The future of systems such as business, government, and education will be data centric. Historically, humanity has made sense of the world through discourse, dialogue, artifacts, myth, story, and metaphor. While those sensemaking approaches won’t disappear, they will be augmented by data and analytics.
Educators often find analytics frustrating. After all, how can you analyze the softer aspects of learning? Or can analytics actually measure what matters instead of what is readily accessible in terms of data? These are obviously important questions. Regardless of how they are answered, however, ours is a data-rich world and will only continue to become more so. All educators need to be familiar with data and analytics approaches, including machine and deep learning models. Why does it matter? Well, to use a Ted Nelson quote that Jim Groom used during his excellent talk at Sloan-C this week, it matters “because we live in media as fish live in water”. Power and control decisions are being made at the data and analytics level of educational institutions. If academics, designers, and teachers are not able to participate in those conversations, they essentially abdicate their voice.
About five years ago, a few colleagues (Shane Dawson, Simon Buckingham Shum, Caroline Haythornthwaite, and Dragan Gasevic) and I got together with a great group of folks and organized the 1st International Conference in Learning Analytics and Knowledge (complete with a logo that any web users of the 1990s would love). Our interest primarily focused on the growing influence of data around educational decisions and that an empirical research community did not exist to respond to bold proclamations being made by vendors about learning analytics. Since then, a community of researchers and practitioners has developed. The Society for Learning Analytics Research was formed, hosting summer institutes, our annual conference, journal, and a distributed doctoral research lab.
Today we are pleased to announce two new initiatives that we feel will raise the quality of learning analytics, increase transparency around data and algorithms, and create an ecosystem where results can be shared, tested, and validated:
1. Open Learning Analytics. This initiative is based on a paper that we published (.pdf) several years ago. After significant behind-the-scenes work, we are now ready to announce the next steps of the project formally. See here for press release and project scope.
2. Learning Analytics Masters Program (LAMP). The number of masters programs that are offering learning analytics courses, streams or certificates is increasing. Several institutions are in the process of developing a masters in learning analytics. To help provided quality curriculum and learning resources, we have launched LAMP: an open access, openly licensed learning analytics masters program. Institutions will be able to use/remix/do whatever with the content in developing their masters programs. Our inaugural meeting is being held at Carnegie Mellon University in a few weeks to kick off this project and start developing the course content.
If data is the future of education and educational decision making, and in many ways it is, I believe openness is the best premise on which to advance. The projects presented here are our contribution in making that happen.