Tag Archives: edu17

The 2017 EDUCAUSE Mega Post

The latest in my ongoing series of conference “Mega Posts,” this one contains links to all my posts for the sessions I attended at the 2017 EDUCAUSE conference in Philadelphia. The reason I take such extensive notes and then publish them online is because I’m here on the state’s dime; this ain’t no vacation! Additionally, others may benefit in some small way from my conference experience. It represents a very small slice of what was actually available at EDU17; hopefully you’ll find it useful.

One of my priorities this time around was to learn as much as I could about student privacy and the use of student data. There’s a ton of talk about big data and predictive analytics, and some of the hype is actually justified (you have no idea how much it hurts me to admit that…I can’t stand vendor hype). That said, I find the comparative lack of conversation about ethics and legal considerations concerning. Talk about “student success” is like talk about “the American Dream.” Every campus is for it, but few campus leaders can clearly articulate it in an easily understandable way.

It’s a happy coincidence that a couple weeks ago my boss (VP of Student Affairs) asked me to do some research on online FERPA training. Enter my first pre-conference session, “Student Privacy Boot Camp.” This was, all on it’s own, worth the price of the entire conference to me. I had the privilege of getting educated by the Director of Student Privacy Policy from the US Department of Education. How cool is that?

Anyway, please enjoy these posts and share responsibly with your colleagues. See y’all next year!

Tuesday, October 31

These were pre-conference sessions, and they were both excellent. Next time you come to EDUCAUSE, I highly recommend attending a pre-conference session that piques your interest. I don’t know what the vetting/selection process looks like, but I’ve found that speakers are uniformly very good.

Wednesday, November 1

Thursday, November 2

Friday, November 3

Bridging the Divide Between IT and Student Success

Presenters

  • Michael Berman, Vice President for Technology & Innovation, California State University, Channel Islands
  • John Suess, Vice President of IT & CIO, University of Maryland, Baltimore County
  • Maria Thompson, President, Coppin State University
  • Timothy Renick, VP for Enrollment Management & Student Success, Vice Provost Georgia State University

NOTE: any errors, omissions or inadvertent misrepresentations are completely my fault. This conversation moved quickly and there was a lot of audience participation my fingers weren’t quite quick enough to catch – I beg your indulgence, dear reader. – Paul

Michael introduced the panel and panelists briefly talked about what they do at a very high level.

What does student success mean to you or your institution?

MT: it’s the reason we exist. Coppin State was right across the street from the unrest in 2015. We emphasize getting students enrolled and off to a strong start.

TR: practically, it’s about closing gaps among underserved populations (which are growing).  I believe we have a moral and social obligation to deploy fixes that actually work.

JS: we’re setting goals for retention and graduation that help us focus on what we need to do to “move the needle.” Stepping back, we have to consider what’s useful to the student long-term. Are we providing experiences that will be useful later in life?

MB: 5 years ago student success was defined idiosyncratically depending on the campus. In the past, we’ve prided ourselves in the CSU as being good at access, but often we left it up to the student to succeed. Some of our metrics have been: how many graduate in 4 years, how many graduate in 6 years? CSU’s GI2025 sets goals for each campus.

JS: what is transfer student success? We don’t institutionally have benchmarks that measure this.

MT: How many of us look at what student success means for the students?

TR: there are measures (like moving up from one economic quartile to another) that are important for our students that are very useful. However, that particular statistic may not resonate for everyone equally.

JS: we’re beginning to incorporate co-curricular data, but we’re not as good at quantifying what that actually means.

MT: co-curricular does show impact, but our average age is 27 (and a large number who are 65), so we could define this based on the multi-generational populations.

If student success is a team activity, what is your role in supporting the team’s success?

TR: I started in enrollment management; we had a student success committee that would meet to discuss this topic..not just once a month, but every week. Challenges for one area were a challenge for all areas to consider. Something that used to be the purview of say the vice provost, was now something that

MT: we put together a student success council with representatives from every division on campus, including faculty, students and staff that were empowered to take action based on data. If that means cancelling a program that doesn’t work, then that’s something we would do. TR: how do you message to your faculty “we’re going to do more than just talk about things?” MT: I look at the data EVERY SINGLE DAY. I memorize those numbers and I refer to them constantly.

