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2015 Reflections

31 December 2015 by Clark 3 Comments

It’s the end of the year, and given that I’m an advocate for the benefits of reflection, I suppose I better practice what I preach. So what am I thinking I learned as a consequence of this past year? Several things come to mind (and I reserve the right for more things to percolate out, but those will be my 2016 posts, right? :):

The Revolution is real: the evidence mounts that there is a need for change in L&D, and when those steps are taken, good things happen. The latest Towards Maturity report shows that the steps taken by their top-performing organizations are very much about aligning with business, focusing on performance, and more. Similarly, Chief Learning Officer‘s Learning Elite Survey similarly point out to making links across the organization and measuring outcomes. The data supports the principled observation.
The barriers are real: there is continuing resistance to the most obvious changes. 70:20:10, for instance, continues to get challenged on nonsensical issues like the exactness of the numbers!?!? The fact that a Learning Management System is not a strategy still doesn’t seem to have penetrated. And so we’re similarly seeing that other business units are taking on the needs for performance support, social media, and ongoing learning. Which is bad news for L&D, I reckon.
Learning design is rocket science: (or should be). The perpetration of so much bad elearning continues to be demonstrated at exhibition halls around the globe. It’s demonstrably true that tarted up information presentation and knowledge test isn’t going to lead to meaningful behavior change, but we still are thrusting people into positions without background and giving them tools that are oriented at content presentation. Somehow we need to do better. Still pushing the Serious eLearning Manifesto.
Mobile is well on it’s way: we’re seeing mobile becoming mainstream, and this is a good thing. While we still hear the drum beating to put courses on a phone, we’re also seeing that call being ignored. We’re instead seeing real needs being met, and new opportunities being explored. There’s still a ways to go, but here’s to a continuing awareness of good mobile design.
Gamification is still being confounded: people aren’t really making clear conceptual differences around games. We’re still seeing linear scenarios confounded with branching, we’re seeing gamification confounded with serious games, and more. Some of these are because the concepts are complex, and some because of vested interests.
Games seem to be reemerging: while the interest in games became mainstream circa 2010 or so, there hasn’t been a real sea change in their use. However, it’s quietly feeling like folks are beginning to get their minds around Immersive Learning Simulations, aka Serious Games. There’s still ways to go in really understanding the critical design elements, but the tools are getting better and making them more accessible in at least some formats.
Design is becoming a ‘thing’: all the hype around Design Thinking is leading to a greater concern about design, and this is a good thing. Unfortunately there will probably be some hype and clarity to be discerned, but at least the overall awareness raising is a good step.
Learning to learn seems to have emerged: years ago the late great Jay Cross and I and some colleagues put together the Meta-Learning Lab, and it was way too early (like so much I touch :p). However, his passing has raised the term again, and there’s much more resonance. I don’t think it’s necessarily a thing yet, but it’s far greater resonance than we had at the time.
Systems are coming: I’ve been arguing for the underpinnings, e.g. content systems. And I’m (finally) beginning to see more interest in that, and other components are advancing as well: data (e.g. the great work Ellen Wagner and team have been doing on Predictive Analytics), algorithms (all the new adaptive learning systems), etc. I’m keen to think what tags are necessary to support the ability to leverage open educational resources as part of such systems.
Greater inputs into learning: we’ve seen learning folks get interested in behavior change, habits, and more. I’m thinking we’re going to go further. Areas I’m interested in include myth and ritual, powerful shapers of culture and behavior. And we’re drawing on greater inputs into the processes as well (see 7, above). I hope this continues, as part of learning to learn is to look to related areas and models.

Obviously, these are things I care about. I’m fortunate to be able to work in a field that I enjoy and believe has real potential to contribute. And just fair warning, I’m working on a few areas in several ways. You’ll see more about learning design and the future of work sometime in the near future. And rather than generally agitate, I’m putting together two specific programs – one on (e)learning quality and one on L&D strategy – that are intended to be comprehensive approaches. Stay tuned.

That’s my short list, I’m sure more will emerge. In the meantime, I hope you had a great 2015, and that your 2016 is your best year yet.

Scenarios and Conceptual Clarity

10 December 2015 by Clark 5 Comments

I recently came across an article ostensibly about branching scenarios, but somehow the discussion largely missed the point. Ok, so I can be a stickler for conceptual clarity, but I think it’s important to distinguish between different types of scenarios and their relative strengths and weaknesses.

