Product as Praxis: How Learning Analytics tools are ACTUALLY Differentiated

I’ve been thinking a lot recently about product as praxis. Without putting too much conceptual weight behind the term ‘praxis,’ what I mean is merely that educational technologies are not just developed in order to change behavior. Ed tech embodies values and beliefs (often latent) about what humans are and should be, about what teaching and learning are, and about the role that institutions should play in guiding the development of subjectivity. As valued, educational technology also has the power to shape, not just our practices, but also how we think.

When thought of as praxis, product development carries with it a huge burden. Acknowledging that technology has the power (and the intention) to shape thought and action, the task of creating an academic technology becomes a fundamentally ethical exercise.

Vendors are not merely responsible for meeting the demands of the market. ‘The market’ is famously bad at understanding what is best for it. Instead, vendors are responsible for meeting the needs of educators. It is important for vendors to think carefully about their own pedagogical assumptions. It is important for them to be explicit about how those assumptions shape product development. The product team at Blackboard (of which I am a part), for example, is committed to values like transparency and interoperability. We are committed to an approach to learning analytics that seeks to amplify the power existing human capabilities rather than exclude them from the process (the value of augmentation over automation). These values are not shared by everyone in educational technology. They are audacious in that they fly in the face of some taken-for-granted assumptions about what constitute good business models in higher education.

Business models should not determine pedagogy. It is the task of vendors in the educational technology space to begin with strong commitments to a set of well-defined values about education, and to ensure that business models are consistent with those fundamental beliefs. It will always be a challenge to develop sustainable business models that do not conflict with core values. But that’s not a bad thing.

When it comes to the market for data in eduction, let’s face it: analytics are a commodity. Every analytics vendor is applying the same basic set of proven techniques to the same kinds of data. In this, it is silly (and even dangerous) to talk about proprietary algorithms. Data science is not a market differentiator.

What DOES differentiate products are the ways in which information is exposed. It is easy to forget that analytics is a rhetorical activity. The visual display of information is an important interpretive layer. The decisions that product designers make about WHAT and HOW information is displayed prompt different ranges of interpretation and nudge people to take different types of action. Dashboards are the front line between information and practice. It is here where values become most apparent, and it is here where products are truly differentiated.

Five Must-See Analytics Sessions at EDUCAUSE 2016

EDUCAUSE is big.  Really big. With so much to take in, conference-goers (myself included) are easily faced with the paradox of choice: a sense of paralysis in the face of too many options.  To help myself and others, I have scanned this year’s conference agenda and selected five presentations that I think will be individually strong, and that as a group offer a good overview of the themes, issues, and state of analytics in higher education today.

Learning at Scale with Analytics: Findings from the Field and Open Questions

Wednesday, October 26, 2016 | 3:40 PM – 4:30 PM | Meeting Room 204B

Moderated by Michael Feldstein (e-Literate), and featuring John Whitmer (Blackboard), Russ Little (PAR), and Jeff Gold (California State University), and Avi Yashchin (IBM), this session promises to provide an engaging and insightful overview of why analytics are important for higher education, the biggest challenges currently facing the field, and opportunities for the future.  Although most of the speakers are strongly affiliated with vendors in the analytics space, they are strong data scientists in their own right and have demonstrated time and time again that they do not shy from critical honesty.  Attend this session for a raw glimpse into what analytics mean for higher education today.

Deploying Open Learning Analytics at National Scale: Lessons from the Real World

Thursday, October 27 | 8:00am – 8:50am | Ballroom A, Level Three

Jisc is a non-profit company that aims to create and maintain a set of shared services in support of higher education in the UK.  The Effective Learning Analytics project that Michael Webb will discuss in this session has aimed to provide a centralized learning analytics solution in addition to a library of shared resources.  The outputs of this project to date have valuable resources  to the international educational analytics community in general, including Code of practice for learning analytics and Learning Analytics in Higher Education.  Jisc’s work is being watched carefully by governments and non-governmental organizations worldwide and represents an approach that we may wish to consider emulating in the US (current laws notwithstanding).  Attend this session to learn about the costs and opportunities involved in the development of a centralized approach to collecting and distributing educational data.

Founding a Data Democracy: How Ivy Tech is Leading a Revolution in Higher Education

Thursday, October 27 | 1:30pm – 2:20pm | Meeting Room 202A/B, Level Two

The higher education community is abuzz with talk of how data and analytics can improve student success.  But data and analytics are worthless unless they are put in the hands of the right people and in the right ways.  I am really interested to see how Ivy Tech has worked to successfully democratize access to information, and also about the ways that access to data has driven the kind of institutional and cultural change necessary to see the most significant results from data-driven initiatives.

