So along comes Mark J. Penn with Microtrends: the small forces behind tomorrow’s big changes (2007), his new book outlining 72 microtrends — some of which could make your instruction more specific and more relevant to learners.

Working Retired

• There are over 5 million people 65+ in the workforce (in USA  as of 2005), double the number in 1980
• 3 out of 4 boomers have no intention of seeking a traditional retirement

from inquisitr.com

The workforce is aging. In Europe, the aging of the workforce is even more extreme. (The median age in Europe will be 52.7 in 2050 — it’s currently 37.7!) Some people keep working because — guess what — they don’t have the funds to retire, maybe thanks to the recent economic collapse. Some people want to keep  themselves active. People in general are living longer, and living better.

So what does that mean for learning? Older workers tend to be more resistant to eLearning, so will they require more face-to-face training? Perhaps it will just mean learning that’s adjusted to their needs, such as impaired vision or hearing. Or maybe the more experienced workforce will require less education overall, and instead will be a boon of teachers, mentors and subject matter experts. Or will younger workers get frustrated because there’s no room to grow? Or maybe the older workers will simply be part-time, or in less high-powered jobs and a different kind of educational challenge.

For every 2 million eligible to retire each year, what if 1 million decide to keep working? A microtrend becomes a major trend.

Extreme Commuters

• In 2000, 3.4 million people traveled at least 90 min to get to work

warrenwilliams.co.nz

The current average commute is 25 min (2005). But this sub-group of people travels 3x that amount! What do these people do on the road? There’s a great opportunity for audio-based learning — otherwise known as podcasts. Workers download at work and plug-in on the road. Maybe there can even be a dedicated XM channel — business learning for commuters. A series of 20 minute, well-produced, magazine format segments on leading business ideas. Or maybe just topics relevant to your organization or business.

Extreme commuters live in the exurbs for the most part — and this is can either be a lifestyle choice or economic choice. Either way — they have time on their hands and are a captive audience.

Numbers Junkies

Americans are fascinated with numbers and science. We don’t necessarily like doing math and science, but we do like watching it on TV.  Numb3rs, CSI, Freakonomics — Americans go wild for math explanations, because they make us feel smart. These shows and books have million of viewers and readers.

numberjunkies.ca

“Science and technology are all around us, and we want it demystified” (307). We want the “aha” from the numbers — from the analysis and interpretation that helps make sense of the world, that gives us ideas on how the world works.

Apply it to learning. Incorporate unexpected analytics into your learning. Put in researched, relevant tidbits on how people learn, how your organization learns. How they compare others in the industry, perhaps.

Would they be interested to know that the non-profit sector has grown 2.5%, outstripping both business (1.8%) and government (1.6%), between 1970 and 2001? That in 2001, 6 million worked in non-profit, in 2001 that figure was 12.5 million?

Incorporating math and science into instruction gets people’s attention.

…I can’t resist one more…

Yes, yes — just one more! Is short attention span a MYTH?

• The average length of a top-ten best-selling book has increased from 385 page in 1995 to 485 pages in  2005 (think of all those book clubs!)
• 1/2 million people run marathons, triathlons and other long-attention sports
• 50 million Americans do cross-word puzzles
• The 30-min infomerical is a 90 billion/year industry (oh yeah, Ron Popeil rules!)

Just think about that. Are our attention spans really getting shorter?

If you’re interested in my annotated list of Microtrends, please send me an email (ranihgill -at- gmail) or leave a comment.

Personas - courtesy galiciaCAD.com

Having been on the job market for several months now, I’ve noticed that Captivate and Articulate are required for almost every e-learning or instructional design position that I see posted. Having used Captivate, and having seen Articulate being used – I now understand why so many consider e-learning boring. These two software tools are designed to rapidly convert PowerPoint presentations into e-Learning. They also make it easy to tack a quiz onto the end of the learning. The metaphor behind the software design is “book”, is “page-turner”. The result is boring e-Learning.

Now hold on, am I just blaming the tools — especially since I haven’t really used Articulate? Am I limited by my own vision of what these tools are capable of? Possibly. Am I asking too much of e-Learning designers? Maybe. It is hard to create engaging e-Learning. Just look at my own portfolio — can’t say the learning is *that* engaging.

