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Learning as Artifact Creation

Digitization is deceptive in that the deep impact isn’t readily observable. Remember when MOOCs were going to transform higher education? Or when personalized learning was going to do away with instructors? Going back about a century ago, audio, then video, was also going to disrupt education. All of these trends have been window dressing – a facade more reflective of the interests of those who advocate for them rather than a substantive departure from established norms.

Yet, change is happening, often under the radar of enthusiasts because it’s harder to sell a technology product or draw clicks to a website when being nuanced and contextual. Education is an idea/information-based process. How information is accessed, created, and shared is revealing about the future of learning. Essentially, follow information in order to understand the future of higher education. Today, information is networked and digital. University transformation and proposed innovation should align to this reality to have a broad impact – notably on student learning and the development of knowledge in society.

In 2004, I tried to respond to the network/digitization alpha trend by describing the new actions and experiences that were available to learners: Connectivism: A Learning Theory for the Digital Age. This article and work with Stephen Downes formed subsequent development of MOOCs and learning analytics.

Connectivism was presented as a theory that described how learning happened in networks, complex ambiguous information spaces, digital environments, and the increased opportunities of the participative web. Unfortunately, much of that theory remains undeveloped. Details regarding cognitive processes, teacher actions, learner mindsets, design models, and social interaction remain rudimentary. I’m confident that these will be developed over time, but progress has been slow. As a result, connectivism is something people cite rather than engage and develop into a more complex theory or framework of learning. But, whether connectivism or by some other name, a social networked model of learning is our future.

Enter the artifact…

One aspect of connectivism that has great potential for development is the role of the artifact in learning. With CCK08, we found fascinating activities arising due to student created artifacts. One student creates an image to detail the architecture of the course. Another updates it and adds to it. Another comes by and critiques it. The artifact serves as a social learning object. This process reflects my earlier point: big trends unfold behind the scenes over time and in education, they map and mirror to what people do with information that is digital and networked.

Here’s an example: Education over the past several centuries has been defined by the centrality of the instructor and the actions of a learner in relationship to what the instructor knows. There have always been voices that challenged this model – Dewey, Illich, Freire, Montessori – but the system of learning that defines our society is modeled on the assumption of learners needing to duplicate what instructors already know. Learning artifacts – a paper or a test – were largely held between the instructor and student. Group work and class presentations brought others into the relationship, but the message was still clear: the instructor and the content were central, all else was held in their orbit.

Then the internet happened. And later the web. Small groups of people could share without a mediator. You didn’t need a publisher to blast your thoughts to a bulletin board. Yahoo groups, Friendster, and other early social software didn’t fully live up to the vision of the mesh, but they enabled communication. Content creation was still largely the domain of experts or people in positions of control. Britannica and newspapers were still gatekeepers. Then, the late 1990′s rolled around and Blogger made self-publishing reasonably accessible to anyone.

We could now create artifacts, not only talk about them.

A stunning period web innovation occurred between 2000-2005: delicious, myspace, many blog platforms, flickr, wikis, etc. The gates were opened and everyone was a content creator and everything was subject to user creation. Everything was a possible social artifact. Take and share a picture. Post your thoughts on a blog. Tag and share valuable resources. The web had its velveteen rabbit moment and became real to people who had previously been unable to easy share their creative artifacts. Eventually we were blessed with the ugly stepchildren of this movement (Twitter, Facebook) that enabled flow of creative artifacts but in themselves where not primarily generative technologies.

Educationally, this provided new opportunities for students. That class lecture that didn’t make sense? There is a better resource online. That stats textbook that is confusing? There is a MOOC for that. Don’t like a class? Tell the web. Don’t like your instructor? Tell the web (rate my prof). Have an important thought to share? Upload a video to youtube. An awesome song? Upload. Share. A terrific painting you’ve been working on? Upload. Share.

