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.

The Trouble with ‘Student Success’

I’m increasingly troubled by ‘student success,’ and am even somewhat inclined to stop using the term entirely.

The trouble with ‘student success,’ it seems to me, is that it actually has very little to do with people. It’s not about humans, but rather about a set of conditions required for humans to successfully fill a particular role: that of a student.

So, what is a student?

A student (within the context of higher education, and as the term is employed within student success literature) is someone who is admitted to an institution of higher education, is at least minimally retained by that institution (many colleges and universities require at least 60 non-transferred credit hours in order to grant a degree), and graduate with some kind of credential (at least an Associate’s degree, but preferably a Bachelor’s). The student is the product of higher education. It is the task of colleges and universities to convert non-students into students (through the admissions process), only to convert them into a better kinds of non-students (through the graduation process). The whole thing is not entirely different from that religious process whereby an individual must first be converted from an a-sinner (someone who doesn’t grasp what sin is) into a sinner (they need to learn what sin is, and that they have committed it) in order to be transformed into a non-sinner through a process of redemption.

The language of ‘student success’ assumes that ‘being a student’ is an unmitigated good. But being a student is not a good in itself. The good of being a student is a direct consequence of the fact that being a student is requisite for attaining other higher goods. Having been a successful student is necessary in order to become a good worker. From the perspective of the individual, having been a successful student translates into being able to get a better job and earn a higher salary. From the perspective of a nation, a well-educated populace translates into an ability to meet labor demands in the service of economic growth. If this is the end of being a student, then, shouldn’t we talk about ‘Worker Success’? Replacing ‘student-‘ with ‘worker-‘ would retain every feature of ‘student success,’ but with the advantage of acknowledging that a post-secondary degree is not an end in itself, but is rather in the service of something greater. It would be more honest. It might also have the effect of increasing graduation rates by extending the horizon of students beyond the shoreline of their college experience and out toward the open sea of what will become something between a job and a vocation.

But I find the idea of ‘worker success’ still troubling in the same way as ‘student success.’ As with ‘student success,’ ‘worker success’ speaks to a role that humans occupy. It refers to something that a person does, rather than what a person is. As with being a successful student, being a successful worker implied having satisfactorily met the demands of a particular role, a set of criteria that come from outside of you, and that it is incumbant upon you to achieve. A successful student is someone who is admitted, retained, and graduates and so it is unsurprising that these are the measures against which colleges and universities are evaluated. A successful institution is one that creates successful students. Pressure is increasingly being put on institutions to ensure that students find success in career, but this is far more difficult to track (great minds are working on it). A successful worker is one who earns a high-paying job (high-salary serving as a proxy for the amount of value that a particular individual contributes to the overall economy).

What if we were to shift the way that we think about student success, away from focusing on conditional and instrumental goods, and instead toward goods that are unconditional and intrinsic? What if we viewed student success, not as an end in itself, but rather as something that may or may not help human beings contract their full potential as human beings? Would it mean eliminating higher education as it is today? I don’t think so. I’m not a utopian. I readily understand the historical, social, cultural, and material conditions that make school and work important. To the contrary, shifting out perspective toward what makes us human may in fact serve to underline the importance of an undergraduate education, and even of that piece of paper they call a degree. To the extent that an undergraduate education exposes minds to a world of knowledge, at the same time as it provides them with an opportunity to earn a good wage means that they are freed from the conditions of bare life (i.e. living paycheck to paycheck) and can commit their energies to higher order pursuits. Considered in this way, the importance of eliminating achievement gaps on the basis of race. ethnicity, gender, income, etc is also increased. For these groups who have been traditionally underserved by higher education, what is at stake in NOT having a post-secondary credential is not just a wage, but also perhaps their potential as human beings. At the same time as it make higher education more important, considering the student journey from the perspective of human success also opens up legitimate alternative pathways to formal education through which it is also possible to flourish. Higher education might be a way, but it might not be the way. And that should be okay.

I don’t know what this shift in perspective would mean for evaluating institutions. As long as colleges and universities are aimed at producing student-graduates, their reason for being is to solve a tactical problem — “how do we admit and graduate more students” — and they can be evaluated empirically and quantitatively by the extent to which they have solved the problem. The minute that colleges and universities start to reconceive their mission, not in terms of students, but in terms of humans, their success becomes far more difficult to measure, because the success of students-as-humans is difficult to measure. By thinking of education as a way of serving humans as opposed to serving students, our task becomes far more important, and also far more challenging.

But since when were the Good and the Easy the same thing?

Three Ways Higher Ed can Avoid IT ‘Lock-In’

In a recent Future Trends Forum discussion with Bryan Alexander, George Siemens expressed concern about lock-in: a situation in which technology investments become so integrated with the business practices of an institution that disentanglement becomes all but impossible. Where hyper-rationalized approaches to data-driven decision-making come together with inflexible technological ecosystems characterized by a lack of interoperability, what we end up with is a dystopian future in which colleges and universities are unable to change their investments.