Research, Think, Write, Design

So here I come to my tag-line: research, write, think, design. Will this make learning more engaging? Perhaps, perhaps not. But it might make it more relevant.

• Research the business outcome. Why are you creating this learning course/widget/thing? What business outcomes are you trying to effect? What behaviors are you trying to change? What do you want people to do?!? Not just “We want people to learn this new financial software” — but “We want people to increase their efficiency and accuracy in expense reporting (or budget planning or budget management)”. This leads the question: “Well, heck, what are they doing now? Who are THEY?”
• Research the learners. Are they novices? experts? do they have different roles/needs? can you create personas from these needs? Is it possible to actually collect data on them? How technically savvy are they? How do they get their information? How do they interact with their LMS? Do they interact with the LMS? Examples of defining personas and how to use personas can be found on the Cooper Journal website. One can think of personas as meaningful customer segmentation made real by colorful descriptions — see the description of how Best Buy uses personas in designing their stores and interacting with their customers in my review of The Deciding Factor.
• Think about the research – well, can we just call this analysis? Sure, analyze you data. Concept. Examine the correlations. What can we learn from these data patterns, without making correlation errors. Clearly this is one of the areas that I need to address, in addition to designing better research.
• Write about it – does it seem redundant to write about your research and analysis? I think not. The process of writing and having to explain your research analysis is key to communication and deepening the understanding. The writing process forces the assumptions to the surface. It exposes the flaws in your argument. Writing is also key to the design process.
• Design – for me this is where you begin to explore methodologies, tools, techniques. This is where you think about learning outcomes, the learning experience. I also strongly believe that the basis of good learning design is good writing — understanding the subject matter, finding good examples, writing good scripts. The quality of the discourse matters. The writing underlying the learning design is often where it all falls apart — maybe the writer can’t imagine the learner persona. Maybe they do not fully understand the subject matter or business outcome. Good research and analysis don’t always lead to good design, but directs the design, channels the creative energies.

Yes, then we have development, where we use whatever toolset (choosing of which is part of the design process) or perhaps organizational constraints dictate what tools to use. Then the implementation, then the evaluation. Well, the evaluation should actually be a part of the research phase — if we know the business outcome, how will we know when we got there? Define success at the beginning and figure out how to effectively measure it.

For example, “We want people to increase their efficiency and accuracy in expense reporting (or budget planning or budget management)” — well there may be measurements in time required to do X, or accuracy in X (how many times to redo), or how many people to do X, what is the cost of doing X — then see if these metrics change after/during the learning. Of course, this assumes these metrics were collected in the first place for you to measure change against.

A role that incorporates this level of thinking, research, design would be ideal. Writing this down helps.

My question – Are Training & Development departments  thinking this way? Are organizations thinking this way? Is it that people just “don’t have time!” to do this level of research? You know, I don’t think so. I think much of the information is there, easy to get, is we ask the right questions. User-experience designers are already doing this. Product Management is already doing this. Let’s do it internally and not just for clients/customers. This is low-hanging fruit – but a big mental adjustment.

Predication and plans for 2010

In response to the ASTD big question this month – I offer the following

Predictions:

• Design thinking will be the buzz word for 2010. To be honest, I’m still figuring out what it means for learning. I think this all started with Tom Kelly’s IDEO: Art of Innovation book back in 2005 (see my review of this book) and continues with Tim Brown’s Change by Design. On IDEO’s blog, design thinking is described by 3 ideas: Inspiration, Iteration and Change
• Analytics will rule. I think the learning profession, especially online learning folks, will have to collect more specific data on how people are using the learning, finding patterns in the data to describe different “types” of learners. Again, still figuring this one out. (See post on analytics book review.)

Challenges:

• Finding new metaphors for learning. The metaphors of the book, and of the the classroom/course still dominate learning. What are the new metaphors? How do we move beyond these old metaphors? I think the big tool sets out there (Captivate, Articulate) push us towards these metaphors. Will there be new tools that move us in a different direction?
• Aging workforce. We’ve heard much about Gen Y. However, I think we are also dealing with an aging workforce. I need to research more stats.