So in my book Engaging Learning, I was looking to talk about how to make engaging learning experiences. I was pushing games (and still do) and how to design them, but I also wanted to acknowledge the various approximations thereto. So in it, I characterized the differences between what I called mini-scenarios, linear scenarios, and contingent scenarios (this latter is what’s traditionally called branching scenarios). These are all approximations to full games, with various tradeoffs.

At core, let me be clear, is the need to put learners in situations where they need to make decisions. The goal is to have those decisions closely mimic the decisions they need to make after the learning experience. There’s a context (aka the story setting), and then a specific situation triggers the need to make a decision. And we can deliver this in a number of ways. The ideal is a simulation-driven (aka model-driven or engine-driven) experience. There’s a model of the world underneath that calculates the outcomes of your action and determines whether you’ve yet achieved success (or failure), or generates a new opportunity to act. We can (and should) tune this into a serious game. This gives us deep experience, but the model-building is challenging and there are short cuts.

In mini-scenarios, you put the learner in a setting with a situation that precipitates a decision. Just one, and then there’s feedback. You could use video, a graphic novel format, or just prose, but the game problem is a setting and a situation, leading to choices. Similarly, you could have them respond by selecting option A B or C, or pointing to the right answer, or whatever. It stops there. Which is the weakness, because in the real world the consequences are typically more complex than this, and it’s nice off the learning experience reflects that reality. Still, it’s better than knowledge test. Really, these are just a better written multiple choice question, but that’s at least a start!

Linear scenarios are a bit more complex. There are a series of game problems in the same context, but whatever the player chooses, the right decision is ultimately made, leading to the next problem. You use some sort of sleight of hand, such as “a supervisor catches the mistake and rectifies it, informing you…” to make it all ok. Or, you can terminate out and have to restart if you make the wrong decision at any point. These are a step up in terms of showing the more complex consequences, but are a bit unrealistic. There’s some learning power here, but not as much as is possible. I have used them as sort of multiple mini-scenarios with content in between, and the same story is used for the next choice, which at least made a nice flow. Cathy Moore suggests these are valuable for novices, and I think it’s also useful if everyone needs to receive the same ‘test’ in some accreditation environment to be fair and balanced (though in a competency-based world they’d be better off with the full game).

Then there’s the full branching scenario (which I called contingent scenarios in the book, because the consequences and even new decisions are contingent on your choices). That is, you see different opportunities depending on your choice. If you make one decision, the subsequent ones are different. If you don’t shut down the network right away, for instance, the consequences are different (perhaps a breach) than if you do (you get the VP mad). This, of course, is much more like the real world. The only difference between this and a serious game is that the contingencies in the world are hard-wired in the branches, not captured in a separate model (rules and variables). This is easier, but it gets tough to track if you have too many branches. And the lack of an engine limits the replay and ability to have randomness. Of course, you can make several of these.

So the problem I had with the article that triggered this post is that their generic model looked like a mini-scenario, and nowhere did they show the full concept of a real branching scenario. Further, their example was really a linear scenario, not a branching scenario. And I realize this may seem like an ‘angels dancing on the head of a pin’, but I think it’s important to make distinctions when they affect the learning outcome, so you can more clearly make a choice that reflects the goal you are trying to achieve.

To their credit, that they were pushing for contextualized decision making at all is a major win, so I don’t want to quibble too much. Moving our learning practice/assessment/activity to more contextualized performance is a good thing. Still, I hope this elaboration is useful to get more nuanced solutions. Learning design really can’t be treated as a paint-by-numbers exercise, you really should know what you’re doing!

The new shape of organizations?

20 October 2015 by Clark 2 Comments

As I read more about how to create organizations that are resilient and adaptable, there’s an interesting emergent characteristic. What I’m seeing is a particular pattern of structure that has arisen out of totally disparate areas, yet keeps repeating. While I haven’t had a chance to think about it at scale, like how it would manifest in a large organization, it certainly bears some strengths.

Dave Grey, in his recent book The Connected Company that I reviewed, has argued for a ‘podular’ structure, where small groups of people are connected in larger aggregations, but work largely independently. He argues that each pod is a small business within the larger business, which gives flexibility and adaptiveness. Innovation, which tends to get stifled in a hierarchical structure, can flourish in this more flexible structure.