Consider the impact of these opportunities on education and how poorly the higher education system has responded. Consider our curriculum as a self-contained coherent resource. The goal of education? Teach this container to the students. What happens when you add artifact creation? The entire curriculum can shift. If I lecture on the development of open learning and open source technologies, I’m presenting my voice, my priorities, my values. If someone comes along and says “what about the power structure and the bias that underpins this content”? Bam. It’s a new course. Someone creates a video reacting to a lecture I delivered? Bam. It’s a new course. This doesn’t always happen on grand scales. Often the artifact has a limited impact – a brief detour in a new conversation and learning direction for students. The aggregate of these artifacts is significant because it places students in a new mindset, one defined by personal autonomy and agency.

All of this is obvious. It’s mainly about the permissions that technology enables: namely, to write ourselves and our values into any curriculum and learning interaction. The impact that this has on the learning experience is not well understood. We have theories of community (community of inquiry, community of practice). We have many theories of content and content interaction (including transactional distance). There is something about the artifact that is unique in its ability to make every learner a teacher, every contribution a redirection of learning, every interaction a reaction and augmentation.

In one of our LINK grants we currently exploring the power of artifacts as redirective entities (an NSF grant titled COCOA). How does creating a blog post, video, or meme contribute to enlarging the curriculum? How do artifacts contribute to, and take away from, the course content? What is a well-designed artifact? What causes resonance between learner resources being shared and students that respond to those resources?

Learning Analytics Courses

After about a year of planning, we can finally announce the following courses on edX focusing on learning analytics. The intent of these courses is to eventually lead into a MicroMasters and then advance placement in an in-development Masters of Science in Learning Analytics at UTA. Each course runs about three weeks and we’ve tried to settle on prominent analytics tools for educational data so the experience is one where skills can immediately be applied.

We’ve structured these courses to provide an intro to analytics in education (a good compliment to the courses is our recently released Handbook of Learning Analytics – free download):

  • Learning Analytics Fundamentals
  • Social Network Analysis
  • Cluster Analysis
  • Predictive Modeling in Learning Analytics
  • Feature Engineering for Improving Learning Environments
  • Connecting Learning Data to Improve Instructional Design
  • Natural Language Processing and Natural Language Understanding in Educational Research
  • Knowledge Inference and Structure Discovery for Education
  • Multi-modal Learning Analytics
  • We have exceptional instructors – world leaders in the field. We are, however, well aware of the gender imbalance. We had five faculty (women) who ended up dropping out due to existing commitments. If you’d like to help right this imbalance, email me and let me know courses or topics that you’d like to instruct in the LA domain.

    Open Education: Please give

    In about a month, David Wiley and I are teaching this course on edX: Introduction to Open Education. As we are both firm adherents to social and participatory pedagogical models (i.e. we like it when others do our work), we need some help. Specifically, we’d love to have faculty/researchers/practitioners provide short 3-5 minute reflections on one or more of the following topics:

    Week 1: Why Open Matters
    Week 2: Copyright, The Public Domain, and The Commons
    Week 3: The 5R Activities and the Creative Commons Licenses
    Week 4: Creating, Finding and Using OER
    Week 5: Research on the Impact of OER Adoption
    Week 6: The Next Battles for Openness: Data, Algorithms, and Competency Mapping

    The process:
    1. Create a short video/tutorial or any other artifact (if we have yours by Sept 14, we’ll include it in the course) responding to any of the above weekly topics
    2. Upload your creation to some site where we can access/download it
    3. Email me a link (gsiemens, gmail) or share on Twitter using #openedmooc or leave a link in the comments
    4. Sit back and enjoy the feeling of accomplishment that will wash over you knowing you’ve made the world a better place.

    New Project: Digitizing Higher Education

    In fall, I’ll be running a course on edX with a few colleagues on Digitizing Higher Education. This course is part of a larger initiative that I’ll be rolling out later this month focused on helping universities transition into digital systems: University Networks.

