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Open Learning Analytics

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.

What will universities monetize in the future?

Universities do more than teach. Research is one of the most important activities of higher education. From the lens of students and society, however, the teaching and learning process and what it costs, is the primary focus.

The university economic and operational structure, in relation to educating learners, can be seen as consisting of three legs of a stool: content/curriculum, teaching, and assessment. The past decade has not been kind to higher education’s economic model as two legs of the stool – content and teaching – have started to move toward openness. Academic resources can now be found from top universities around the world. If I was tasked with designing a course from scratch, I would start by searching repositories, rather than creating any new content.

More recently, the teaching leg of the stool is seeing stress. Open online courses now make lectures of faculty from elite universities accessible to learners around the world (minus a few countries on the US “we don’t like” list).

This leaves assessment as the last leg of economic value. The badges and competency-based learning movement may challenge assessment, but at this point it remains reasonably secure.

What will universities do in the future to monetize their value? I offer the image below – instead of monetizing learning, content, and teaching, universities in the future will monetize assessment and the process of filling learner knowledge gaps. Content is largely free/open. Teaching is becoming more free/open. If something can be duplicated with only limited additional expense, it cannot serve as a value point for higher education. Creating personalized and adaptive learning processes that account for the personal knowledge graph of a learner is, and likely will continue, to be a source of value economically for universities.

University of Texas at Arlington

This is likely not news to most readers as it has been posted in various blogs, forums, and announced at the MOOC Research conference in December, but I have applied, and received approval, for a leave of absence from Athabasca University to establish and set up a digital learning research lab at University of Texas at Arlington. I will be based in Arlington, but will continue to work with my AU doctoral students.

My research to date has focused on the social and technological learning, sensemaking and wayfinding activities of individuals in digital information environments and how these actions inform the design of learning, curriculum and ultimately institutions. At the core of this research is how people interact with information. When information is limited, it can be assessed and understood individually or through social interactions with peers. When information is abundant, technology extends human cognition and capacity for sensemaking. How people use technology and social methods to make the world sensible, and the types of knowledge institutions required to assist that process, is what we hope to address through the Learning Innovation & Networked Knowledge (LINK) Research Lab.

A key second goal at UTA will be the development of a digital learning research network. Just like local-only classrooms no longer make sense, research institutions that work only within a small local domain don’t make sense. I’m particularly interested in understanding how we can connect excellent research with practical implementation. More is known about quality learning in literature than is reflected in classrooms and online courses. The digital learning research network is expected to bring those two domains together.

The challenge of coherence

I’ve been thinking about coherence formation in the learning process for many years (it was a key topic of my phd). Traditionally, coherence of knowledge is formed by the educator through her selection of readings and lectures. The assumption underpinning learning design is something like “decide what’s important and then decide how to best teach it or foster learning activities around it”. When students take a formal course, success is measured by how well they internalize (whatever that means) and repeat back to us what we told them. Most grading and evaluation happens at the intersection where comparisons are made between what the student can demonstrate in relation to what has been taught.

As students advance through their studies, they are asked to begin contributing new knowledge. There aren’t any clear lines around when students should start contributing instead of consuming, but masters level learning is a common demarcation point. I’m drawn more to the work of Bereiter and Scardamalia and their emphasis of knowledge building at all levels of learning, including primary/secondary levels.

I’ve found it difficult to articulate coherence provided by educators in contrast with coherence formed by learners and the growing role of the internet in fragmenting previous models of coherence. Most courses that I teach now do not rely exclusively on one or two texts. Instead, a bricolage of readings, videos, and other mutlimedia resources form course content. This fragmentation, however, generates a lack of coherence. Learning is the process of creating coherence – of seeing how pieces (ideas, concepts) are connected. I found the best description of this process in a recent article about Hola (while most articles about Hola emphasize “a way to get blocked content”, a simple definition is difficult. Hola does a variety of things: peer to peer content sharing, sharing idle computing capacity, VPN, a way to circumvent blocked content, etc). I’ll take it a step beyond and say that this is the most prescient statement regarding the future of learning that I have read in years:

Our processing power has increased so much faster than our networking speed that it’s easier to piece together stuff from all these nodes than to get a coherent piece of media from far away on the network

The vulnerability of learning

In a meeting with a group of doctoral students last week, one individual shared her challenging, even emotionally draining, experience in taking her first doctoral course. Much of her experience was not focused on the learning or content. Instead, she shared her self-doubts, her frustrations of integrating doctoral studies into her personal and professional life, the fatigue of learning, and feeling overwhelmed. Personal reflections such as these are important but are usually not considered when discussing learning and being a successful learner.

