eLearning Roadtrip

When I Think of Student Success

The tendency to link software with student success has accelerated since 2012, as the educational technology sub-sector known as "integrated planning and advising for student success" has gained prominence through a series of grants funded by the Bill & Melinda Gates Foundation to colleges and the tech partners they have selected to help the colleges improve student performance. These days, more than 100 companies offer integrated planning and advising technology platforms and tools for student success includes that serve a wide variety of functions, from deciding where to go to college, to facilities and course scheduling, from helping students select select majors to staying on track with program completion. Student success is a huge part of the current ed tech market scene. (Disclosure: In my current day job, I work for a company with a large part of its business dedicated to Student Success.)

EDUCAUSE has led efforts to help institutions explore both the promise and the risks of Integrated Planning and Advising for Student Success (iPASS). Per their website:  "iPASS is an integrative approach to student success that promotes shared ownership for educational progress among students, faculty, and staff. It encompasses services that help students formulate and advance toward educational goals, including advising, counseling, progress tracking, and academic early alerts. iPASS technologies can contribute by documenting and tracking students’ educational plans, improving data analysis, offering self-service resources that reduce advisor workloads, and triggering interventions based on student behavior or faculty input."

There have been two Gates Foundation-funded rounds of grant-making by EDUCAUSE in support of student success. The first was the Integrated Planning and Advising System project, designed to get schools and their tech partners to collaborate on getting platforms and practices to work together better. I was involved in the first IPAS round when the Predictive Analytics Reporting (PAR) Framework was contracted by the Gates Foundation to evaluate the 19 "student success + software initiatives" that were funded in that first go-round. We visited the 19 IPAS campuses that were working with specific tech company partners to further the implementation of a tech platform that could help campuses with student planing and advising platforms. PAR had used the PAR Student Success Matrix we had developed to help our own PAR members make the connection between predictions of  student risk with selection of interventions. We were able to determine and demonstrate measured effectiveness with particular groups of students under particular circumstances - sort of like getting the most effective prescription for dealing with a particular medical condition. We had been excited about our own results; the fact that we were going to be able to evaluate student success impact with our matrix of time-in-program and actionable predictors with a broader set of institutions was thrilling.

By the time the second round of grants were funded, the name of the initiative had become "integrated Planning and Advising for Student Success: iPASS". The connection between tech and advising (e.g. watchlists, early alerts), tech and planning (e.g. curriculum pathway planners, strategic scheduling), program fit and course navigation (e.g. obstacle course GPS), integrating predictive analytics with real-time student activity stream data.) As it happens, the iPASS initiative helped spark PAR Framework's acquisition by Hobsons in January, 2016, when two state systems of education proposed integrating PAR's predictives and intervention measurement with Starfish's real time student activity data stream used to build student watchlist/advisement capabilities as part of their iPASS grant proposal.

5 years, two major initiatives and several rounds of small planning and implementation grants later, EDUCAUSE has produced a library of student success resources for educational practitioners, on everything from "Things you should know about IPAS" (yes, that acronym is different than in the previous paragraph; more on that in a bit) to implementation handbooks, data tables and evaluation reports. and  from https://library.educause.edu/resources/2013/8/integrated-planning-and-advising-services-ipas-research.

It has been great to be a participant in this wave of innovation in education, where data is being used as currency for more effective decision-making in schools and colleges. No doubt, when we look back on how decisions were made in 10 years from now, we are going to wonder how we managed to live without platforms that help visualize patterns, red-flag student risks before they become problems, provide a 3D map of an academic transcript that can cross-walk among formal and informal learning experiences. 

Even so, I need to say something.

When I think of student success I never, ever think of software first. 

Software can make things easier to do, of course. It offers far better ways to keep track of searchable information requiring any manipulation. I'd rather build formulae in a spreadsheet, and have huge collections of data fueling explorations that report results in approachable visual reports and dashboards. Learning management and content management and curricular tracking, assessments and performance support are all more available, more accessible and more targeted at specific points in the educational experience than ever before.

Just as data associated with online and digital transactions help inform marketeers of our retail intentions even before we know for certain, ourselves, patterns of students behaviors can raise red flags before students are even aware that they may be drifting into dangerous territory for "students like them".

Technological inventions, applied to performance and learning problems, can help solve those problems. In doing so, the inventions become innovations, human ingenuity applied to solve performance problems in novel and compelling ways.