    Here’s the pitch:

    Higher education faces tremendous change pressure and the resulting structures that are now being formed will alter the role of universities in society for the next several generations. The best time to change systems is when it is already experiencing change. A growing number of consulting agencies and service providers are starting to enter the higher education space, bringing visions that are not tightly focused on learner development and service to knowledge advancement in research domains – i.e. a shift to utilitarian views of education. I’m concerned that in the process, universities will lose control over their enterprise and will become some version of corporate lite.

    I recognize that universities need to change. They need to start with a basic question: If we were to create a model of higher education today that serves the needs of learners and society, what would it look like given our networked and technologically infused society? . The answer is not pre-existing. It’s something that we need to explore together. Societies and regions that make this change will benefit from increased employment opportunities for citizens, higher quality of life, and greater control over their future.

    The project, University Networks, involves working with a small number of universities, or specific faculties and departments, that are committed to rethinking and redesigning how they operate. My goal is to bring on 30 universities and over a period of 4 years, rethink and redesign university operations to align with the modern information and knowledge ecosystem. The intent is to impact 1 million learners over the next four years through offering innovative teaching and learning opportunities, utilizing effective learning analytics models, integrating learning across all spaces of life, and creating a digital and networked mindset to organization operations.

    A few details:

    • This is a cohort model where universities learn from each other and share those resources and practices that can be shared – for example, shared curriculum and shared quality rubrics. The cohort model enables more rapid change since the investments of all universities in the network will increase the value of the resources for everyone.
    • We provide centralized consultancy (this is a non-profit) where we enter a university for two weeks of in-depth analysis of existing practices and work with leadership to plan future investments and goals. Once this analysis is done, each university will enter one of ten modules based on their current progress. For example, a university without an LMS will enter module one whereas a university with advanced infrastructure but looking to develop online programs will enter at module four.
    • The shared consultancy and cohort model results in universities working with a fraction of the investment needed in working with a traditional corporation or consultancy firm. Clearly enabling partners will be needed and we’ll support and advise in that area as well. Our focus, however, is on rapid innovation owned and controlled by the university.
    • My motivation for this is twofold: 1. to serve the advancement of science through modern universities that reflect the information age and the changing economy. 2. to actively research systemic transformation in higher education.
    • As partners in university innovation, we (through Interlab) have deep expertise in machine learning, systemic innovation, networked learning, online learning, and digitization of organizations. More on our group here: http://interlab.me/collaboration/. What does this mean? Basically that we are committed to repositioning higher education for the modern era and that we have the skillsets to deliver on that commitment.
    • If you are interested in learning more, please email me: contact me. We are hosting an information event on June 30. We’ll provide more information at that time about the project, getting involved, and our expectation of university partners.

      We have an excellent advisory board directing this project:

    • John Galvin (Intel)
      Dror Ben-Naim (Smart Sparrow)
      Katy Borner (Indiana University)
      Al Essa (McGraw-Hill)
      Casey Green (Campus Computing Project)
      Sally Johnstone (NCHEMS)
      Mark Milliron (Civitas)
      Catherine Ngugi (Open Education Africa)
      Deborah Quazzo (GSV Advisors)
      Matt Sigelman (Burning Glass)

    Handbook of Learning Analytics (open)

    When we started the learning analytics conference in 2011, we aligned with ACM. We received a fair bit of criticism for not pursuing fully open proceedings. Some came from our sister organization, IEDMS, that has open proceedings. We made a difficult choice to go with the traditional route of quality, indexed proceedings, largely in order to ensure that colleagues from Europe and Latin America could receive funds for their travels. It’s often not understood by advocates for openness that a key challenge for researchers is to publish for impact or publish for prestige. Prestige, as defined by so called “reputable” journals, is often a requirement for getting government funding for travel.

    To ensure broader dissemination, and cope with our guilt, of our research, we set up an open journal: Journal for Learning Analytics.

