Tag Archives: it

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.

Machine Learning 101

Presenters

  • Greg Corrado, Senior Research Scientist, Google
  • Vincent Nestler, Professor & Assistant Director of Cybersecurity, CSU San Bernardino
  • David Vasilia, Enterprise Network Administrator & Faculty, CSU, San Bernardino
  • Internet2 & GCP: internet2.edu/gcp
  • CS edu grants: cloud.google.com/edu

Machine Learning 101

  • Already in everyday products: photos, inbox, maps
  • 2 disciplines: AI and machine learning
  • Traditional AI systems are programmed to be clever
  • ML-based AI systems are designed to learn to be clever
  • Classic AI works on rules and contingencies; ML AI learns from examples and data.
  • Machines learn by example: models (which have parameters) feed predictions, which feeds a learner, which in turn feeds the parameters. This is surprisingly simple and generic.
  • Need 4 things: computational resources, good tools & algorithms, training examples, creativity and ingenuity of people.
  • Effective, but very gradual process that takes millions or billions of examples for it to work. It needs to cycle many many times.
  • ML coming of age in this decade because the computational power is exists now and it’s cheap and plentiful enough, i.e. CPU, GPU, Google TPU.
  • tensorflow.org a toolkit for machine learning
    • Open standard
    • Next gen deep learning tools built in
    • One system flexible enough for ML research
    • Robust enough for use in real products
    • Same software Google researchers use
  • Deep learning not one function, but a set of composable subfunctions for model building.
  • Distributing ML Tech Globally
    • Shared Tools: TensorFlow + CloudML
    • Ready-made ML systems (Cloud Vision API, Cloud Speech API, Cloud Translate API, etc.)
    • Use our tools to build your own system!
    • Example: TensorFlow cucumber sorting tool (really!)
    • Shared knowledge: open research publication at intl conferences; global direct community education; funding academic research and education.
  • Google published 90+ papers in the last 4 years
  • Takeaways:
    • Differentiation between AI vs. ML vs. Robotics
    • It isn’t magic, just a tool
    • Machines learn best from examples
    • Why now? fast computation
    • Make ML work requires creativity/ingenuity, cheap/fast computation/examples to learn from (data), tools & algorithms, TensorFlow makes ML software available for free.
    • Google Cloud makes hardware available.

Cloud for Higher Ed

  • Programming a campus rover: students are given a sensor, a raspberry pi, and Python. Then, they need to figure out how to integrate it.
  • Hacking now means hacking things together. You don’t have to be an engineer and you don’t need to know everything.
  • How can I level the playing field for my students? Be able to connect to Chrome and a Google compute engine. Everyone can look at and work with this environment, and they can explore from there.
  • A project we worked on in class: Android mapping for WiFi signal strength on campus. War driving took signal strength and using mapping API to literally map it to a real topographical map. Now we can “see our WiFi.”
  • We used Intermapper software to map the Internet, specifically the CENIC network from Los Angeles. The students loved this.

Panel

  • What is the difference between deep learning and machine learning? ML is the larger field of making machines that learn. DL is a small subset of this.
  • How far is Google taking cultural sensitivity into account with ML? Take translate as an example: you can dig into what the algorithm did to come up with its response.
  • If we use a Google tool, does this tool report what it learns back to Google? NO. What is pricing model for Google Cloud for Google Apps customers? It is independent of G-Suite.

Next Steps

  • Google is now a member of Internet2.
  • Will work with universities across the US to explore how Google Cloud Platform can better serve higher education
  • Help students build what’s next!
  • GCP Education Grants are available to: faculty in US, teaching university courses in CS or related fields in 2016-17 academic year. Examples: general CS, Cybersecurity, systems administration, networking.