Innovative educational entrepreneurs  - "edu-preneurs', if you will - see an opportunity for tech in schools and are compelled to create products that promise to disrupt education as we know it. They point to deep data science and predictive analytics as if these analysis techniques are beyond the norm.

It's seems important to remind ourselves that in education, the most most important innovator in this value chain are not necessarily the ed tech entrepreneurs. Rather, the ones on the front lines of teaching and learning, supporting student achievement putting new tech tools to the test in the service of student success are todays true innovators. 

It's important to have shared articulated expectations of "the norm", so that one can identify those not yet achieving at that level, and how one inspire those working "above average" to continue to improve. Common expectations of meaning and outcomes are essential elements of student success that gives all students a shot at achieving their dreams. We need to include ALL students in our considerations of success, not just those attending college for the first time, going to school full time, or living on campus. We need to keep the student at the center.

With thanks to Kalah Burks and Rebecca Wheeler, University of South Alabama, for the great conversation that shook this post out of my head.

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.

 

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].

 

Karen Swan and the Amazing Multi-institutional PAR Student Success Matrix

Back in June 2013 the PAR Framework openly published an intervention assessment framework called the Student Success Matrix to help PAR members figure out how to respond when confronted with predictions of which students were likely to drop out. "What should we do to respond to these predictions showing our students at risk?" our members asked.  At that time Dr. Karen Swan, Stukel Distinguished Professor of Educational Leadership at the University of Illinois Springfield, reminded us that there are already lots and lots and lots of things that academic advisors, faculty and other student success professional already do to help students succeed in school. Going back through the professional literature, she and Dr. Peter Shea, SUNY-Albany, helped us find models of student engagement and participation relevant to our work at hand.

Karen proposed a framework to map interventions using predictor behaviors of success and points in the academic completion cycle.  Over the past year Karen and Peter have worked closely with Sandy Daston, PAR Framework Director of Student Success and the entire PAR SSMx working group (Hae Okimoto, University of Hawaii System; Jennifer Freed, Dan Huston, and Fermin Ornelas, Rio Salado College; David Shulman, Broward College; Denise Nadasen, Jack Neill and Karen Vignare, UMUC; Patsy Moskal and DeLayne Priest, University of Central Florida; Michelle Wiley, Penn State World Campus; Mysti Reneau, Community Colleges of Spokane) to refine processes and tools for promoting intervention measurement. This group of experienced educational practitioners and decisionmaker has helped determine what can actually be done for student at risk - even down to knowing how to support those 37 students that are most likely to fail Math 101 this semester. 

PAR institutions have begun the process of inventorying, tagging, describing measuring and evaluating literally hundreds of interventions currenty in use on campuses to help student succeed. Thanks to the SSMx we have accelerated the process of connecting predictions of risk to the interventions that will mitigate risk. 

This post is to acknowledge Karen's leadership and contributions to PAR's thinking about using predictions as cataysts for taking action, and for linking interventions to predictions to underscore the importance of linking predictions to action.

 

When I realized how easy is was to get started with learning analytics, I laughed out loud

I've always wanted to write a headline like that.  

If you are like me, any link suggesting that learning analytics can be easy is worth a quick look. Even if only to sneer at the temerity of whoever would say that. So, thank you for being curious enough to click. Now that I have your attention...

Getting started with a learning analytics initiatve doesn't need to be hard. What's hard is sustaining a learning analytics initiative IF one is only doing something, anything, as a pet project, an exploration, or a trial run because someone gave you free software. 

To paraphrase the incomparable Tina Turner, we're gonna start this learning anaytics conversation nice and easy (don't worry, no dancing required). Here we go. Five quick easy steps to get started:

(1) Start with a real problem to solve. Scope your problem statement so that you maximize your probabiities of  success. Do you have market conditions to address, sneaking suspicions that there might be trouble in River City that you'd better find out about before someone else does? Maybe places where you are pretty sure you can do a better job? You might want to think of something that will help you test the waters of change without putting yourself at risk of total humiliation if it doesn't work.

(2) Figure out who needs to be involved in creating the business case to get this problem solved. How much of a priority is solving the problem you identified going to be for others in your organization? Who need to be engaged and on-board to get this done? 

(3) Determine what you need to answer questions related to solving this problem. What information do you need? Who owns it? Where does it live? How are you going to secure it? How will it be analyzed? Who can see it, work with it, publish it? How will results be used? How long will the results live, and who will have access?

