e-Literate

Present is Prologue

Tag: analytics

  • Recommended Reading: Signs of Restraint in the Analytics Hype Machine

    Recommended Reading: Signs of Restraint in the Analytics Hype Machine

    Mike Sharkey’s recent post on the Blackboard blog site, “Analytics isn’t a thing,” triggered by an epiphany he had while reading the latest NMC Horizon Report, suggests that we might finally be seeing a maturation in the much-hyped analytics space. Rather than viewing analytics as a product category in and of itself, Mike concludes that the market is finally embracing analytics as a tool that can help solve discrete problems in specific situations. He writes:

    “My point is that we shouldn’t be “selling analytics” and customers shouldn’t be looking to “buy analytics.” Analytics isn’t a thing. Analytics help solve problems like retention, student success, operational efficiency, or engagement.”

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  • Analytics Literacy is a Major Limiter of Ed Tech Growth

    Whatever else you think of the election, it has been the mother of all teachable moments for many of us. It has raised questions about what we thought we knew about our democracy, our neighbors, our media…and apparently learning analytics. The shock of the polls being “wrong” has raised a lot of questions about how much we can really trust data analytics. Audrey Watters has written the most fleshed out critique that I’ve seen so far. But Dave Cormier tweeted about it as well. And I have had several private conversations along these lines. They all raise the question of whether we put too much faith in numerical analysis in general and complex learning analytics in particular. That is an excellent question. But in doing so, some of these arguments position analytics in opposition to narratives. That part is not right. Analytics are narratives. They are stories that we tell, or that machines tell, in order to make meaning out of data points. The problem is that most of us aren’t especially literate in this kind of narrative and don’t know how to critique it well.

    This is going to be a wide-ranging post that goes a little lit crit at times and dives into an eclectic collection of topics from election polling to the history of medicine. But even the most pragmatic, b-school-minded entrepreneur or VC may find some value here. Because what I’m ultimately talking about is a fundamental limiter on the future growth of the ed tech industry. The value of learning analytics, and therefore the market for them, will be limited by the data and statistical literacy of those who adopt it. The companies that are focused on developing fancier algorithms are solving the wrong problem—at least for now. These tools will have limited adoption until they are put into the hands of educators who understand their uses and limitations. And we have a long way to go in that department.

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  • The University As Ed Tech Startup: UMUC, Global Campus, Texas, and SNHU roll their own

    Today the University of Maryland University Campus (UMUC) announced its plans to spin off their Office of Analytics into a separate for-profit ed tech company.

    The University System of Maryland Board of Regents today approved a University of Maryland University College (UMUC) plan to spin off its Office of Analytics into a new company, HelioCampus, that will provide business intelligence products and services to universities nationwide. [snip]

    The new company will provide a foundational analytics platform and data analysis services. This comprehensive offering will include all the tools needed to support or jumpstart an analytics program. HelioCampus will host a secure platform in the cloud that will include flexible data models and best-in-class visual analytics to accelerate analysis.

    The technology will be complemented by a team of higher-education and business-intelligence experts. Each institution will be assigned a dedicated analyst that will partner with key stakeholders to interpret the data and highlight key trends.

    The Chronicle quotes two UMUC execs – CIO Pete Young (staying at UMUC) and VP for Analytics Darren Catalano (leaving UMUC to become HelioCampus’ chief executive). It is worth noting, as Stephen deFilipo did on Twitter, that both Pete Young and Darren Catalano come from Rosetta Stone. (more…)

  • What We Don’t Know About Learning Analytics

    Long-time e-Literate readers know that I have been a fan of the concept of learning analytics for a number of years now. But it became apparent at this year’s Learning Impact conference that learning analytics are the new hotness. Everybody is talking about them, and increasing numbers of vendors (LMS vendors, ERP vendors, textbook vendors, etc.) are trying to figure out how to get in on the party.

    Which means it’s probably time to start asking some critical questions about how well we really understand learning analytics and where the potential for failure and disappointment might be.

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  • Must-Read: Campbell and Oblinger on Academic Analytics

    John Campbell and Diana Oblinger have co-authored an EDUCAUSE paper on academic analytics that anyone with a practical interest in the topic should read. To begin with, it is a model of how to write a paper that addresses multiple institutional stakeholders across very different domains of expertise. It starts with a clear overview of the goals, breaks down the technical and logistical challenges into terms that non-experts can easily understand, lists out the likely questions, benefits, and risks for each stakeholder group, and presents high-level steps to prepare organizations that are looking to take on such an academic analytics project. And on the substance, it presents a balanced and comprehensive picture of the pros and cons of applying data mining techniques to student information in the service of improving educational outcomes.

    I particularly appricate the practical goals that the the authors set: increasing retention and graduation rates. I understand these may seem like pedestrian measures that don’t tell us how much students have learned and don’t necessarily improve the quality of the teaching either. But Campbell and Oblinger make a persuasive case that achieving these goals correlates with better outcomes for students, universities and society as a whole. Students who graduate tend to get better pay and better benefits and are more engaged in civic activities than their peers who don’t graduate, particularly in certain minority communities. Universities with higher percentages of students who graduate save more money per-student on recruiting, leaving more money to invest in improving the quality of education. And the society as a whole benefits from having more people who are not dependent on public support programs, who pay more in taxes, and who are more engaged in politics and other community-focused activities. On top of all this, we actually know how to measure retention and graduation rates. I’m concerned that many of the frantic efforts we on college campuses now to quantify student learning will turn out to be wastes of time and money because we don’t really know how to quantify learning in meaningful ways. Worse, if we delude ourselves about how much we’re able to measure, we may end up distorting the system of educational incentives in ways that actually harm students. This is exactly what has happened in K-12 in the United States with No Child Left Behind.

    The Campbell/Oblinger approach may not be sexy, but it’s practically and ethically sound while still managing to be ambitious.  This paper deserves your attention.