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Activating Latent Knowledge Capacity

Last week, we wrapped up another successful Learning Analytics Summer Institute at Harvard. The recordings of most of the talks and panels can be found here. Since we were already in town, Dragan Gasevic and I were invited by edX to give a talk to their staff and member institutions (we are running a course on edX in fall on Data, Analytics, & Learning).

The focus for the talk at edX, slides below, was to try and get at what is wrong with MOOCs and education in general. To answer the challenge of “what is wrong with education” it’s helpful to step back a bit and consider two challenges.

1. We aren’t connecting

Historically, society has created knowledge institutions that mirror what is done with information in a particular era – see McNeely & Wolverton. In this line of reasoning, we can best understand the future of education by understanding what is being done with information today. After about a decade of experience with web 2.0, social media, participative technologies, it’s not unreasonable to state that at least a segment of society today recognizes information as multi-authored, participative, distributed, and networked.

In education, many of us have been advocating for networked learning (or as Stephen Downes and I have been articulating it, connectivism). Academic conferences and even the K-12 space has turned to networks as a way to describe what learning is and how it happens. The one draw back to networked learning is that while we have managed to advance conversation on the fragmentation of learning so that it is not a cohesive whole created solely by the instructor, we have not yet advanced the process of centring or stitching together fragmented parts into cohesive wholes for individuals. Some rudimentary progress includes the use of #hashtags to stitch together distributed conversations but this only provides a one medium aggregation. The best implemented model for pulling together multi-platform conversations that I’ve seen to date is Downes’ gRSShopper. That leaves us at a difficult point educationally. Progress has been made on pulling centralized information elements apart (this is particularly evident in media with newspapers or TV news programs – I get the majority of my news in bits and pieces through a mess of different tools and sites), but we haven’t yet developed the technologies that will allow pulling things back together into coherent, personally owned, wholes.

This is no small challenge. In many ways, this is where computing was in two separate phases: pre-Microsoft Office and pre-Facebook. I remember when I used to work with distinct software tools like Quattro and WordPerfect (before they were owned by one company). Moving data between different software was a pain. MS came along and blessed society with Office – an integrated suite. It pulled together what I used to do in several different tools. Facebook plays a similar integrative role for participative technologies. For people who had been blogging since late 1990′s or early 2000′s, Facebook wasn’t of much value. Between flickr, del.icio.us, blogs, RSS readers, and wikis, we were living the distributed, networked, learning dream. Unfortunately, only a small percentage of society wants to deal with a range of 10 different tools. Ease of use and low-barrier to entry rules the day. Facebook allowed anyone to start sharing images, ideas, and form social networks and to do so in a single tool with similar functionality across different activities. My social network used to consist of the people in my RSS reader. Facebook made connecting easy and they were rewarded as a billion+ people joined. The key lesson here is that integrative technologies, in spite of the current app trend, draw greater numbers of users than single functionality tools.

The importance of integrative toolsets for learners cannot be overlooked. It is unreasonable to expect a learner to care about the same issues that an instructor of a participatory course cares about. While concerns of access, participation, and equity might be important to me, a learner may well enter a course with the primary goal of learning a skill or concept. My values may not be her values.

2. Latent Capacity

Technology cannot be reduced to a single narrative or outcome. While “web 2.0″, as a term, symbolized participation and collaboration, it is really a multi-narrative strand where some people were enabled and others were shut out, some were given a platform and others lost a platform, some connected with their readers/fans and others were exposed as [insert label] to their fans/readers. There are many narratives to describe the tools that today define how people interact. I have been grappling with understanding the prominent or even dominant impact of technology – i.e. what is one aspect of technology that is most pronounced and most misunderstood? Keeping in mind that a single narrative has shortcomings, I’ll argue that activation of latent capacity is the driving element of every successful technology of the past 15 years. Uber uses latent car capacity. Airbnb, latent physical space capacity. Twitter/Facebook, activate multiple latent capacities: sharing, social connections, and images. The Arab Spring, now sadly turned into a rather harsh winter, and Occupy Wall Street activate the latent power capacity of individuals. A system of control and oppression can be challenged when people take up their power, their voice.