Plans:

• Get work, get paid. Let there be work! Looking forward to being more fully employed in 2010, whether that be employment or more contracts: 2009 was not the best year to graduate :-}
• Design more games. Been focussed so much on the employment thing, forgot to do the thing I really wanted to explore.
• Learn more about:
  • Design thinking
  • Analytics
    
  • Metaphors for learning
  • Workforce statistics
  • Tools that break the mold(s)

Hmmm… I see many blog posts coming out of this. Happy 2010 everyone – may it be filled with blue skies and new possibilities!

I’ve recently decided to deepen my understanding of analytics, in part to think about my website and how to better promote it, and secondarily to better understand this new way of thinking about business decisions. So I thought I’d start with this book and a broad introduction to this field.

The Deciding Factor is just that — a broad introduction written for executives by two guys from the Fair Issac corporation (the group that invented the credit score.) The essential gist of this book is that we can use analysis of the tons of data collected about customers/consumers to better understand how to not just make, but to automate decisions. If you’ve ever read the book The Numerati by Stephen Baker, it is the more detailed dive into the new world of data mining and decision making that is governing many business and political decisions.

Quoting Lowell Bryan, managing partner at McKinsey & Gary Hamel author of The Future of Management, the authors bring to our attention that “increasingly the work of managers won’t be done by managers. Instead it will be pushed out to the periphery. It will be embedded in systems.”

The greatest benefit will be seen in retail operations (credit cards, banks, retail stores) — where there is a lot of data on consumer behavior, and where the primary business decisions are being made on the front-lines by retail employees or call-center folks. One of the most interesting examples the authors give is about Best Buy and how they used analytics to better understand the types of customers coming into the store, created personas for each of these behavior types: Barry, the affluent techy enthusiast; “Jill”, the busy suburban mom; “Ray” the price-conscious family man; “”Buzz”, the young gadget fiend. Best Buy then rearranged the layout of the some of its stores to better serve these types. In addition, they trained their front-line employees to ask life-style questions to uncover the needs of each of these types (“how are you going to use this product?”, “Is it for you or someone else?”.)

The three essential components of using analytics is:

1. Developing a Rules-Based System – automating high-volume operations decisions to make the decisions more consistent and increase control (such creating and understanding personas, and creating a set of questions to ask the types.)
2. Using Predictive Analytics Models – creating decision models and frameworks to mathematically evaluate the trade-offs among conflicting objectives then execute decisions; using adaptive control (the process of making the best possible decision to control a complex system based on current knowledge and learn more about how the system behaves.)
3. Connecting Decisions Across Multiple Dimensions - also known in part as cross-selling.

The use of both descriptive analytics – the process of finding relationships/patterns among data (i.e. figuring out the personas as described in the Best Buy example); and of predticive analytics – using what you do know to make informed decisions aobut what you don’t know to predict what might happen in the future – it expresses the future in terms of odds and probabilities.

The descriptions of how to make business decisions using analytics sounds similar to the way one imagines business decisions are currently made. However, there is a difference of degree and of control. In expert-driven decision making, one uses the experts and their experience. In data-informed decision-making, the experts interpret the information into a report and it informs the decisions. In data-guided, a predictive model replaces the unproven assumptions with objective information, and the model provides advisory decisions (methinks this is what happened in the investment banks). Finally in data-driven decision making, the running of analytic models and execution of decision are completely automated. The control is in the rules created to describe the system, whether that be a bank, a electronic store, or credit card business. The problems come in when the system is described incompletely and something unexpected happens.

The gist of this book is that decisions based on analytics are the way of the future. Decisions on how much credit-card increase to give people, whether to approve them for a loan, or what kind of other products can be sold to them are all automated. The rules are set by the business and math people, who interpret the data and create rules based on this interpretation. This feels very much like game-design.

To effectively use analytics you need organizations that have tons of data, and that collect the right kind of data. From this you can derive patterns and understand how to better make decisions, make suggestions based on these analytics. But you have to have good data, and thoughtful people making the rules, otherwise you may end up creating a system that controls you, rather than informs you. It’s a new way of thinking of understanding the world.

This book is a basic introduction to this world — at this point, I feel I need more in-depth thoughtful exploration of how it works best in the web world. Onto the next read.