JS: UMBC is in a different place. We’re a more traditional organization with shared governance and thus more dispersed. We just set up a persistence committee that meets every two weeks; we use the Civitas platform for data and feedback. One of the benefits of being in IT is that you get a “sense” for what’s going on across the campus, which puts you in a position where you can provide guidance and advice on how to streamline things.

MB: IT often has all the responsibility, but none of the authority. We kind of a universal support for pretty much the entire campus.

JS: we want to build the tools that allow students to take control of their own pathway through their experience.

MT: I think it’s important for the CIO to report to the president! (applause). I see IT as the circulatory system of the campus.

How does your state leverage your student success initiative?

TR: Georgia State has been leveraging predictive analytics for some time. We knew we needed more academic advisors, and we got funding for it, with the understanding that the best practices we learned would spread across the state.

MB: we’re rethinking the way we use our SIS in pretty fundamental ways (they’re bloated and slow). We’re trying to change to be more flexible and agile, but we’re still in the planning stages.

JS: one thing University of Maryland has done effectively is course redesign, which is a role that systems can effectively play.

TR: we’ve taken advantage of chatbots, but it’s not about the technology but the knowledge gained; for example, 80% of the questions asked of which are about financial aid.

JS: there are different models between Student Affairs and IT:  strong partnerships with IT, developing core competencies. Some of these conversations are difficult.

MT: there is technology fatigue for a lot of users, so I have to be mindful of the people who are keeping their eye on the big picture. We need to time these things so that they are not disruptive.

MB: we don’t need point solutions, we need API-based tools that will allow for more effective integrations and aggregation of data.

What’s one big mistake that campuses make when trying to use technology to promote student success?

JS: you need to “balance the ingredients in the cake.” Buying tech products needs to be balanced against adding staff to support it.

MB: you can’t alway rely on the tech to solve every problem.

(Audience question) What kind of data makes a president wake up at 2:00 AM?

MT: my dashboard has all enrollment, student success data, number of applicants, yield and more. We’ve opened that data up to every single employee at every level of the institution. We have training and role-appropriate drill-down, but everyone can view success data in the aggregate.

Gravitas and Grit: How IT Leaders Inspire Peak Performance

Presenters

  • Dianna Sadlouskos, Strategic Alliance Partner, Next Generation Executive Search
  • Joanna Young, Principal, JCYCIO
  • Melissa Woo, Senior Vice President for IT & CIO, Stony  Brook University
  • Brendan Guenther, Director for Academic Technology, Michigan State University
  • Russell Beard, Vice President of Information Technology, Bellevue Colllege

NOTE: any errors, omissions or inadvertent misrepresentations are completely my fault. This conversation moved quickly and there was a lot of audience participation my fingers weren’t quite quick enough to catch – I beg your indulgence, dear reader. – Paul

DS: No powerpoint today (yay!), we’re focusing on conversation today. Provided definition of “Grit” by Angela Duckworth (too long to capture).

DS – Question 1: How would you translate grit into your own personal path to leadership?

MW: “stick-to-it-iveness” was the key for me. I went through so many search committees, it was crazy. I incorporated feedback from coaches. Ask for feedback!

JY: first CIO job I applied for was an abject failure. The search consultant’s feedback was really helpful…it was tough, but amazingly helpful advice. Over prepare!  Every meeting you have with your president is a new interview.

RB: you have to have the ability to be patient and learn to breathe.

DS – Any essential grit stories to share?

(Audience member) It’s not only about grit, it’s about the people surrounding you. So many people said “you were great!” which was not helpful as I needed. I interviewed for 13 jobs before I got the one I have now. Just keep going.

DS: just because you don’t get a job, it’s OK, you may still be a very good candidate…you’e not a failure!

DS – Do you think that Grit is something you can develop in people?

BG: I think so. Everyone hits bumps in the road, sometimes you don’t bring the right people onto your team, you have to be able to adapt.

MW: you have to consider tough love. I tend to force people into projects that they are not comfortable with so that they have the opportunity to grow.

JY: job for life is no longer the case. You have to be able to force yourself into a role you’ve never had before. I had no telecom in my background, but I ended up running the largest broadband project in my state (having “wicked smaht” people around me was a great help).

DS – Interest and Practice: is there a difference in how you guide development of these attributes in mid-level managers versus millenials?

BG: the things that you are (where and how you grew up, etc.) have a lot to do with how you think.