In education exists the most substantive latent capacity in society. A classroom consists of 30 (or sometimes 300) people listening to a teacher teach. The knowledge and creative capacity of any class is stunning. Unfortunately, this knowledge is latent as the system has been architected, much like a dictatorship, to give control to one person. In many cases, students have become so accustomed to being “taught” that they are often unable, at first, to share their knowledge capacity. This is an experience that I have had in every MOOC that I’ve taught. The emphasis in MOOCs that I’ve been involved with is always on learners taking control, learners joining a network, or learners becoming creators. In a Pavolovian sense, many learners find this process disorienting and uninviting. We have been taught, after a decade+ of formal schooling, to behave and act a certain way. When someone encourages a departure from those methods, the first response is confusion, distrust or reluctance.

I’ll call my theory of knowledge and learning “100 people in a room”. If we put 100 people in a room, the latent knowledge capacity of that room in enormous. Everyone in this room has different life experiences, hobbies, interests, and knowledge. We could teach each other math, physics, calculus. We could teach poetry, different languages, and political theory. The knowledge is there, but it is disconnected and latent. Much of that knowledge is latent for two reasons: 1) We don’t know what others know, 2) connections aren’t made because we are not able with our current technologies to enable everyone to speak and be heard.

Personal Knowledge Graph

To address these shortcomings, I’ve been arguing for the development of something like a Personal Knowledge Graph (PKG). The main idea is that learners need a way to express and articulate what they know. This can be done through someone explicitly stating “I know this” or it could be mined or inferred. Learners need to own their PKG but it should be shareable with schools, companies, and peers.

Once we know what people know, we have a chance to activate latent knowledge through social and technological approaches. The work that Dragan Gasevic has done with his doctoral students indicates that learners begin to use hashtags as a cognitive agent. In some cases, a hashtag becomes a more important agent than a faculty member. In other instances, recommender systems could connect individuals who have complimentary and/or opposing knowledge graphs. This leads to new pedagogical models and changing roles for universities, notably a transition from spraying the same content to all learners to a more nuanced (knowledge gap filling?) approach.

Education is approaching where the web was in mid-2000′s – a growing range of technologies providing certain opportunities for learning and interaction, but largely fragmented. Education is waiting for it’s latent capacity activating tool, or at minimum, a means of giving each learner the ability to stitch together a coherent interpretation of a knowledge domain. Of course we need feedback loops and systems of recognition. It is not enough that I state I know something. Peers, faculty, and employers should be able to comment on my claims and I should be able to provide evidence. When I do not understand a concept correctly, there should be processes for correction.

If, when, education begins to focus on activating the knowledge of individuals rather than primarily focusing on single point knowledge pontification, new concerns will arise. For example, how can creativity be encouraged when learners receive personalized content addressing knowledge gaps? What happens to formal assessment? What role does expertise play in a room of 100 knowledgeable people? The transitions underway in society, in knowledge, and in universities, are long term and won’t be played out in the next few years. It’s a decades long transition. But it is important to begin challenging legacy assumptions and start considering, however imperfect our ability to see it today, what an education system looks like when we activate latent capacities of all participants.

Attend (online) the Learning Analytics Summer Institute

Last year, we held the Learning Analytics Summer Institute (LASI) at Stanford. This year we will hold LASI at Harvard. The event starts tomorrow and runs for three days (June 30 – July 2). Our interest and mission with the Society for Learning Analytics Research is to make data and algorithms open and accessible to researchers in order to create transparency around how analytics are being used in teaching and learning. As such, we will be live streaming LASI. The schedule of speakers, and we have an amazing set of panels and keynotes, is available now (scroll down the page for the live video feed). We are also running a global network of LASI-Locals in Hong Kong, Egypt, South Africa, Netherlands, Spain, Latin America, UK, and other regions. If you are interested in learning analytics and how they are being deployed by researchers and students, please join us online. It’s a distributed global conversation with a few thousand peers who are exploring data, analytics, and learning.

