How ed tech marketers are bad for higher education

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A lot of ed tech marketers are really bad. They are probably not bad at their ‘jobs’ — they may or may not be bad at generating leads, creating well-designed sales material, creating brand visibility. But they are bad for higher education and student success.

Bad ed tech marketers are noisy. They use the same message as the ‘competition.’ They hollow out language through the use and abuse of buzz words. They praise product features as if they were innovative when everyone else is selling products that are basically the same. They take credit for the success of ‘mutant’ customers who — because they have the right people and processes in place — would have been successful regardless of their technology investments. Bad marketers make purchasing decisions complex, and they obscure the fact that no product is a magic bullet. They pretend that their tool will catalyze and align the people and processes necessary to make an impact. Bad marketers encourage institutions to think about product first, and to defer important conversations about institutional goals, priorities, values, governance, and process. Bad marketers are bad for institutions of higher education. Bad marketers are bad for students.

Good marketing in educational technology is about telling stories worth spreading. A familiar mantra. But what is a story worth spreading? It is a story that is honest, and told with the desire to make higher education better. It is NOT about selling product. I strongly ascribe to the stoic view that if you do the right thing, rewards will naturally follow. If you focus on short-term rewards, you will not be successful, especially not in the long run.

Here are three characteristics of educational technology stories worth telling:

  1. Giving credit where credit is due – it is wrong for an educational technology company (or funder, or association, or government) to take credit for the success of an institution. Case studies should always be created with a view to accurately documenting the steps taken by an institution to see results. This story might feature a particular product as a necessary condition of success, but it should also highlight those high impact practices that could be replicated, adapted, and scaled in other contexts regardless of the technology used. It is the task of the marketer to make higher education better by acting as a servant in promoting the people and institutions that are making a real impact.
  2. Refusing to lie with numbers – there was a time in the not-so-distant past when educational technology companies suffered from the irony of selling analytics products without any evidence of their impact. Today, those same companies suffer from another terrible irony: using bad data science to sell data products. Good data science doesn’t always result in the sexiest stories, even it it’s results are significant. It is a lazy marketer who twists the numbers to make headlines. It is the task of a good marketer to understand and communicate the significance of small victories, to popularize the insights that make data scientists excited, but that might sound trivial and obscure to the general public without the right perspective..
  3. Expressing the possible – A good marketer should know their products, and they should know their users. They should be empathetic in appreciating the challenges facing students, instructors, and administrators and work tirelessly as a partner in change. A good marketer does not stand at the periphery.  They get involved because they ARE involved.  A good marketer moves beyond product features and competitive positioning, and toward the articulation of concrete and specific ways of using a technology to meet the needs of students, teachers, and administrators a constantly changing world.

Suffice it to say, good marketing is hard to do. It requires domain expertise and empathy. It is not formulaic. Good educational technology marketing involves telling authentic stories that make education better. It is about telling stories that NEED to be told.

If a marketer can’t say something IMPORTANT, they shouldn’t say anything at all.

The Politics of Oversharing: The Circle by Dave Eggers

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The Circle by Dave Eggers tells the story of a young woman working to navigate a fictional google-type corporation with its sights set on achieving universal surveillance. What the company hopes to achieve is a panopticon vision of society in which no one has any secrets from anyone else. Everything that everyone does is recorded, streamed, archived, and made available for anyone and everyone to see.

The ‘Three Wise Men’ who founded this fictional company — called ‘The Circle’ — represent three perspectives that we see guiding Big Data investments today:

(1) The gleeful possiblist – unconcerned with the consequences of technologies that are created, this kind of person is simply interested in exploring what is possible. They wash their hands of ethical or long-term implications, since those hinge a kind of widespread adoption that has nothing to do with innovation in itself.

(2) The business man – like the gleeful possiblist, the business man washes their hands of ethical consequences since, driven by a desire to grow the business, the success of the products and services created hinges on the desires of the masses.

(3) The utopian – this is the most thoughtful of the three perspectives, in that it is the only one to accept responsibility for the future. With respect to issues of of big data and surveillance, it sees privacy as a problem to be solved. With privacy comes secrets and the possibility of lies. With secrets and lies come conflict. Universal surveillance and absolutely transparency mean complete accountability, technology-mediated empathy, and freedom from fear.

The bulk of Egger’s work is spent describing a variety of social surveillance technologies and the burdens they place on users like the protagonist, Mae. In many ways, this world is described in compelling and favorable terms. The reader is not disturbed, but actually drawn into agreement with the dominant utopian ideology into which Mae is progressively indoctrinated. Of course, there are nay-sayers, dissenters, and outsiders but in this world they are the minority. In a world where everyone’s opinion matters, democracy is absolute. If democracy is a good thing, absolute democracy the the best thing.

