SMAC it!

The current decade has started to witness a fast changing landscape in technological innovations, some of which have brought about sweeping changes in everyday activities. If you look at a typical day in the life of a tech-savvy individual (either a college-going student or a working professional), you will find them operating in ways you’d not imagined a few years ago. Now come on, did you foresee people around you move about carrying a computer chip in their eyeglasses or wrist watches? At least two of my techie colleagues are seen donning the Google Glass in office these days, and I know of at least one friend—a fitness fanatic—working out with all these fancy wearable gadgets and checking in his progress on Facebook.

To take stock of the changes—small and big—that have stealthily crept in and comfortably fitted into our everyday life, let’s meet with Susan, a sales executive from a multinational corporation.

So, what does a typical day in her life look like? Let’s sneak a peek …


Looks familiar, doesn’t it? Like any other digitally savvy individual, Susan’s daily routine is strongly influenced by elements of the social and mobile worlds. Then, a crucial question that comes tom my mind as a learning designer is why should her learning environment be any different? Why can’t Susan’s learning use these very elements and mimic her real life?

Picture this …


Wouldn’t the merging of her personal and learning spaces result in an enhanced experience?

Accept it—the 21st century modern learners are clearly very different from what we’ve seen in the past. Their life is largely influenced by the Social, Cloud, and Mobile worlds. Add “Analytics” to this list and you get what is now a popular and fairly impactful acronym—SMAC—in the IT services world.

SMAC is influencing this digital generation so much that they now have shorter attention spans and, therefore, end up demanding information in smaller bites, albeit very fast and preferably on the go. When these new-age learners look for learning, they go to Google, YouTube, TED talks, Khan Academy, and, for the last couple of years, MOOCs! Increasingly, these learners want their learning experiences to match the pace and style of their life.

Undoubtedly, the Social and Cloud-based learning environments powered by Analytics and Mobile First design are characterizing and influencing the way learners learn today. These individuals increasingly want to merge their personal and learning spaces, so that their leaning experiences mimic their everyday life. Most importantly, these learners foster a culture of continuous and continual learning by:

  • Learning from a constant stream of knowledge and information
  • Collaborating to share knowledge, experiences, ideas, and resources as part of their everyday life
  • Extracting learning from their everyday activities both at work and in their personal life

And how best can learning design address the needs of the SMAC learner?

At the least, we need to start attempting to change, to swing around, and to shift elements in our design thinking. We need to move from a narrow “design a training” approach to thinking holistically in terms of “providing an integrated learning ecosystem” that should, at the least, provide for the following:

  • Unique learning experience
    • Self-driven and personalized paths
    • Micro (byte-sized) and pervasive learning
    • Peer to peer collaboration
    • Integration of social media and/or social media-like elements
    • Curation and dynamic building of content
    • Live projects and practical application
  • Scalability and Cost effectiveness
    • Should cost less to develop (by integrating open resources)
    • Longer shelf life and sustainability of training materials
    • Option for an Instructor-less delivery
    • Cloud-based deployment
    • Flexible and easy access via mobile devices

Simply put, we need to SMAC it!

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MOOCs as an Experiment

From among the providers of MOOCs, edX is more actively focused on treating MOOCs as an experiment—to better the quality of education. And, as a step forward in their endeavor, they recently announced a partnership with Google to build and operate, an open source learning platform. Read the press release or visit for more.

Coming back to the mission of improving learning, let’s run through the many initiatives—some predicted as disruptive—currently buzzing in the higher ed world. MOOCs and Adaptive Learning (and therefore learning analytics) usually top the list; another effort that is notable is the move toward competency-based education, and catching up with these efforts are digital badges for informal learning. Incidentally, WCET, the Mozilla Foundation, Blackboard, Inc., and Sage Road Solutions LLC are running a 6-Week long MOOC exploring badges as the emerging currency for professional credentials.

Additionally, the Innovating Pedagogy Report (2013) by the Open University lists the following themes: Seamless Learning (which I’d call “learning on the go”), Crowd Learning, Digital Scholarship, Geo Learning, Learning from Gaming, Maker Culture, and Citizen Inquiry.