JY: I now work for a millenial, someone I hired as an intern. We are learning so much from one another…he is completely fearless. People earlier in their career tend to have a higher degree of confidence (let ’em fail fast and learn fast). However, I want to give them guide rails to keep them from crashing and burning.

MW: at a conference I was at last week, keynote was about “radical candor.” Millenials are not as delicate as you think! Treat them as they are early career.

DS – is there advice you can give the group about how you inspire practice in aspiring leaders?

(Audience member) I don’t give advice, I ask a lot of questions. People with good social IQ pick up on what you’re doing, and will work through things in their head. Set parameters “here’s where I don’t want you to go.”

(Audience member) Give the person permission to fail, but coach them back to success.

DS – How to inspire mid-level managers to engage and re-invigorate their interest?

JY: get a different job and/or a different team.

RB: you’re prepared and need to manifest the presence to perform.

How does talent versus effort impact leaders?

JY: effort is great, but you need to apply effort effectively. Don’t use a teaspoon when a backhoe is the tool you really need. Talent is like a big “T” – you may have depth in tech, but you need to have breadth in business, how your campus works and more.

BG: you have to have the ability to identify talent. For effort, being able to identify the right talent sets among different people to work together.

DS: Can you share examples of staff who were talented but struggled?

DS: Tiger Woods vs. John McEnroe

JY: some of those staff are people who run with scissors who are very talented but are a danger to themselves. Often these people think of themselves as the smartest people in the room.

(Audience member) the smartest people in the room biggest issue is the fact that many of them are unable to be coached.

(Audience member) Coaching those team members is really helpful. For my team, when hiring, the skillset comes second to the ability to work within a team.

JY: rhetorical question: what’s more important: technical skills, or ability to work with faculty? (scattered callouts of “faculty”).

MW: you need to be able to have the difficult conversations to people.

RB: Honest feedback is important and one of the most important things we do as leaders.

How do you encourage staff to take risks and grow?

JY: influence your environment to make failure acceptable, so long as learning occurs.

BG: our role as coach/mentor is to help our staff pull the layers of failure apart so as to teach lessons that they can grow from. You HAVE to be there when your people fail.

RB: questions like “you should think about” were great, not prescribing solutions was important for me.

Algorithms of Oppression: How Search Engines Reinforce Racism

Presenter:

Research I’m sharing is the result of several years of research. Oddly enough, nobody really seemed to care about this research until the 2016 election 🙂 Platforms are often engaged in forms of redlining, foreclosing ways people can learn about others. Artificial Intelligence are going to move to the fore in our national dialogue about our rights, and are something we should consider as we move forward.

Google search autosuggestions featured a range of sexist ideas. These ideas reflect not just what is popular or only what Google search users are doing/thinking, which include:

  • Women cannot: drive, be bishops, be trusted, speak in church
  • Women shouldn’t: have rights, vote, work, box
  • Women should: stay at home, be slaves, be in the kitchen, not speak in church.
  • Women need to: be put in their places, know their place, be controlled, be disciplined

Examples

Washington Post article highlighted a Google Maps search for “nigga house” that took users to the White House. Response from Google was “Some inappropriate results are surfacing in Google Maps that should not be…” which is well, not exactly wonderful.

Kabir Ali highlighted a search for “three black teenagers” (mug shots) vs. “three white teenagers” (Getty-style stock photos).

Google “unprofessional hairstyles for work” (exclusively black women) versus “professional hairstyles for work” (exclusively white women).

“Algorithms represent a particular knowledge logic built on specific presumptions about what knowledge is and how one should identify its most relevant components That we are now turning to algorithms to identify what we need to know is as momentous as having relied on credentialed experts, the scientific method, common sense, or the Word of God. (T. Gillespie, The Relevance of Algorithms in Media Technologies)

Theoretical & Methodological Frameworks

  • Social Construction of Tech: tech is a social construction, embedded with social and political values
  • Black Feminism & Critical Race Theory: power relations are expressed through a “matrix of domination” based on our historical, social and economic positions. It is actionable, anti-racist research
  • Critical Information Studies: interdisciplinary interrogations of info and power.
  • Critical Discourse Analysis: power relations are expressed through a “matrix of domination” based on our historical, social and economic positions.

Why Pick on Google?

It’s far and away the search engine of choice. (Pew study). Most adult search engine users have faith in the fairness and accuracy of their results; trust in public goods. Google is in fact reliable for certain things, like how do I get from point A to point B, where is the closest Starbucks, what is the phone # for such and such a business, etc. However, for other things, that’s not necessarily so.