As one would expect from a book like this, as the circle nears completion, Eggers uses the opportunity to explore several distopian themes, including the possibility of a future tyrannical leader making use of truth-telling technology for systematically manipulating public perception. But Eggers is really good at ambiguity. He does an excellent job of using his narrative to explore important themes and possibilities while at the same time withholding judgement. One does not get from Eggers the sense that the trajectory of our serveillance technologies and big data policies are good or bad. What is strongly affirmed, however, is the fact that we are responsible. The purpose of The Circle is to force its readers to reflect on the consequences of their behavior, to consider their part as complicit in shaping the future. The Circle is effective in underscoring the importance of making thoughtful decisions about how we use technology instead of being passive users in a world made of us rather than by us and for us.

Five strategies for succeeding with data in higher education

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What important steps can you take to increase the success of your analytics project on campus?

At the 2017 Blackboard Analytics Symposium, A. Michael Berman, ‎VP for Technology & Innovation at CSU Channel Islands and Chief Innovation Officer for California State University, took a different approach to answering this question. Instead of asking about success, he asked about failure. What would project management look like if we set out to fail from the very beginning? As it turns out, the result looks pretty similar to a lot of well-meaning data projects we see today.

 

What important lessons can we learn from failure?

#1. Set clear goals

Setting clear goals is not easy, but it is an important first step to successfully completing any project. If you don’t know what you are setting out to do, you won’t know when you are done, and you won’t know if you succeeded. Setting clear goals is hard work, not only because it requires careful thinking, but also because it involves communication and consensus. Clear communication of well-defined goals creates alignment, but it also invites disagreement as different stakeholders want to achieve different things. Goal setting is a group exercise that involves bringing key stakeholders together to agree on a set of shared outcomes so you can all succeed together.

#2. Gain executive support

Garnering the support of executive champions is a crucial and often overlooked step. All too often, academic technology units are prevented from scaling otherwise innovative practices simply because no one in leadership knows about them. Support from leadership means access to resources. It means advocacy. It also means accountability.

#3. Think beyond the tech

IT projects are never about technology. They are always about solving specific problems for particular groups of people. For the most part, the people that are served by an analytics project have no interest in what “ETL” means, or what a “star schema” is. All they know is that they lack access to important information. What many IT professionals fail to appreciate is the fact that their language is foreign to a lot of people, and that using overly technical language often serves to compound the very problems they are trying to solve. Access to information without understanding is worse than no access at all.

#4. Maximize communication

Communication is important in two respects. It is important to the health of your analytics project because it ensures alignment and fosters momentum around the clearly defined goals that justified the project in the first place. But it is also important once the project is complete. The completion of an analytics project marks the beginning, not the end. If you wait until the project is complete before engaging your end users, you have an uphill battle ahead of you that is fraught with squandered opportunity. With a goal of ensuring widespread adoption once the analytics project is completed, it’s important to share information, raise awareness, and start training well in advance so that your users are ready and excited to dig in and start seeing results as soon as possible.

#5. Celebrate success

It’s easy to think of celebration as a waste of company resources. People come to work to do a job. They get paid for doing their job. What other reward do people need? But IT projects, and analytics projects in particular, are never ‘done.’ And they are never about IT or analytics. They are about people. Celebration needs to be built into a project in order to punctuate a change in state, and propel the project from implementation into adoption. In the absence of this kind of punctuation, projects never really feel complete, and a lack of closure inhibits the exact kind of excitement that is crucial to achieve widespread adoption.

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Originally posted to blog.blackboard.com

3 strategies for dealing with public speaking anxiety: Lessons from a pro athlete

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Fans often ask my wife, pro equestrian Elisa Wallace, if she still gets nervous. Her answer is always: yes.

Even at the highest levels of equestrian competition, it is not uncommon for athletes to involuntarily evacuate the contents of their stomach before an event. With pressure coming from large numbers of spectators (in person and on television), the hopes and dreams of fans and country, and — most importantly — the internal desire to do justice to the potential of her equine partner, a spike in adrenaline is impossible to avoid.

That involuntary physiological response is completely natural. It is a function of the fact that Elisa cares. If it didn’t happen, something would be wrong.

I’ve been thinking a lot about this this adrenaline response in my own professional life. Although still relatively new to speaking in front of very large audiences, I’ve been public speaking now for a long time, both as a teacher and as a scholar. It seems that no matter how much experience I get, I can’t overcome the experience of an involuntary adrenaline response prior to taking a stage.

This is something that I worry about, since I know that this response has an impact on my ability to think clearly, and to recall even the most well-practiced talk tracks. I worry about a quivering voice. I worry about fumbling about on stage, dropping things, and losing my train of thought.