READ FULL STORY HERE >> https://edtechdigest.wordpress.com/2016/08/08/flexible-reality/

Number Games: Data Literacy When You Need It

My wife’s coach one told her that “experience is what you get the moment after you needed it.”  Too often the same can be said for data literacy.  Colleges and universities looking to wisely invest in analytics to support the success of their students and to optimize operational efficiency are confronted with the daunting task of having to evaluate a growing number of options before selecting a products and approaches that are right for them.  What products and services are most likely to see the greatest returns on investment?  What approaches have other institutions taken that have already seen high rates of success?  On the one hand, institutions that are just now getting started with analytics have the great advantage of being able to look to many who have gone before and who are beginning to see promising results.  On the other hand, the analytics space is still immature and there is little long-term high-quality evidence to support the effectiveness of many products and interventions.

Institutions and vendors who have invested heavily in analytics have a vested interest in representing promising results (and they ARE promising!) in the best light possible.  This makes sense.  This is a good thing.  The marketing tactics that both institutions of higher education and educational technology vendors employ as they represent their results are typically honest and in good faith as they earnestly work in support of student success.  But the representation of information is always a rhetorical act.  Consequently, the ways in which results are presented too often obscure the actual impact of technologies and interventions.  The way that results are promoted can make it difficult for less mature institutions to adjudicate the quality of claims and make well-informed decisions about the products, services, and practices that will be best for them.

Perhaps the most common tactic that is used to make results appear more impressive than they are involves changing the scale used on the y-axis of bar and line charts.  A relatively small difference can famously be made to appear dramatic if the range is small enough.  But there are other common tactics that are not as easily spotted that are nonetheless just as important when it comes to evaluating the impact of interventions.  Here are three:

There is a difference between a percentage increase and an increase in percentage points.  For example, an increase in retention from 50% to 55% may be represented as either an increase of 5 points or 10%.  It is also important to note that the same number of points will translate into a different percentage increase depending on the starting rate.  For example, a 5-point increase from a retention rate of 25% represents an increase of 20%.  A 5-point increase from a starting retention rate of 75%, on the other hand, is only an increase of 7%.  Marketing literature will tend to choose metrics based on what sounds most impressive, even if it obscures the real impact.

A single data point does not equal a trend.  Context and history are important.  When a vendor or institution claims that an intervention saw a significant increase in retention/graduation in only a year, it is possible that such an increase was due to chance, an existing trend, or else was the result of other initiatives or shifts in student demographics.  For example, one college recently reported a 10% increase in its retention rate after only one year of using a student retention product.  Looking back at historical retention rates, however, one finds that the year prior to tool adoption marked a significant and uncharacteristic drop in retention, which means that any increase could just as easily have been due to chance or other factors.  In the same case, close inspection finds that the retention rate following tool adoption was still low from an historical perspective, and part of an emerging downward trend rather than the reverse.

It’s not the tool.  It’s the intervention. One will ofter hear vendors take credit for significant increases in retention / graduation rates, when there are actually other far more significant causal factors.  One school, for example, is praised for using a particular analytics system to double its graduation rates.  What tends not to be mentioned, however, is the fact that the same school also radically reduced its student : advisor ratio, centralized its administration, and engaged in additional significant programmatic changes that contributed to the school’s success over and above the impact that the analytics system might have made by itself.  The effective use of an analytics solution can definitely play a major role in facilitating efforts to increase retention and graduation rates.  If fact, all things being equal, it is reasonable to expect a 1 to 3 point increase in student retention as a result of using early alerts powered by predictive analytics.  Significant gains above this, however, are only possible as a result of significant cultural change, strategic policy decisions, and well-designed interventions.  It can be tempting for a vendor specially to at least implicitly take credit for more than is due, but it can be misleading and have the effect of obscuring the tireless efforts of institutions and people who are working to support their students.  More than this, overemphasizing products over institutional change can impede progress.  It can lead institutions to falsely believe that a product will do all the work, and encourage them to naively embark on analytics projects and initiatives without fully understanding the change in culture, policy, and practice to make them fully successful.

Vlogging my way through BbWorld16

EPISODE I: Going to Vegas

Headed to Las Vegas for DevCon and BbWorld 2016. Having attended twice before as a customer, I am very excited to have played a part in organizing this year’s event.

In this vlog episode, I check in with Scott Hurrey (Code Poet at Blackboard) and ask him about what excites him the most about DevCon. Dan Rinzel (Product Manager, Blackboard Analytics) and John Whitmer (Director of Analytics and Research at Blackboard) tackle some extreme food portions.

EPISODE II: Teamwork makes the Dream Work

A day of rehearsal for the BbWorld16 opening general session leads to an air of playful excitement in anticipation of the main event. ‘Dr John’ talks about why data science isn’t scary, and why everyone should be interested and involved.

EPISODE III: Making Magic Happen

Want to go behind the scenes and get a sense of all of the work that goes into the opening main stage keynote presentation each year? Michelle Williams takes us on a tour!

EPISODE IV: Yoga and Analytics

Meet the Predictive Analytics ‘booth babes,’ learn from Michael Berman that yoga and analytics DO mix. Executive Director of the University Innovation Alliance, Bridget Burns, explains why there is a need for more empathy between institutions of higher educations and educational technology companies, and in higher education in general.

EPISODE V: We Are Family

Rachel Seranno from Appalachian State University talks about power poses and memes. Eric Silva praises the power of Twitter. Casey Nugent and Shelley White from the University of Nebraska – Lincoln describe how they are working with Blackboard consultants to understand and optimize instruction.