    I’m very excited about a new project that started as an idea during LAK13 in Leuven and is another commitment to openness by the Society for Learning Analytics Research: The Handbook of Learning Analytics. The book, CC-licensed, weighs in at 356 pages and provides a good snapshot of the status of learning analytics as a field. It’s a free download (both the book and the chapters). Given the number of masters programs that now incorporate learning analytics courses, or a growing number of LA masters programs, we felt it was important to get a research document into the public space.

    Being Human in a Digital Age

    I’m exploring what it means to be human in a digital age and what role universities play in developing learners for this experience. Against the backdrop of everything is changing, we aren’t paying enough attention to what we are becoming. The Becoming is the central role of education in a machine learning, artificial intelligence era. It’s great to see people like Michael Wesch exploring the formative aspect of education. Randy Bass’s work on Formation by Design is also notable and important.

    I spent a few weeks in Brisbane recently working with the Faculty of Health on digital learning and how to prepare the higher education system for this new reality. On my final presentation, I focused on the needs of learners in this environment and what we need to focus on to help develop their capabilities to be adaptive and respond to continual changes. Slides are below.

    Adaptive Learners, Not Adaptive Learning

    Some variation of adaptive or personalized learning is rumoured to “disrupt” education in the near future. Adaptive courseware providers have received extensive funding and this emerging marketplace has been referred to as the “holy grail” of education (Jose Ferreira at an EdTech Innovation conference that I hosted in Calgary in 2013). The prospects are tantalizing: each student receiving personal guidance (from software) about what she should learn next and support provided (by the teacher) when warranted. Students, in theory, will learn more effectively and at a pace that matches their knowledge needs, ensuring that everyone masters the main concepts.

    The software “learns” from the students and adapts the content to each student. End result? Better learning gains, less time spent on irrelevant content, less time spent on reviewing content that the student already knows, reduced costs, tutor support when needed, and so on. These are important benefits in being able to teach to the back row. While early results are somewhat muted (pdf), universities, foundations, and startups are diving in eagerly to grow the potential of new adaptive/personalized learning approaches.

    Today’s technological version of adaptive learning is at least partly an instantiation of Keller’s Personalized System of Instruction. Like the Keller Plan, a weakness of today’s adaptive learning software is the heavy emphasis on content and curriculum. Through ongoing evaluation of learner knowledge levels, the software presents next step or adjacent knowledge that the learner should learn.

    Content is the least stable and least valuable part of education. Reports continue to emphasize the automated future of work (pfdf). The skills needed by 2020 are process attributes and not product skills. Process attributes involve being able to work with others, think creatively, self-regulate, set goals, and solve complex challenges. Product skills, in contrast, involve the ability to do a technical skill or perform routine tasks (anything routine is at risk for automation).

    This is where adaptive learning fails today: the future of work is about process attributes whereas the focus of adaptive learning is on product skills and low-level memorizable knowledge. I’ll take it a step further: today’s adaptive software robs learners of the development of the key attributes needed for continual learning – metacognitive, goal setting, and self-regulation – because it makes those decisions on behalf of the learner.

    Here I’ll turn to a concept that my colleague Dragan Gasevic often emphasizes (we are current writing a paper on this, right Dragan?!): What we need to do today is create adaptive learners rather than adaptive learning. Our software should develop those attributes of learners that are required to function with ambiguity and complexity. The future of work and life requires creativity and innovation, coupled with integrative thinking and an ability to function in a state of continual flux.

    Basically, we have to shift education from focusing mainly on the acquisition of knowledge (the central underpinning of most adaptive learning software today) to the development of learner states of being (affect, emotion, self-regulation, goal setting, and so on). Adaptive learners are central to the future of work and society, whereas adaptive learning is more an attempt to make more efficient a system of learning that is no longer needed.

