thing itself. It would be a comedy if the repercussions of this toxic system were not so tragic.

"Let me put it bluntly: if a company has taken venture capital, you have already been sold. It’s not a matter of if, it’s simply a matter of when. (Unless the company goes under before it can exit, that is.)

"A venture-capital funded startup is a temporary company that has to convince enough people into using their platform so that they can make good on the exit they promised their investors at the very beginning. It is the opposite of a long-term, sustainable business." (Italics are mine).

You can read Aral Balkan's complete "Ello, Godbye" blogpost by clicking here.

I draw your attention to this article because every time I hear someone confide that their platform provider is a rising VC funded firm that is going for its next round of money, I wonder if these education customers understand the degree to which EVERYTHING their vendor provider must now do is focused on meeting their investors' expectations.

What an earthquake taught me about Twitter

We were rudely awakened this past Sunday morning when an earthquake threw us out of bed at 3:20 am. It was a big one. The roar of the quake, the smashing glasses, dishes, and pottery, accompanied by the sound of exploding transformers and the splash of hundreds of gallons of water being thrown out of the pool, was deafening.

And then the shaking ceased, the noise stopped, and it was all over.

Of course, we knew what had happened. We live in Northern California. People who live in "earthquake country" are prepared for earthquakes, or at least we like to think we are. Flashlights in every room, with shoes at bedside (to avoid bare feet meeting broken glass), 5 gallons of water and food/shelter for 72 hours. 

What we were NOT prepared for is the shock of sitting in the silence and the dark in the immediate aftermath of an earthquake that throws you out of bed at 3:20 in the morning.

The wifi and landline phone were dead; we had no power.  But I had a fully charged mobile phone and working cell service. I turned to Twitter and within about 15 seconds I learned that we had just experienced an 6.1-on-the-scale earthquake, the epicenter of which was just 9 miles to the southeast of where we live. I saw that friends in San Francisco and San Jose had been awakened by the quake, too, and had already checked in to see if we were okay. We saw that our friends in Napa had been slammed, and were advised not to drive over to help because the roads had been damaged. 

As the morning went on, we eventually posted pictures on Instagram and messages on Facebook to let friends and family know that we were alright. But what I learned in those first moments, when immediacy mattered most, was that in case of emergency, when WE needed targeted information, Twitter was where we turned first. 

 

 

 

 

Alan Greenberg: Blackboard Reboots

I admire and respect Wainhouse Research analyst Alan Greenberg, so was quite interested to read his recent post in which he reflects on changes that Blackboard introduced at this year's Blackboard World. I, too, was impressed with the "new Blackboard" while at Blackboard World this year, and am really looking forward to diving into some of the work we (the PAR team) will be doing with the Blackboard team.

Please refer to Alan's newletter for the complete article and especially for his take on why Blackboard's changes are a big deal.

Here are a few "nuggets" from his post to whet your appetite for more......

Blackboard Reboots @ Blackboard World
Alan D. Greenberg 

In an effort to give institutions and schools more value and greater exposure to its technology sets, Blackboard has completely revamped its product line, now integrating together its product portfolio with a totally new user experience (UX) and a declared focus on learners. All teaching and learning solutions will now include Blackboard’s “base” learning management system (Blackboard Learn) and provide a full-featured online environment with course delivery, synchronous collaboration tools, mobile apps, and integration with and access to content. Technology adoption services are also included.  

A tiered set of solutions are aimed at helping the educational user community address challenges such as retention, student engagement, learning analytics, communications and standards preparedness. There are four higher education teaching and learning solutions, four higher education campus commerce and security solutions, and five K-12 solutions that center on teaching and learning and community engagement. The company is considering adding more solution sets for international markets.

Blackboard also plans to launch a new version of its learning environment that is delivered via the public cloud. Now the company is providing customers a full set of options: self-hosted, hosted in Blackboard’s private cloud, or via the public cloud. Word is this public cloud offering will be fully available sometime this month or in September. Being piloted in the public cloud is Blackboard’s Open Education powered by Blackboard, also announced at Blackboard World. This is a course platform that allows teachers and faculty to run public online courses and/or Massive Open Online Courses (MOOCs).

The company has created a customized learning and analytics offering available to the 259 higher education Internet2 members. The Internet2 NET+ offering will not only provide access to Blackboard’s solutions, but also will include robust analytic and learning outcomes tools, a cloud-based, cross-institutional content repository that supports most competitive LMS’s, and help desk and adoption services. Cornell University; the University of Nebraska — Lincoln; the University of Maryland, Baltimore County; the University of Texas at Arlington; and Virginia Commonwealth University had representatives who guided and shaped the Internet2 NET+ certified solution.

At the core of the offering, which is fully deployable now, is Blackboard’s Learning Insight solution, which is a full-featured online and mobile version of Blackboard Learn LMS and Blackboard Collaborate.

(Ellen's aside: yes, this means that Collaborate works in the browser. NO more Java downloads!)

FOR MORE, click here and scroll down for Alan's full article.

Driving the #LearnerAnalytics Bus

The message emerging from this week's University of Maryland University College (UMUC) Learner Analytics Summit (#innovatelearning) was explicit and to the point: Forward thinking instititutions are stepping up to opportunities for improving post-secondary experiences for students using data to inform proactive, evidence-based decision-making. 