In education, seemingly in tandem with the advancement of technology and online learning, growing emphasis is placed on making the learning process more efficient. Through a barrage of instructional techniques and technologies, researchers and administrators strive to reduce the time that it takes a learner master a topic or complete a degree. While this is a laudable goal, it is an impoverished and malnourished view of education.

Learning involves many dimensions, but triggered by my conversation with my doctoral students, two are relevant here: epistemological and ontological. Epistemology is concerned with knowledge. In the educational process, that means the focus is on helping students to learn the knowledge (concepts, ideas, relationships) that a teacher or designer has designated as being important. Most thinking on improving education centres on the epistemological aspect of learning. While epistemology addresses “knowing”, ontology is concerned with “being” or “becoming”. For many students, this is the most substantial barrier to learning. Our education system and teaching practices largely overlook ontological principles. Instead, the focus is on knowledge development at the expense of “learner becoming”.

Learning is vulnerability. When we learn, we make ourselves vulnerable. When we engage in learning, we communicate that we want to grow, to become better, to improve ourselves. When I first started blogging, I had a sense of fear with every post (“did that sound stupid?”), loss of sleep soul-searching when a critical comment was posted, and envy when peers posted something brilliant (“wow, why didn’t I think of that?”). When a student posts an opinion in a discussion forum or when someone offers a controversial opinion – these are vulnerability-inducing expressions. On a smaller scale, posting a tweet, sharing an image, or speaking into the void can be intimidating for a new user. (I’m less clear about how being vulnerable becomes craving attention for some people as they get immersed in media!). While the learning process can’t be short-circuited, and the ambiguity and messiness can’t be eliminated, it is helpful for educators to recognize the social, identity, and emotional factors that influence learners. Often, these factors matter more than content/knowledge elements in contributing to learner success.

MOOCs and Emerging Educational Models: Policy, Practice, and Learning

We are now less than two weeks away from our MOOCs and Emerging Educational Models: Policy, Practice, and Learning conference. Registration is still open.

The conference will showcase successful grantees from the MOOC Research Initiative, as well as numerous panels addressing challenges around planning, designing, and running MOOCs. The full schedule is now available.

Changing Schools, Changing Knowledge: The Agenda

I’ve had the privilege of being on Steve Paikin’s The Agenda several times over the last few years. Steve is an informed and provocative interviewer, one of the best I’ve encountered covering the education sector. Earlier this year, I had an opportunity spend time on Steve’s program talking about how changing knowledge needs and structures are influencing the development of new learning systems and models. The interview is below:

Invitation: MOOC Framework

Tomorrow (November 20), I’ll be hosting an online discussion on a MOOC Framework that I’ve been developing with a few colleagues. If you’re interested, more information is here.

The Jam itself runs from 12:00 – 6:00 pm. Register here. The discussion agenda has also been posted.

The Failure of Udacity

Well, there it is folks. After two years of hype, breathless proclamations about how Udacity will transform higher education, Silicon Valley blindness to existing learning research, and numerous articles/interviews featuring Sebastian Thrun, Udacity has failed.

No one did more of a disservice to MOOCs than Thrun through his wild proclamations (“we have found the magic combination for online learning” and “in the future there will only be 10 universities”, digital learning manifestos, and so on) and self-aggrandizing. No one will do more damage to MOOCs as a concept than Thrun now that he realizes how unfounded his statements actually were.

Amazingly, after Udacity and Thurn’s “bull in a China shop” run through higher education, he proclaims that he has seen the light: “”We were on the front pages of newspapers and magazines, and at the same time, I was realizing, we don’t educate people as others wished, or as I wished. We have a lousy product…It was a painful moment.”"

The Udacity pivot, showcased (a latin term meaning “spin”) as a good thing in the Fast Company article, is the equivalent of Obama doing an Affordable Care is Working media tour. Make no mistake – this is a failure of Udacity and Sebastian Thrun. This is not a failure of open education, learning at scale, online learning, or MOOCs. Thrun tied his fate too early to VC funding. As a result, Udacity is now driven by revenue pursuits, not innovation. He promised us a bright future of open learning. He delivered to us something along the lines of a 1990′s corporate elearning program.

Stop what you are doing. Watch this: The Avalanche that Hasn’t Happened

David Kernohan delivered an stunning presentation at the Open Education conference: The Avalanche that Hasn’t Happened. He provides a critical evaluation of the testing/evaluation narrative in education. It is the best take down that I have seen of the dominant trends of for-profit, testing, and deliverology (honestly, that’s a word) impacting education. This video (below) needs to be shared broadly, particularly with leaders in the education sector. This is an impressive and valuable documentary. If David decides to develop a career creating education documentaries, I’ll be the first to provide kickstarter support. Resources and citations for the video are available here.