Tag for the event: #lasi14

If you blog, please follow directions here on adding to the blog feed (it’s not quite gRSShopper)

Online event: Scaling Corporate Learning

We’ve now posted additional resources for the Scaling Corporate Learning online conference (June 18 & 19):

Schedule

Speaker Bios

If interested in joining, register here

MOOC Research Initiative: Reports Released

I’m pleased to announce that the reports from the MOOC Research Initiative (MRI) are now available. In the spirit of useful science, each grantee was asked to provide a one sentence overview of their research findings.

I enjoyed working on this grant. The traditional method of exploring a knowledge domain takes years. With MOOCs, given their rapid development, this didn’t seem feasible. The approach that we took with MRI was small grants with a rapid review cycle, interim presentation of results through our MRI conference with other grantees in order to increase idea exchange, and short funding cycle. We have numerous papers that will be published in a special issue of IRRODL. Science should look more like this process, especially in fields where things are changing quickly, than the long multi-year cycle that we currently see.

New MOOC: Data, Analytics, & Learning

I’ve run a range of open courses on a fairly broad range of platforms: D2L, Moodle, Instructure, a mess of social media tools, and (most frequently) with Stephen Downes’ gRSShopper.

This fall, together with colleagues, I’ll be offering an open course on edX: Data, Analytics, and Learning. From the description:

In education, the use of data and analytics to improve learning is referred to as learning analytics. Analytics have not yet made the impact on education that they have made in other fields. That’s starting to change. Software companies, researchers, educators, and university leaders recognize the value of data in improving not only teaching and learning, but the entire education sector. In particular, learning analytics enables universities, schools, and corporate training departments to improve the quality of learning and overall competitiveness. Research communities such as the International Educational Data Mining Society (IEDMS) and the Society for Learning Analytics Research (SoLAR) are developing promising models for improving learner success through predictive analytics, machine learning, recommender systems (content and social), network analysis, tracking the development of concepts through social systems, discourse analysis, and intervention and support strategies. The era of data and analytics in learning is just beginning.

I’ll provide more information soon about the design of the course – we are focusing on dual structured and self-organized approaches to the course.

MOOCs: Scaling Corporate Learning

While MOOCs have gained the interest and attention of higher education, they have failed to make much of an impact on corporate learning. That is starting to change. Over the past year, organizations such as Google, GE, Cisco, McAfee, Bank of America, AXA, and AT&T have started to experiment with MOOCs. Non-profits and NGOs such as Linux Foundation, WEF, OECD, Red Cross, and others have also started experimenting with large scale online learning.

In trying to get a pulse of MOOCs in corporate learning, I’ve found it difficult to get a sense of what is happening broadly with MOOCs outside of higher education. I catch press releases and partnership announcements, but the conversation is too fragmented to provide a sense of lessons being learned and various implementation model. The sharing of practices and experiences in higher education is more prominent.

To address this lack of dialogue in this space, we are organizing an online conference on MOOCs: Scaling Corporate Learning. The event is free/open and will be held June 18 & 19. Registration is now open. The schedule and speaker list will be posted later this week.

MOOCs: Expectations and Reality

In spite of (because of?) significant media attention, the dialogue around MOOCs has been more theoretical than informed. Research is lagging well behind rhetoric. Fortunately, that is starting to change. Fiona Hollands and Devayani Tirthali from Teachers College, Columbia University, have released what is the most informed analysis of MOOCs that I have read to date: MOOCs: Expectations and Reality (.pdf). My only quibble is with the attempt of Andrew Ng (Coursera) to rename xMOOCs as Modern MOOCs. It’s a language game (“Freedom Fries”) that belies what anyone acquainted with learning sciences knows: the xmooc format is a pedagogical regression. Very little modern about it.