More recently, on Harold Jarche‘s recommendation, I read Niels Pflaeging’s Organize for Complexity, a book also on how to create organizations that are high performance. While I think the argument was a bit sketchy (to be fair, it’s deliberately graphic and lean), I was sold on the outcomes, and one of them is ‘cells’ composed of a small group of diverse individuals accomplishing a business outcome. He makes clear that this is not departments in a hierarchy, but flat communication between cross-functional teams.

And, finally, Stan McChrystal has a book out called Team of Teams, that builds upon the concepts he presented as a keynote I mindmapped previously. This emerged from how the military had to learn to cope with rapid changes in tactics. Here again, the same concept of small groups working with a clear mission and freedom to pursue emerges.

This also aligns well with the results implied by Dan Pink’s Drive, where he suggests that the three critical elements for performance are to provide people with important goals, the freedom to pursue them, and support to succeed. Small teams fit well within what’s known about the best in getting the best ideas and solutions out of people, such as brainstorming.

These are nuances on top of Jon Husband’s Wirearchy, where we have some proposed structure around the connections. It’s clear that to become adaptive, we need to strengthen connections and decrease structure (interestingly, this also reflects the organizational equivalents of nature’s extremophiles). It’s about trust and purpose and collaboration and more. And, of course, to create a culture where learning is truly welcomed.

Interesting that out of responding to societal changes, organizational work, and military needs, we see a repeated pattern. As such, I think it’s worth taking notice. And there are clear L&D implications, I reckon. What say you?

#itashare

Supporting our Brains

13 October 2015 by Clark 5 Comments

One of the ways I’ve been thinking about the role mobile can play in design is thinking about how our brains work, and don’t. It came out of both mobile and the recent cognitive science for learning workshop I gave at the recent DevLearn. This applies more broadly to performance support in general, so I though I’d share where my thinking is going.

To begin with, our cognitive architecture is demonstrably awesome; just look at your surroundings and recognize your clothing, housing, technology, and more are the product of human ingenuity. We have formidable capabilities to predict, plan, and work together to accomplish significant goals. On the flip side, there’s no one all-singing, all-dancing architecture out there (yet) and every such approach also has weak points. Technology, for instance, is bad at pattern-matching and meaning-making, two things we’re really pretty good at. On the flip side, we have some flaws too. So what I’ve done here is to outline the flaws, and how we’ve created tools to get around those limitations. And to me, these are principles for design:

table of cognitive limitations and support tools So, for instance, our senses capture incoming signals in a sensory store. Which has interesting properties that it has almost an unlimited capacity, but for only a very short time. And there is no way all of it can get into our working memory, so what happens is that what we attend to is what we have access to. So we can’t recall what we perceive accurately. However, technology (camera, microphone, sensors) can recall it all perfectly. So making capture capabilities available is a powerful support.

Similar, our attention is limited, and so if we’re focused in one place, we may forget or miss something else. However, we can program reminders or notifications that help us recall important events that we don’t want to miss, or draw our attention where needed.

The limits on working memory (you may have heard of the famous 7 ±2, which really is <5) mean we can’t hold too much in our brains at once, such as interim results of complex calculations. However, we can have calculators that can do such processing for us. We also have limited ability to carry information around for the same reasons, but we can create external representations (such as notes or scribbles) that can hold those thoughts for us. Spreadsheets, outlines, and diagramming tools allow us to take our interim thoughts and record them for further processing.

We also have trouble remembering things accurately. Our long term memory tends to remember meaning, not particular details. However, technology can remember arbitrary and abstract information completely. What we need are ways to look up that information, or search for it. Portals and lookup tables trump trying to put that information into our heads.

We also have a tendency to skip steps. We have some randomness in our architecture (a benefit: if we sometimes do it differently, and occasionally that’s better, we have a learning opportunity), but this means that we don’t execute perfectly. However, we can use process supports like checklists. Atul Gawande wrote a fabulous book on the topic that I can recommend.

Other phenomena include that previous experience can bias us in particular directions, but we can put in place supports to provide lateral prompts. We can also prematurely evaluate a solution rather than checking to verify it’s the best. Data can be used to help us be aware. And we can trust our intuition too much and we can wear down, so we don’t always make the best decisions. Templates, for example are a tool that can help us focus on the important elements.