Analytics and Student Success: Research and Benchmarking

Thursday, October 27 | 8:00am – 8:50am | Meeting Room 304A/B, Level Three

Everyone’s talking about analytics, and every institution seemingly has the will to invest.  Attention paid to analytics in media and by vendors can lead to the impression that everybody’s doing it, and that everyone who’s doing it is seeing great results.  But the truth is far from the case.

I’m not the greatest fan of benchmarking in general.  Too often, benchmarking is productized by vendors and sold to universities despite providing very little actionable value.  Worse yet, they can exacerbate feelings of institutional insecurity and drive imprudent investments.  But when it comes to analytics, benchmarking done right can provide important evidence to counteract misperceptions about the general state of analytics in the US, and provide institutions with valuable information to inform prudent investment, planning, and policy decisions.  In this presentation, I look forward to hearing Christopher Brooks and Jeffery Pomerantz from EDUCAUSE discuss their work on the analytics and student success benchmarking tools.

Building with LEGOs: Leveraging Open Standards for Learning Analytics Data

Friday, October 28 | 8:00am – 8:50am | Meeting Room 304C/D, Level Three

I am a huge advocate of open standards in learning analytics.  Open standards mean greater amounts of higher quality data.  They mean that vendors and data scientists can spend more time innovating and less time just trying to get plumbing to work.  In this interactive presentation, Malcolm Brown (EDUCAUSE), Jenn Stringer (University of California, Berkeley), Sean DeMonner (University of Michigan-Ann Arbor), and Virginia Lacefield (University of Kentucky) talk about how open learning standards like IMS Caliper and xAPI are creating the foundation for the emergence of next generation learning environments.

Lines in the Sand: Putting Family First

Nana passed away last week.

Nana was my wife’s grandmother. After battling cancer, and finally beating it with the removal of a kidney, she eventually succumbed to infection — a side-effect of her immune system having been decimated by chemotherapy.  

I am fortunate that I have not had to deal with death in my family since I began working full time. But a consequence of this is that I have never had to really decide how to balance related family affairs with the demands of work. In fact, I’ve never really had an experience where family and work collided. I’ve never had to answer the question: work or family. I’ve never had to draw a line in the sand.

In the absence of principles, decisions are hard. Every choice is new and has to be wrestled with singularly. I appreciate that life is complex, and that there are ethical positions that would have us grapple with every decision in this way. But values are important, and values should immediately translate into at least a small set of default positions.

I am fortunate to have a boss whom I also consider a friend and mentor. As the funeral was scheduled and I learned that it would conflict with work and work-travel commitments, I gave him a call. What he said was that, for him, family and religion are areas in which he refuses to compromise. Sure, work commitments might mean that you can’t make it to every one of your kid’s soccer games, but when it comes to things like funerals for close family members and religious holidays, he refuses to compromise, even it it might be moderately inconvenient.

I like to make the distinction between compromise and sacrifice. Compromise is what happens when preferences and tastes come into conflict. There’s nothing wrong with compromise, since compromising is often necessary for the sake of establishing, maintaining, and strengthening relationships. The art of compromise is the political virtue par excellence. Sacrifice, on the other hand, is what happens when you make a decision that conflicts with core values. To make a sacrifice, then, means calling who you are into question. It creates a significant dissonance between what you believe and what you do, and forces you to re-evaluate both. Compromise might be inconvenient, but sacrifice is unacceptable.

I have incredibly fond memories of Nana. She’s my wife’s grandmother, and so I have only known her for a relative short time. But in that time, I have enjoyed her sense of decorum (a true Southern Lady) and her authentic laughter punctuated by little snorts. I have enjoyed her cooking and her love of history. She, along with her husband ‘Papa,’ are committed to family above all else, and so it is fitting that her passing would itself leave this legacy: the fact that I am in Mississippi for Nana’s funeral and spending time with family as they recount stories and rekindle old relationships is a function of a decision precipitated by her passing.

I didn’t make a decision to take off time from work to spend with family in celebration of Nana’s life. The fact that I am here is a consequence of a decision that goes much deeper. It is the result of a line in the sand that I have drawn and now refuse to cross. (A line that I am embarrassed to say that I had not, strictly speaking, made sooner).

Family first.