Of all the innovations listed, the report clearly outlines MOOCs and Learning analytics with silver linings but also goes on to note that for these innovations to succeed, they need to complement formal education, rather than disrupt it.

“By bringing together MOOCs (as massive test beds for experiment outside traditional education) and learning analytics (as the means to provide dynamic evidence of the effectiveness of different teaching and learning methods) there is an opportunity for rapid, evidence-informed innovation on a grand scale” concludes the report. It describes MOOCs as an innovating pedagogy that brings together other innovations. Indeed, the integration of all these innovations so as to make them work in tandem is what forms the blueprint of the future learning ecosystem.

So let’s look at some of the various on-going experiments with MOOCs …

1. Awarding Badges: Early this year, the OLDS MOOC focusing on curriculum design for OERs experimented with awarding badges, which were also compatible with the Mozilla Open Badge Backpack. In a way, badges serve MOOCs well as they help recognize learner efforts (and achievements) in the course (thereby taking away the burden of formalizing every single MOOC with certification) and also help enhance the engagement levels, which in turn should improve completion rates. Badges have multiple uses – from the learning provider side, badges can help faculty and universities identify the right candidates for enrolment and further engagement to prepare them for careers; from the learner side, they will be able to demonstrate skills and granular learning, even the learning acquired on the job.

2. Integrating Social Media: From using hashtags to facilitate discussions on Twitter, to sharing on a variety of platforms such as Flickr, and forming online communities, faculty have already started to experiment with social media in their classrooms and online courses. Given that the millennials live and breathe social media, teachers have found this as a means to engage students actively in the learning and to help them generate newer ideas. A recent post titled “5 technologies to promote creative learning” on the Learning with ‘e’s blog by Steve Wheeler (an Associate Professor at Plymouth University) presents some good examples of how infusing wikis, twitter, and video mashups can take the learning experience a step further.

3. Mobile Learning: FutureLearn, the UK-based MOOC provider, has already announced that their MOOCs will be optimized for mobile devices, and in a recent interview, Daphne Koller stated that Coursera has started building up a mobile-devices team. Making a full course available on a mobile device and designing for mobile, however, are two different things. My colleague Tanya D’souza from Tata Interactive Systems has made an attempt to differentiate between the two in her white paper on “Creating Mobile Learning That Works.”

4. Gamification: Anant Agarwal of edX often talks about integrating the principles of gaming and interaction into MOOCs. In my white paper on “Designing MOOCs,” I’ve also listed some benefits of integrating sophisticated e-learning technologies such as games, simulations, and 3D into learning. Here’s a useful video I found on YouTube of Amy Jo Kim, CEO, Shufflebrain, talking about core concepts for smart gamification.

5. Learning Analytics: The current MOOC platforms are all capturing volumes of student data and claiming to using this data for research and examining the fundamentals of learning. The NMC Horizon Report of 2011 noted that the time to adoption for learning analytics will be about 4-5 years. A lot has happened since then. Here’s a blog post that delves into details, of particularly two components: machine grading and learning analytics, of edX’s technology platform.

Generally speaking…how individualized is the learning?

I was at Britto’s, the famous bar and restaurant in Goa, with a bunch of friends waiting to savor some fish curry, when one comrade at the table sparked out a debate about the perils of generalization. The group was talking about people and stereotypes in general when this close friend of mine argued “…but how can you generalize? Not all people from so and so country or so and so community act in the exact same manner and your generalized view about them is unfair!” “I know…they don’t!” I argued back “and yet there are some general trends about them…” Our argument could’ve easily taken a serious turn had we not been on vacation and at that point in time, starving! So, when the food arrived, we decided to dive straight in (to the food) and thereafter continued to joke about our argument throughout our trip, by prefixing every other statement with “generally speaking…!”