Recently, I asked my wife how she deals with this kind of pre- performance stress response. She gave me three pieces of advice, based on her own experience as a professional athlete:

1. Embrace it.

The only reason you experience an adrenaline response prior to engaging in a public activity (or any activity for that matter) is that you care. That’s a good thing. The worst thing you can do is to stress out about stressing out. Instead, expect your adrenaline to spike and embrace it as an important part of your process. By simply reinterpreting this physiological response as working for you instead of against you, you can transform a hindrance into a helper.

2. You deserve to be there.

A lot of our anxiety comes from insecurity. For anyone with a realistic self-concept, it can be difficult to overcome ‘impostor syndrome.’ Whether you are a professional athlete or a public speaker, remember that you have worked hard, and the only reason you are there is because others want to see you there. You are there because you are already respected, and others already value your opinion. You have nothing to prove. Just do what you came to do.

3. Get pumped.

(1) and (2) are about mindset. This point is about how to get there. Many professional athletes have mastered the art of creating portable fortresses of solitude. They put their headphones on, listen to music, and tune out. Elisa has a ‘pump up’ playlist on her phone. Prior to going on cross country (the most thrilling and dangerous of her three phases), listening to music is helpful in two ways. It simultaneously (and paradoxically) helps you to tune out extraneous information so you can focus on the task at hand, and distracts you (in a productive way) from the importance of what you are about to do. Preference, of course, is music with driving bass lines, which we know from research has the effect of boosting confidence as well.

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Originally published to horsehubby.com

Ethics and Predictive Analytics in Higher Education

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In March 2017, Manuela Ekowo and Iris Palmer co-authored a report for New America that offered five guiding practices for the ethical use of predictive analytics in higher education.  This kind of work is really important.  It acknowledges that, to the extent that analytics in higher education is meant to have an impact on human behavior, it it is a fundamentally ethical enterprise.

Work like the recent New America report is not merely about educational data science.  It is an important facet of educational data science itself.

Are we doing ethics well?

But ethics is hard.  Ethics is not about generating a list of commandments.  It is not about cataloging common opinion.  It is about carefully establishing a set of principles on the basis of which it becomes possible to create a coherent system of knowledge and make consistent judgements in specific situations.

Unfortunately, most work on the ethics of analytics in higher education lacks this kind of rigor.  Instead, ethical frameworks are the result of a process of pooling opinions in such a way as to strike a balance between the needs of a large number of stakeholders including students, institutions, the economy, the law, and public opinion.  To call this approach ethics is to confuse the good with the expedient.

Where should we begin?

An ethical system worthy of the name needs to begin with a strong conception of the Good.  Whether stated or implied, the most common paradigm is essentially utilitarian, concerned with maximizing benefit for the greatest number of people.  The problem with this approach, however, is that it can only ever concern perceived benefit.  People are famously bad at knowing what is good for them.

A benefit of this utilitarian approach, of course, is that it allows us to avoid huge epistemological and metaphysical minefields.  In the absence of true knowledge of the good, we can lean on the wisdom of crowds.  By pooling information about perceived utility, so the theory goes, we can approximate something like the good, or at least achieve enough consensus to mitigate conflict as much as possible.

But what if we were more audacious?  What if our starting point was not the pragmatic desire to reduce conflict, but rather an interest in fostering the fullest expression of our potential as humans?  As it specifically pertains to the domain of educational data analytics, what if we abandoned that instrumental view of student success as degree completion?  What if we began with the question of what it means to be human, and wrestled with the ways in which the role of ‘student’ is compatible and incompatible with that humanity?

Humane data ethics in action

Let’s consider one example of how taking human nature seriously affects how we think about analytics technologies.  As the Italian humanist Pier Paolo Vergerio observed, all education is auto-didactic.  When we think about teaching and learning, the teacher has zero ability to confer knowledge.  It is always the learner’s task to acquire it.  True, it is possible to train humans just as we can train all manner of other creatures (operant and classical forms of conditioning are incredibly powerful). but this is not education.  Education is a uniquely human capability whereby we acquire knowledge (with the aim of living life in accord with the Good). Teachers do not educate.  Teachers do not ‘teach.’ Rather, it is the goal of the teacher to establish the context in which the student might become actively engaged as learners.

What does this mean for Education?  Viewed from this perspective, it is incumbent on us as educators to create contexts that bring students to an awareness of themselves as learners in the fullest sense of the word.  It is crucial that we develop technologies that highlight the student’s role as autodidact.  Our technologies need to help bring students to self-knowledge at the same time as they create robust contexts for knowledge acquisition (in addition to providing opportunities for exploration, discovery, experimentation, imagination and other humane attributes).