No doubt, we all hope that the answers to questions we can't sometimes even articulate yet are going to emerge from dashboards and apps, fully formed, as if from the Magic 8-Ball. Sadly this is not the way things work. We need to build capacity for decision-making in a data-informed world just as much as we need to figure out the exo-structure required for systems to interoperate and for intelligence to be shared.

Matt Pistilli (Purdue University) tweeted one of the great tweets of the event when he noted that "the data do not speak for themselves. I have, in fact, been in the room with them, and they did not speak a word #innovatelearning".  It's a great reminder that if we are going to get anywhere quickly on this journey, we are going to need to be the ones driving the bus on learner anaytics adoption and creating organizational cultures of evidence-based decision-making.

Malcolm Gladwell has talked about tipping points, those moments when a new practice or innovation, idea or trend emerges into public consciousness. Learner analytics are at the point of tipping into public consiciousness in the war on student loss.  

Congratulations to Karen Vignare and her team for convening us, to Bill Moses and the Kresge Foundation for the means to do so, a high-five to Linda Baer for her guiding hand, and thanks to all the presenters and participants for helping us start figuring out how to drive the learner analytics bus, on the journey toward Evidence-Based Decision Land. (Cue unicorns, bluebirds and rainbows.)

PAR Framework: Learning Analytics as a Service for Higher Ed

Thanks to Libby Nelson's great article Big Data 101: Colleges are hoping predictive analytics can fix their dismal graduation rates, we have been fielding a lot of questions about the PAR Framework. Here is an update on who we are and the work in which we are engaged.

MEET THE PAR FRAMEWORK - The Predictive Analytics Reporting (PAR) Framework is a national, non-profit provider of learning analytics as a service. PAR brings 2-year, 4-year, public, proprietary, traditional and progressive colleges and universities together to collaborate on mitigating student loss by identifying the effective practices that support student progress toward their academic goals. The PAR Framework offers a unique multi-institutional perspective for examining dimensions of student success that will help improve retention in US higher education.  PAR is distinguished among the many data analytics solutions emerging in the education domain by its common, openly published data definitions and student success frameworks.

HOW PAR WORKS - PAR uses predictive analytics to deliver tools and reports aimed at improving academic success for students in online, blended and on-ground programs. PAR members receive flexible predictive models, cross-institutional benchmarks, and an intervention insight toolkit supporting a student success methodology that links predictions with interventions and student supports.

• PAR institutions provide a full set of anonymized undergraduate student data that allows for comparative investigation of student success trends over time, at the individual student, course and degree level. (PAR members provide periodic updates of their data ensuring the ability to measure changes over time, the impact of student interventions and enabling the predictive models that PAR produces to be adjusted and tuned for current data.)

• In return, PAR institutions receive actionable reports and student watch lists developed using predictive analytics.  Once institutions deliver their student-anonymized and institutionally de-identified data, PAR researchers and data scientists use statistical and data mining techniques to identify and reveal the factors that most impact student success in the combined dataset and the individual institution level.  Members quickly receive institutionally-specific models that predict the likelihood of each student achieving institutional specific milestones. These include but are not limited to passing a course or courses and being retained at the institution.  Member institutions also receive academic models for their institution that include detailed student level data to create watch lists, structure interventions and inform local decisions about the factors identified as having the greatest impact on student success. They also receive institutionally anonymized and de-identified findings from the entire dataset that yield over-arching insights about retention patterns in U.S. higher education to inform practice and policy.

PAR TOOLS FOR BENCHMARKING:

• The PAR Student Success Matrix (SSM^X) helps institutions comprehensively assess their student success policies, interventions and programs. The SSM^X makes it possible to organize the wide variety of student supports – from orientation to mentoring to advising – into a systematic validated framework designed to quantify the impact of student success practices and determine the best support for students at the point of need.  The SSM^x also reveals gaps and overlaps in student support programs and gives institutions the tools to evaluate the efficacy of their investments at the program level.  
•  Quantified intervention effectiveness results. The common PAR measures for assessing and predicting risk and the validated frameworks categorizing student support services create the mechanism to effectively measure the impact of student supports within and across institutions.  PAR member institutions are encouraged to take part in quantitative, closed loop field tests, measuring the effect of targeted student supports (e.g. tutoring, student services, email, text messages alerts) against at-risk students.
• Institutional benchmarks. PAR Framework members receive institutionally-reflective and cross-institutional benchmark reports of student level outcomes such as retention, successful credit accumulation, academic performance indicators, and earning a credential.

COMPREHENSIVE INSIGHTS BASED ON VARIATIONS IN:

• Student demographics
• Financial aid factors
• Traditional vs Non-traditional status
• Full-time/Part-time status
• Major/Program pursued
• Credential pursued
• Online vs. On-ground vs. Other modalities of course taking
• Prior academic history

PARTICIPATION IN A COLLABORATIVE COMMUNITY OF EXPERTS: PAR Framework members represent an engaged community of forward thinking institutions, each with perspective and practices around student retention, progression, and completion. PAR membership provides institutional leaders with the models, technical support, services, and guidance needed to evaluate techniques, tools and solutions used to improve student outcomes. PAR provides the collaborative environment needed to support policy implementation and effective practice guidance necessary to capitalize on the power of data-driven decision-making for improving student success in reliable, repeatable, scalable ways.

 For more information about joining PAR, please contact us at [email protected].