(4) Determine the necessary project talent to make sure you can get the work done. It's not just about having access to people who understand how to work with data. It's about having the right team in place across the board who can execute to plan and deliver results, and making sure that you have planned for enough of their time to be effective. How will you find them? What will you need to attract their attention and keep them engaged?

(5) Develop a bottom-up budget to see if you can afford to do what you want to do. Odds are, you will need more money than you have on hand. (Well... odds are that you will end up needing more money than you think you do, too, but that's a conversation for a later time). So, where might you find the funding to do what you want to do? Who else might be interested in getting involved in your great adventure?

Obviously each one of these steps can take on a life of its own once you get going, but as a place to begin these are a great way to get your arms around your big idea and see if its possible to move ahead.

Now that Predictive Analytics are Hotter than Ever

Now that predictive analytics are hotter than ever in higher education, it's probably a good idea to review a few fundamentals for those who are just starting to sip from the cup of possibilities.

WHY PREDICTIVES? Predictive analytics have been used in a wide variety of settings, including higher education, to manage finances, inventory, operations, assets and resources. Increasingly, higher ed institutions are turning to business intelligence for enrollment management and student recruitment. The next great wave for predictive analytics adoption in higher education is focusing on institutional performance outcomes and individualized student success. 

WHY COLLEGES NEED TO CARE: The more that colleges and universities are being held accountable for achieving specific performance outcomes (e.g., improved student retention, better completion rates for college students), the more predictive analytics will be touted as a solution for anticipating risks likely to be encountered while trying to achieve those outcomes. 

SIMPLY KNOWING WHO IS AT RISK ISN'T ENOUGH. Knowing how to mitigate risks and how different students can be better served with targeted interventions and support makes predictions actionable. Predictions without action don't really matter very much to anyone.

BEWARE THE BRIGHT AND SHINY: It's going to be a little bit like the early days of the first dot.com out there, with lots of  solutions available and even more promises about all the great things that those interventions and solutions can do (!!!). How will you know who to believe? How will you know what's right for you? You owe it to yourself to be informed.

THERE IS NO SINGLE "RIGHT WAY" TO CONDUCT PREDICTIVE ANALYSES. There are many techniques used to conduct predictive analyses. Typically one chooses the technique or techniques likely to yield results for the kinds of predictions one wants to make.

For example, if you are interested in predicting a discrete attribute you might be likely to use techniques including Logistic Regression, Decision Trees (CHAID, CART, Random Forest), Naïve Bayes, Support Vector Machine, Survival Analysis or Neural Networks. If you were more interested in predicting a continuous attribute, you'd look toward techniques such as Multiple Linear Regression, Time series or Decision Trees (CHAID, CART, Random Forest). If you were most interested in common groups, you might consider Hierarchical k-nearest neighbors, Neural Networks or Decision Trees (CHAID, CART, Random Forest).

Selecting the right techniques used for conducting predictive analyses have a lot to do with knowing the questions that the predictions will help answer, or the performance problems that are likely to be solved. Speaking of which...

START WITH A PROBLEM TO SOLVE: Sorry to burst your bubble but predictive analytics don't work quite like Magic 8-Ball. Not even Hadoop technologies do that. Answers simply do not emerge fully formed from the mists of your analytic techniques.  It's easier to focus on solving a problem (e.g. what causes students to drop out? Are these causes common in all settings?) or finding a new opportunity (e.g. what motivates students to complete courses faster?). Your problem statements and queries will help you focus on finding data sources and selecting techniques for analyses that are likely to reveal the patterns you seek.

Back on the Road Trip

Learning anaytics are catalyzing transformative conversations about completion and retention, competencies and ethics, pathways and profiles, micro-credentials and modalities. They are going to have tremendous impact on just about everything that learning technology professionals do. More to the point, they are going to define much of what we do and how we do it. 

You might be wondering what this has to do with elearning. One of the little secrets about predictive analytics is that predictions are useful if one takes actions to address whatever weakeness or opportunities that predictions reveal. Going forward, the real action will not be around predictions, per se, but on taking action to address the risks and opportunities that predictive analytics reveal.

These days this has a lot to do with what "big tent" elearning offers, at least the kind of elearning that delivers on the promise to personlize and motivate and pace, featuring learning technologies from interactive pdfs to wearable apps to immersive environments and adaptive pathways and everything in between.

And with that, we are back on the Road Trip.