It is in large part this humanistic perspective that has informed my excitement about student-facing dashboards.  As folks like John Fritz have talked about, one of the great things about putting data in the hands of students is that it furthers institutional goals like graduation and retention as a function of promoting personal responsibility and self-regulated learning.  In other words, by using analytics first and foremost with an interest in helping students to understand and embrace themselves as learners in the fullest sense of the term, we cultivate virtues that translate into degree completion, but also career success and life satisfaction.

In my opinion, analytics (predictive or otherwise) are most powerful when employed with a view to maximizing self-knowledge and the fullest expression of human capability rather than as way to constrain human behavior to achieve institutional goals.  I am confident that such a virtuous and humanistic approach to educational data analytics will also lead to institutional gains (as indeed we have seen at places like Georgia State University), but worry that where values and technologies are not aligned, both human nature and institutional outcomes are bound to suffer.

Three business lessons I learned from my father

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My father is retiring today.

My father is leaving his working life as I feel that mine is getting started. It seems fitting, then, to use may father’s retirement as an occasion to look back at the lessons he has taught me over the years, and that continue to shape how I approach business and life. There are many. Here are three.

Don Harfield

Don Harfield

You can’t steer a parked car.

You can’t steer a parked car. This is great advice for surviving and thriving amidst conditions of uncertainty. None of us know what the future holds. Increasingly, we need to expect the unexpected. Rather than be paralyzed in the face of the unknown, what I have learned from my father is the importance of passionately pursuing a goal, committing yourself to a particular direction, while also being flexible and open to changing trajectory (sometimes radically) as conditions change. As my father retires, his advice continues to be relevant regardless of your stage in career and in life.

Don’t be risk averse. Be risk aware.

Being risk averse produces fear, and leads to an inability to act. Being afraid of risk leads to decisions that are as bad as if risk is unacknowledged. What risk aversion and its opposite have in common is a kind of laziness. If you don’t understand a project and the factors that condition its success, then you are stuck with temperament, simple heuristics, and ‘intuition.’ It is important to put in the work necessary to understand potential risks as much as possible, establish mechanisms to mitigate those risks, and build contingency into any plan to account for risks that you may not have identified or fully appreciated.

Do the right thing. Put people first.

In many ways, I feel like my belief in the importance of virtue can be traced back to the model my father set for me. Do the right thing. Put people first. Have faith that, in doing what’s right, success will happen as a matter of course. An important part of this is to avoid overdetermining what success looks like. It might mean fame of fortune, but it might also mean forming important relationships, achieving a sense of peace, or leaving an indelible mark on your community. If you go about your life chasing after success, whatever it is you’ll always miss the mark. If, on the other hand, you seek only after what is good, you’ll achieve success every time.

How to fail with data

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Sometimes the most effective way of communicating the right way to do something is by highlighting the consequences of doing the opposite.  It’s how sitcoms work.  By creating humorous situations that highlight the consequences of breeching social norms, those same norms are reinforced.

At the 2017 Blackboard Analytics Symposium, A. Michael Berman, ‎VP for Technology & Innovation at CSU Channel Islands and Chief Innovation Officer for California State University, harnessed his inner George Costanza to deliver an ironic, hilarious, and informative talk about strategies for failing with data.

What does this self-proclaimed ‘Tony Robbins of project failure’ suggest?

  1. Set unclear goals – setting unclear goals takes a lot of hard work and may require compromise. It’s way more democratic to let everyone set their own goals.  That way, everyone can have their own criteria for success, which guarantees that whatever you do almost everyone is going to think of you as a failure.
  2. Avoid Executive Support – Going out and getting executive support is also a lot of work. It means going to busy executives, getting time of their calendar, and speaking to them in terms they understand.  It also means taking the time to listen and understand what is important to them.  Why not go it alone?  Sure, it’s unlikely that you will achieve very much, but it’ll be a whole lot of fun.
  3. Emphasize the Tech – Make the project all about technology. And make sure to use as many acronyms as possible.  Larger outcomes don’t matter.  They are not your problem.  Focus on what you do best: processing the data and making sure it flows through your institution’s systems.
  4. Minimize Communication – Why even bother to make people’s eyes glaze over when talking about technology when you can avoid talking to anyone at all? Instead of having a poor communication strategy, it’s better to have no communication strategy at all.  You’ll save the time and inconvenience of dealing with people questioning what you do, because they won’t know what you’re doing.
  5. Don’t Celebrate Success – If you have done everything to fail, but still succeed despite yourself, it’s very important not to celebrate. Why bother having a party when people are already getting paid?  Why take time out of the work day to reward people for doing their jobs?  Isn’t it smarter to just tell everyone to get back to work?  Seems like a far more efficient use of institutional resources.

Speaking from personal experience, Michael Berman insists that following these five strategies will virtually guarantee that you drive your data project into the ground. If failing isn’t your thing, and you’d rather succeed in your analytics projects, do the opposite of these five things and you should be just fine.