“Leading search engines give prominence to popular, wealthy, and powerful sites – via the technical mechanisms of crawling, indexing, and ranking algorithms, as well as thorough human-mediated trading of prominence for a fee at the expense of others.” (Nissenbaum, H., & Introna, L. 2000, Shaping the web: Why the politics of search engines matters)

Regulation of tech companies is sorely needed; there are many monopolies in the market.

For example: a search for “black girls” returns pornography as the first set of results by default. Same thing applies for Asian and Latina girls.

Old Media vs. New Media: the dominant narrative of black women and girls is sexualized.

Media representations of people of color, particularly African Americans, have been implicated in historical and contemporary racial projects. Such projects use stereotypic images to influence the redistribution of resources in ways that benefit dominant groups at the expense of others. (Davis, J.L. & Gandy, O.H. 1999. Racial identity and media orientation: exploring the nature of constraint. Journal of Black Studies).

Searching For Meaning

The Case of Dylan “Storm” Roof (see reference to the “Dylan Roof Manifesto,” 2015 at www.lastrhodesian.com). Dylan’s Google search did not return FBI statistics, but instead the Council of Conservative Citizens.

Things We Can Do

  • Reject “neutrality” and “information brokering”
  • Implement critical digital media literacy
  • Curate the indexable web too
  • Resist colorblind/racist/sexist collection development
  • Reduce technology over-development and e-waste

Who Is Doing Our Data Laundry?

Presenter:

  • Brad Wheeler, Ph.D., IU VP for IT, & CIO; Professor of Information Systems, Kelly School of Business

The world is deluged with data, but you may be asking yourself, what should I do? If they don’t do anything to inform decisions to meet the goals we’re pursuing, what good are they? Are you trying to

  • Rapidly remediate info reporting?
  • Enable better financial decisions?
  • Accelerate student success goals?
  • Empower advisors?
  • Benchmark yourself?

The act of working on data to get what you want is a bit like doing laundry:

  1. You put in capital
  2. You put in labor
  3. You add consumables

…and from this, we expect clean, organized clothes.

By “data laundry,” I’m referring to legitimate process of transforming and repurposing abundant data into timely, insightful, and relevant info for another context. It is a mostly unseen, antecedent process that unlocks data’s value and insights for the needs of decision makers.

Our institutions are often quite data-rich and insight-poor.

Two distinct phases to doing data laundry

  1. Data cleaning
  2. Presenting data as information in a context in which it can be used

Data Cleaning

Discovering > Extracting > Re-coding > Uploading > Normalizing

Information Presentation

Enriching > Comparing > Presenting (this is the “Magic Bundle”)

Insource or Outsource

You can buy the equipment and do the work ourselves, or go to the dry cleaners. Even if you go to the dry cleaners, you still have work to do… If you go to a vendor, which is common in higher ed, you’re going to have a significant amount of work. Companies like Apple, Google and Tesla have chosen to do a lot of insourced work.

IU’s Data Laundromat

IBM did an assessment of our organization and they told us that a) we had a lot of data, b) our data was not in the most usable format and c) we were lacking in ability to perform effective analysis.

Decision Support Initiative (2015)

  1. Enable report and dashboard “discovery” via search
  2. Created a factory for Decision Support Initiatives
  3. Agile Methodology (then run, run, run!)

The initiative goal: Improve decision making at all levels of IU by dramatically enhancing the availability of timely, relevant, and accurate info to support decision makers.

It will:

  • Affect people and orgs
  • Affect Data and Technology
  • Improve decision outcomes

Will clean data lead to good decisions?

Maybe, maybe not…

Caution

From Ackhoff’s Management MISinformation Systems, Management Science, written in 1967:

  1. In many cases, decision makers suffer from an overload of irrelevant information more than a lack of relevant information for a decision.
  2. In many cases, decision makers often request vastly more info than needed to optimally make a decision.
  3. In many cases, decision makers do not have refined models of the relevant information that best predict desired outcomes.

What’s up with YOUR data laundry? (Q&A)

How importance is data governance? Boiled down:

  1. Who has input rights? This should be broad.
  2. Who has decision rights? This should be narrow.

At IU, the data belongs to the trustees. Within compliance with laws (FERPA, HIPAA, etc.) and policy, it can be made available to the appropriate folks.