Four-Star Horse Husband Shares His Philosophy on Life, Love and HorseHubby.com

Story by Susan Friedland-Smith for Sidelines Magazine

Dr. Timothy Harfield, eventer Elisa Wallace’s horse husband of three years, documents a behind-the-scenes look at the life of United States Olympic Eventing Team reserve rider and Mustang advocate from Jasper, Georgia, via weekly Wallace Eventing vlogs. In addition, Timothy has fully-embraced his horse hubby role by cleaning stalls, feeding horses and holding “stuff” when asked. He co-hosts a monthly Horse Husbands Horse Radio Network podcast and founded the hilarious blog HorseHubby.com. Sidelines caught up with Timothy and learned more about his thoughts on love, horses and amoeba eventing.

Read complete interview here: https://sidelinesnews.com/general/timothy-harfield-the-four-star-horse-husband-shares-his-philosophy-on-life-love-and-horsehubby-com.html

Learning analytics and interoperability—a new standard

Learning analytics can be like shining a flashlight into a deep cave.

In an instant, access to data about teaching and learning practices illuminates facts about actual behavior that would otherwise be left to speculation and anecdote. But just as we need multiple light sources to shed light into the many caverns that connect to a single cave, collecting data from multiple learning tools has traditionally involved aggregating a series of independent data extraction processes.

As learning management systems (LMS) become next generation learning environments (NGLE), they increasingly function as hubs for connecting a wide variety of other learning technologies. But, in their current state, educational tools vary widely in terms of the kinds of data they collect, and in the extent to which they make their data available to other systems.

READ FULL STORY HERE >> http://blog.blackboard.com/learning-analytics-interoperability-standard/

Student Success and Liberal Democracy

The political environment in the United States has increasingly highlighted huge problems in our education system. These problems, I would argue, are not unrelated to how we as a country conceptualize student success. From the perspective of the student, success is about finding a high-paying job that provides a strong sense of personal fulfillment. From the perspective of colleges and universities, student success is about graduation and retention. From the perspective of government, it’s about making sure that we have a trained workforce capable of meeting labor market demands. For all of the recent and growing amount of attention paid to student success, however, what is woefully absent seems to be any talk about the importance of education to producing a liberal democratic citizenry. In the age of ‘big data,’ of course, part of this absence may be the fact that the success of a liberal education is difficult to measure. From this perspective, the success of a country’s education system cannot be measured directly. Instead, it is measured by the extent to which it’s citizens demonstrate things like active engagement, an interest/ability to adjudicate truth claims, and a desire to promote social and societal goods. Now, more than any time in recent history, we are witnessing the failure of American education. In the US, the topic of education has been largely absent from the platforms of individual presidential candidates.  This is, perhaps, a testament to the fact that education is bad for politics.  Where it has been discussed, we hear Trump talk about cutting funding to the Department of Education, if not eliminating it entirely. We hear Clinton talk about early childhood education, free/debt-free college, and more computer science training in k-12, but in each of these cases, the tenor tends to be about work and jobs rather than promoting societal goods more generally.

But I don’t want to make this post about politics. Our political climate is merely a reflection of the values that inform our conceptions of student success. These values — work, personal fulfillment, etc — inform policy decisions and university programs, but they also inform the development of educational technologies. The values that make up our nation’s conception of ‘student success’ produce the market demand that educational technology companies then try to meet. It is for this reason that we see a recent surge (some would say glut) of student retention products on the market, and relatively few that are meant to support liberal democratic values. It’s easy to forget that our technologies are not value-neutral. It’s easy to forget that, especially when it comes to communication technologies, the ‘medium is the message.’

What can educational technology companies do to meet market demands (something necessary to survival) while at the same time being attuned to the larger needs of society? I would suggest three things:

  1. Struggle. Keeping ethical considerations and the needs of society top of mind is hard.  For educational technologies to acknowledge the extent to which they both shape and are shaped by cultural movements produces a heavy burden of responsibility.  The easy thing to do is to abdicate responsibility, citing the fact that ‘we are just a technology company.’  But technologies always promote particular sets of values.  Accepting the need to meet market demand at the same time as the need to support liberal democratic education can be hard. These values WILL and DO come into conflict. But that’s not a reason to abandon either one or the other.  It means constantly struggling in the knowledge that educational technologies have a real impact on the lives of people.  Educational technology development is an inherently ethical enterprise.  Ethics are hard.
  2. Augment human judgment.  Educational technologies should not create opportunities for human beings to avoid taking responsibility for their decisions.  With more data, more analytics, and more artificial intelligence, it is tempting to lean on technology to make decisions for us.  But liberal democracy is not about eliminating human responsibility, and it is not about making critical thinking unnecessary.  To the contrary, personal responsibility and critical thinking are hallmarks of a liberal democratic citizen — and are essential to what it means to be human.  As tempting as it may be to create technologies that make decisions for us because they can, I feel like it is vitally important that we design technologies that increase our ability to participate in those activities that are the most human.
  3. Focus on community and critical thinking.  Creating technologies that foster engagement with complex ideas is hard.  Very much in line with the ‘augmented’ approach to educational technology development, I look to people like Alyssa Wise and Bodong Chen, who are looking at ways that a combination of embedded analytics and thoughtful teaching practices can produce reflective moments for students, and foster critical thinking in the context of community.  And it is for this reason that I am excited about tools like X-Ray Learning Analytics, a product for Moodle that makes use of social network analysis and natural language processing in a way that empowers teachers to promote critical thinking and community engagement.

Data Dread: An Intractable Problem of Personal Identity in the Digital Age?

Public concern about ‘big data’ frequently comes down to a vague and ill-defined sense of ‘ickiness.’  I’d like to briefly suggest a way to provides structure to this vague sentiment — let’s call it data dread.  Provisionally, I would argue that public distrust of ‘big data’ comes down to major tension between two promises of the digital age.  On the one hand, as Floridi notes, the advent of social media represents an “unprecedented opportunity to be more in charge of our social selves, to chose more flexible who the other people are whose thoughts and interactions create our social personality” (The Fourth Revolution, p. 64).  In other words, the modern internet allows us not only to more carefully craft our identities, but also to more carefully curate our communities so that our self-representations are more likely to be recognized and accepted. We now have an unparalleled ability to ‘make ourselves’ in a way that resonates, not just with the existentialist philosophies of the 20th century, but also with Renaissance conceptions of man as infinitely fluid and self-determining.  This is the first promise of the digital age.

On the other hand, however, the very technologies that allow us to make ourselves also — and necessarily — produce digital traces, on the basis of which it becomes possible for individuals to be tracked, and identities co-opted,  The digital traces that people leave behind as a result of their efforts to forge their digital identities can also be used by algorithms to produce identities for which individuals are not themselves responsible, but that nevertheless have real effects.  This is not necessarily a bad thing.  In fact, this amounts to a second promise of the digital age: the promise of personalization.  People want their experience of the world to be personalized.  Who doesn’t want the world to revolve around them?  But the problem with the algorithms that personalize experience is that they don’t actually care who you are or how you wish to be recognized.  They don’t care about the identity you wish to construct.  In personalizing our experiences of the world, algorithms also have a very real effect on our self-perceptions.  In order to personalize our experiences, they must first personalize us.

The two promises of the digital age, then, are these: (1) a promise that individuals are free to construct themselves in whatever was they choose, and (2) a promise that digital traces will be used to personalize individual experiences, not just online, but in the ‘real world’ as well.  Our experience of dread comes from the fact that we are simultaneously promised an ability to make ourselves at the same time as we are promised that our selves will be algorithmically made on our behalf.  At the same time as we represent ourselves in such a way as to be recognized and acknowledged by others in the ‘real world,’ algorithms are representing us to ourselves and others, making judgements about who we are and what we want, and intervening in our lives through nudges, recommendations, and other automated events.

For Hegel, people come to recognize themselves as selves through two basic sources: labor and others.  I know that I am a self because I can recognize myself in the work of my hands, and I know that I am a self because of the fact that others relate to me as such.  In the former case, I am in complete control over the self I create.  In the latter, the self that I create is a function of the a negotiation.  Now, for the first time, it has become possible for people to be entirely — and voluntary — excluded from the processes by which their identities are constituted.  In the social world, our identities and the consequences of our behaviors may not be our own, but they are nonetheless a function of a kind of ongoing negotiation with other people.  When machines start to make judgements about us, any sense of negotiation is lost.

Perhaps our sense of ‘ickiness’ in the face of big data — our sense of data dread — is not a function of data itself.  Perhaps it is a function of the fact that there is a contradiction at the heart of so-called ‘social media’ — what Floridi calls Information and Communication Technologies (ICT).  It is not just that digital traces from ICTs make it possible for identities to be algorithmically co-opted.  The algorithmic co-option of personal identity is a necessary feature of modern-day social media technologies in the absence of which those technologies would cease to function.