The fun argument with my friends stayed with me even after I returned from Goa more so because this very question of “how can we generalize if what we are aiming at is to individualize?” had been on my mind from the time I began reading Big Data – A Revolution That Will Transform How We Live, Work, And Think – a book by Viktor Mayer – Schönberger and Kenneth Cukier. In the very first chapter of his book, the authors inform their readers about a key shift (amongst others) in thinking when it comes to big data and that is about losing out on the exactitude. The author states that with small amounts of data, the results are more accurate but the bigger the data gets, the more the inaccuracies.

I am reading about big data because I am curious about learning analytics, which in turn relates to adaptive learning. And my interest in the topic is from the perspective of learning design – how does learning design draw from learning analytics to create an individualized path for the learner? Incidentally, BJET, the British Journal of Educational Technology is calling for white papers for its special issue on integrating learning design, teacher-led inquiry, and learning analytics, which will be published in January 2015. I am oh so looking forward to finding many answers in the literature that will emerge from this initiative.

So, coming back to my question— can learning analytics lead us to the preferences of the individual student? Or does it only create categories and slots each student into a category? In effect, are we still looking at generalizing? Although, this type of generalization is still better than “one size fits all,” but does this generalization lead to the individualization that is possible via a one-on-one between the teacher and student?

A couple of years ago, I worked on a project that involved developing an online degree program for students with Autism Syndrome Disease (ASD). And the USP of this program, as promoted by the college, was an “individualized learning path” for each student, while maintaining academic integrity. The key aspects of the program that contributed to individualization included the following:

    Content delivery in multiple formats to suit a variety of learning styles
    Flexibility in assessment and participation modalities
    One-on-one support from qualified behavior analysts as mentors who act as liaisons between the faculty and the students

Each mentor would be assigned 3-5 students and then the mentor would work with the faculty to customize and individual all aspects of the learning path (including administrative and use of technology) for the students.

Can learning analytics help achieve this?

Although the broader benefits of learning analytics are directed toward bringing in efficiency to the teaching/learning process and to enhance the quality of education ensuring learner success, personalization of the learning process, instructional design, and content is achievable, provided the learning materials are designed to reflect the knowledge architecture of a domain.

Check out some useful links for insights on this topic.

Learning Analytics – Beginner’s notes

I was reviewing the video streaming of the “Why Big Data & Analytics could transform the Learning Sciences and Education” session by Dragan Gasevic, Athabasca University in the Learning Analytics Summer Institutes (LASI) 2013 e-room, and a specific slide on “What values to promote?” made me pause for a bit. The context for the slide (and the entire presentation) was on Learning Analytics and this particular slide listed the following values:

Individualization, contextualization, and socialization

As I listened to the speaker explain these values in the context of how learning analytics can be used to promote them, I tried to make connections to how I’ve made the same values the basis of the instructional design philosophy for designing MOOCs – Massive Open Online Courses. The following diagram from my recently published white paper on “Instructional Design for MOOCs” summarizes the characteristics of a MOOC learning environment that form the basis of design thinking for MOOCs.


My interest in learning analytics (and therefore Big Data) is in its nascent stage and I am trying to make sense of this new fad in the higher ed world and how it impacts learning design. I’ve just started to read about this topic and so am sharing some beginners notes – a few terms and their definitions.

Big Data – “… datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze” Manyika et al. (2011)

Analytics: An overarching concept that is defined as data-driven decision making. van Barneveld, Arnold, & Campbell, 2012 adapted from Ravishanker

Business/Academic Analytics: A process for providing higher education institutions with the data necessary to support operational and financial decision making. van Barneveld, Arnold, & Campbell, 2012 adapted from Goldstein and Katz

Educational Data Mining (EDM): A process for analyzing data collected during teaching and learning to test learning theories and inform educational practice. Bienkowski, Feng, Means, 2012.

Learning Analytics: The use of analytical techniques to help target instructional, curricular, and support resources to support the achievement of specific learning goals through applications that directly influence educational practice. van Barneveld, Arnold, & Campbell, 2012 adapted from Bach.

Predictive Analytics: Uncover relationships and patterns, and can be used to predict behavior and events.

Visual Data Analytics: Discovering and understanding patterns in large datasets using visual interpretation.