Two weeks ago, I had the opportunity to attend SXSWedu, an education conference in Austin, Texas, focused on cutting edge practices and technology. I spent most of my time in Austin attending higher education panels and exhibits, and came away feeling that three major themes dominated this gathering of those at the vanguard of the field.

  1. The continually advancing field of student data analytics

Several well-attended panels focused on how digital information about students and their behaviors could be used to optimize educational experiences and outcomes. It was apparent from these conversations how quickly the field is moving. There is a growing consensus about the need to shift analytics from a retrospective, “autopsy” approach to one in which instructional or advising interventions can happen in real time. Leading edge institutions have already embraced this “early-alert” approach, and use a mix of demographic, academic history, and behavioral data (usually gathered from LMS usage) to identify and intervene on students who are off track. Some institutions and companies have begun to seek out new frameworks for predicting student learning and success, and are integrating data from other sorts of systems and assessments to measure engagement and socio-emotional factors. The University of Central Oklahoma, for example, is working on integrating data about students’ tacit knowledge and mindset—gathered from learning portfolios, card swipe data, co-curricular engagement, and other sources—into its predictive analytics system so that it can better understand the factors that impact student success.  Of course, as an article from the Chronicle of Higher Education points out, each of these advances raises concerns about ethics, privacy, and transparency for institutions and third-party data platforms.

These discussions provoke questions about how the use of analytics will change as personalized and competency-based models gain traction. Today’s early-alert systems (the most common application of student data analytics) are embedded in traditional, time-based models of education. In these systems, an instructor or advisor is alerted that a student is “off-track” when she fails to submit an assignment on time, goes a certain length of time without logging into an LMS, or misses a certain number of classes; in other words, when she fails to progress according to the standard timeline for the course. These indicators don’t measure whether a student is “on-track” to achieve a certain learning outcome.

Competency-based, personalized learning platforms, typically those that have detailed tools that inform instructors about students’ mastery of concepts and skills covered in the course, do a better job of this.  Interestingly, these platforms are informed by a philosophy that learning should (ideally) be self-paced, undermining the whole notion of on or off-track. While very few students are learning in this kind of environment now, it is on the horizon. This seems to be another case in which the early-alert model, which is still a novelty for most institutions, is already being eclipsed by approaches that incorporate a broader swath of data to make interventions that focus on student cognition. It will be interesting to see how all three of these areas (data and analytics, personalized learning, and competency-based education) develop and are applied together in the coming years.

  1. Technology is not the biggest challenge—nor is it a “silver bullet” solution

Time and time again I heard speakers say that the technology they needed was available, but building practices to support the effective implementation and use of that technology was a burden. These speakers pointed to practices such as inter-institutional collaboration, strong leadership, and a commitment to measuring impacts as some of the most important levers for ensuring the effective use of technology to improve student outcomes.

The challenges of navigating partnerships between postsecondary institutions and ed-tech companies received the most attention. Speakers pointed to a vast cultural divide—both real and perceived—between Silicon Valley and academia. Those in ed-tech think that higher education is slow to innovate and resistant to change, while those in academia believe that ed-tech companies don’t truly understand the challenges they face. (One panelist asked: “how can 20-something Stanford graduates from Silicon Valley understand issues of access and success at my institution?”) The tendency of corporate representatives to frame discussions of educational technology in terms of disruption, with heavy student-as-customer undertones, exacerbates this tension.

Ideas for facilitating the implementation of technology solutions at postsecondary institutions included incentivizing universities to incubate their own innovations, empowering faculty champions who could lead the way in technology usage and culture change, and encouraging both institutions and ed-tech companies to pay more attention to project management. One ed-tech representative said that his company hired people who had worked at postsecondary institutions because they improve the company’s product, and also increase the company’s credibility with its institutional partners. As Joshua Kim, the Director of Digital Learning Initiatives at Dartmouth pointed out in an Inside Higher Ed article last week, ed-tech companies’ messages might also be better received if they highlighted access and success over efficiency, cost savings, or un-nuanced narratives of “disruption.”

  1. Competency-based education and the role of the institution

There seems to be a general consensus among SXSWedu panelists that most postsecondary institutions are not adequately or efficiently preparing students for careers. Relatedly, there is concern that the way institutions represent student learning (i.e., transcripts) is poorly matched to what employers need to assess potential hires. Many view an approach that structures learning around competencies and represents student’s achievement with reference to those competencies as a solution to these problems.

Panelists were divided, however, on what the role of the institution should be in solving these problems. Some imagined the institution as a key player in driving towards competency-based models: panelists discussed new ways of designing curricula, forging partnerships with industry, and representing student accomplishments, imagining a single institution as the primary player in creating a skill-based and supported pathway to employment. Other panels—particularly those without any institutional stakeholders—imagined an unbundled, skill-based marketplace in which students would assemble a competency profile from multiple providers.

Personally, I believe that institutions will have a role to play in preparing students for meaningful careers and lives for a long time to come. Moreover, an Airbnb-esque version of higher education (which I did hear proposed) does not seem like a good solution to skills mismatch—especially for students who tend to need more guidance. It’s imperative, then, that institutions continue to innovate in the ways they help students learn, represent what they learn, and pave supported pathways for students on their way to employment.