From the report:

To date, there has been little evidence collected that would allow an assessment of whether MOOCs do indeed provide a cost-effective mechanism for producing desirable educational outcomes at scale. It is not even clear that these are the goals of those institutions offering MOOCs. This report investigates the actual goals of institutions creating MOOCs or integrating them into their programs, and reviews the current evidence regarding whether and how these goals are being achieved, and at what cost.

Personal Learner Knowledge Graph

The entire education system is focused on content/curriculum. Content drives almost all academic conversations. Content is the work of designers (how should we structure this), academics (what and how should I teach), administrators (how can we prove [to some random agency] that we taught students stuff that matters), and employers (this is the content I want potential employees to master).

The content view of learning is deeply embedded in our thinking at all stages of the education system. It’s so ingrained that it is hard to NOT start a learning conversation without content as the focal point.

This content fetish is the heart of what is wrong with education. The big shift that needs to be made in education is to shift from knowing content to knowing learners. This isn’t a pablum-like argument for learner-centric education (this concept, again, starts with content, but gives lip service to learners).

What is needed in education is something like a Personal Learner Knowledge Graph (PLKG): a clear profile of what a learner knows. It doesn’t matter where the learner learned things – work, volunteering, hobbies, personal interest, formal schooling, etc. What matters is that learners are aware of what they know and how this is related to the course content/curriculum. In a sense, PLKG is like the semantic web or Google Knowledge Graph: a connected model of learner knowledge that can be navigated and assessed and ultimately “verified” by some organization in order to give a degree or designation (or something like it).

If the education system can make the transition to learner knowledge graphs, instead of mainly content, the system can start to be far more intelligent than it currently is. For example, if I’m a student who spends summer months idly consuming beverages, I will develop a different skill set than someone who spent their summer volunteering and working (see video below for a discussion I had with Steve Paikin on the Agenda). Yet when the two of us start university in fall, the system normalizes our knowledge to the curriculum. We get the same content even though we are different people with completely different skills and knowledge.

IF a learning system is based on a learner knowledge graph, the career path alone would be greatly enhanced – a learner should know where he is in relation to a variety of other fields based on the totality of his learning (i.e. “this is your progress toward a range of careers”). I’ve tried to somewhat crudely communicate this in the image below.

Video from The Agenda:

Multiple pathways: Blending xMOOCs & cMOOCs

I’m running a MOOC on edX in fall on Data Analytics & Learning (registration will be open soon). As part of this process, we organized a designjam recently bringing together 20 or so folks to think through the design process. I’ll post separately on this event. For now, I just want to highlight one aspect of the meeting: the difference between xMOOCs & cMOOCs and possible ways to blend them.

The interest in making xMOOCs more like cMOOCs (a few silly folks have called it MOOC 2.0 – haha) seems to be growing. In particular, MOOC providers are adding “social” in the same way that vitamins are added to food, “Now, with beta-carotene”! After much discussion at our designjam, I’ve concluded that cMOOCs and xMOOCs are incompatible. They cannot be blended. Pedagogically and philosophically, they are too different. It’s like trying to make a cat a dog. Entertaining, perhaps, but a fruitless venture.

Where I think xMOOCs and cMOOCs can work together is as parallel tracks where learners can navigate from one approach to another. During the designjam, I described this as needed pathways based on learner needs at different time in their learning. For example, when I engage with a new content area, I enjoy some structure and guidance. At other moments, I have random urges to create things. Learners should have freedom to bounce between structure and unstructured pathways based on personal interest.

Matt Crosslin captures these concepts in his blog post (and image below):

Journal of Learning Analytics

Interest in learning analytics is growing. It’s a data centric world and will only become more so in the future. From my biased view, it is critical that educators are aware of the role of analytics in education because of the heaving influence algorithms, data, and analytics have on teaching, learning, and decision making in schools, colleges/universities, and corporate settings.

SoLAR just announced the inaugural issue of Journal of Learning Analytics. It is an open access journal. If you’re interested in data and analytics in learning, this is the journal for you! I have a short introduction to SoLAR and the main activities of the organization and the role (we hope) it plays in bringing together technical and social domains of learning.