    Doctor of Education: Athabasca University

    Athabasca University has the benefit of offering one of the first doctor of education programs, fully online, in North America. The program is cohort-based and accepts 12 students annually. I’ve been teaching in the doctorate program for several years (Advanced Research Methods as well as, occasionally, Teaching & Learning in DE) and supervise 8 (?!) doctoral students currently.

    Applications for the fall 2017 start are now being accepted with a January 15, 2017 deadline. Just in case you’re looking to get your doctorate :) . It really is a top program. Terrific faculty and tremendous students.

    Digital Learning Research Network Conference 2016

    As part of the Digital Learning Research Network, we held our first conference at Stanford last year.

    The conference focused on making sense of higher education. The discussions and prsentations addressed many of the critical challenges faced by learners, educators, administrators, and others. The schedule and archive are available here.

    This year, we are hosting the 2nd dLRN conference in downtown Fort Worth, October 21-22 The conference call for papers is now open. I’m interested in knowledge that exists in the gaps between domains. For dLRN15, we wanted to socialize/narrativize the scope of change that we face as a field.

    The framework of changes can’t be understood through traditional research methods. The narrative builds the house. The research methods and approaches furnish it. Last year we started building the house. This year we are outfitting it through more traditional research methods. Please consider a submission (short, relatively pain free). Hope to see you in Fort Worth, in October!

    We have updated our dLRN research website with the current projects and related partners…in case you’d like an overview of the type of research being conducted and that will be presented at #dLRN16. The eight projects we are working on:

    1. Collaborative Reflection Activities Using Conversational Agents
    2. Onboarding and Outcomes
    3. Mindset and Affect in Statistical Courses
    4. Online Readiness Modules and Student Success
    5. Personal Learning Graphs
    6. Supporting Team-Based Learning in MOOCs
    7. Utilizing Datasets to Collaboratively Create Interventions
    8. Using Learning Analytics to Design Tools for Supporting Academic Success in Higher Education

    Announcing: aWEAR Conference: Wearables and Learning

    Over the past year, I’ve been whining about how wearable technologies will have a bigger impact on how we learn, communicate, and function as a society than mobile devices have had to date. Fitness trackers, smart clothing, VR, heart rate monitors, and other devices hold promising potential in helping understand our learning and our health. They also hold potential for misuse (I don’t know the details behind this, but the connection between affective states with nudges for product purchases is troubling).

    Over the past six months, we’ve been working on pulling together a conference to evaluate, highlight, explore, and engage with prominent trends in wearable technologies in the educational process. The http://awear.interlab.me“>aWEAR conference will be held Nov 14-15 at Stanford. The call for participation is now open. Short abstracts, 500 words, are due by July 31, 2016. We are soliciting conceptual, technological, research, and implementation papers. If you have questions or are interested in sponsoring or supporting the conference, please send me an email

    From the site:

    The rapid development of mobile phones has contributed to increasingly personal engagement with our technology. Building on the success of mobile, wearables (watches, smart clothing, clinical-grade bands, fitness trackers, VR) are the next generation of technologies offering not only new communication opportunities, but more importantly, new ways to understand ourselves, our health, our learning, and personal and organizational knowledge development.

    Wearables hold promise to greatly improve personal learning and the performance of teams and collaborative knowledge building through advanced data collection. For example, predictive models and learner profiles currently use log and clickstream data. Wearables capture a range of physiological and contextual data that can increase the sophistication of those models and improve learner self-awareness, regulation, and performance.

    When combined with existing data such as social media and learning management systems, sophisticated awareness of individual and collaborative activity can be obtained. Wearables are developing quickly, including hardware such as fitness trackers, clothing, earbuds, contact lens and software, notably for integration of data sets and analysis.

    The 2016 aWEAR conference is the first international wearables in learning and education conference. It will be held at Stanford University and provide researchers and attendees with an overview of how these tools are being developed, deployed, and researched. Attendees will have opportunities to engage with different wearable technologies, explore various data collection practices, and evaluate case studies where wearables have been deployed.