This is just the result of several iterations, and I think more is needed (e.g. about data to prevent premature convergence), but to me it’s an interesting alternate approach to consider where and how we might support people, particularly in situations that are new and as yet untested. So what do you think?

AI and Learning

7 October 2015 by Clark Leave a Comment

At the recent DevLearn, Donald Clark talked about AI in learning, and while I largely agreed with what he said, I had some thoughts and some quibbles. I discussed them with him, but I thought I’d record them here, not least as a basis for a further discussion.

Donald’s an interesting guy, very sharp and a voracious learner, and his posts are both insightful and inciteful (he doesn’t mince words ;). Having built and sold an elearning company, he’s now free to pursue what he believes and it’s currently in the power of technology to teach us.

As background, I was an AI groupie out of college, and have stayed current with most of what’s happened. And you should know a bit of the history of the rise of Intelligent Tutoring Systems, the problems with developing expert models, and current approaches like Knewton and Smart Sparrow. I haven’t been free to follow the latest developments as much as I’d like, but Donald gave a great overview.

He pointed to systems being on the verge of auto parsing content and developing learning around it. He showed an example, and it created questions from dropping in a page about Las Vegas. He also showed how systems can adapt individually to the learner, and discussed how this would be able to provide individual tutoring without many limitations of teachers (cognitive bias, fatigue), and can not only personalize but self-improve and scale!

One of my short-term problems was that the questions auto-generated were about knowledge, not skills. While I do agree that knowledge is needed (ala VanMerriÃ«nboer’s 4CID) as well as applying it, I think focusing on the latter first is the way to go.

This goes along with what Donald has rightly criticized as problems with multiple-choice questions. He points out how they’re largely used as knowledge test, and I agree that’s wrong, but while there are better practice situations (read: simulations/scenarios/serious games), you can write multiple choice as mini-scenarios and get good practice. However, it’s as yet an interesting research problem, to me, to try to get good scenario questions out of auto-parsing content.

I naturally argued for a hybrid system, where we divvy up roles between computer and human based upon what we each do well, and he said that is what he is seeing in the companies he tracks (and funds, at least in some cases). A great principle.

The last bit that interested me was whether and how such systems could develop not only learning skills, but meta-learning or learning to learn skills. Real teachers can develop this and modify it (while admittedly rare), and yet it’s likely to be the best investment. In my activity-based learning, I suggested that gradually learners should take over choosing their activities, to develop their ability to become self-learners. I’ve also suggested how it could be layered on top of regular learning experiences. I think this will be an interesting area for developing learning experiences that are scalable but truly develop learners for the coming times.

There’s more: pedagogical rules, content models, learner models, etc, but we’re finally getting close to be able to build these sorts of systems, and we should be aware of what the possibilities are, understanding what’s required, and on the lookout for both the good and bad on tap. So, what say you?

Mobile Time

6 October 2015 by Clark 1 Comment

At the recent DevLearn conference, David Kelly spoke about his experiences with the Apple Watch. Because I don’t have one yet, I was interested in his reflections. There were a number of things, but what came through for me (and other reviews I’ve read) is that the time scale is a factor.

Now, first, I don’t have one because as with technology in general, I don’t typically acquire anything in particular until I know how it’s going to make me more effective. I may have told this story before, but for instance I didn’t wasn’t interested in acquiring an iPad when they were first announced (“I’m not a content consumer“). By the time they were available, however, I’d heard enough about how it would make me more productive (as a content creator), that I got one the first day it was available.

So too with the watch. I don’t get a lot of notifications, so that isn’t a real benefit. The ability to be navigated subtly around towns sounds nice, and to check on certain things. Overall, however, I haven’t really found the tipping-point use-case. However, one thing he said triggered a thought.

He was talking about how it had reduced the amount of times he accessed his phone, and I’d heard that from others, but here it struck a different cord. It made me realize it’s about time frames. I’m trying to make useful conceptual distinctions between devices to try to help designers figure out the best match of capability to need. So I came up with what seemed an interesting way to look at it.

Various usage times by category: wearable, pocketable, bag able. This is similar to the way I’d seen Palm talk about the difference between laptops and mobile, I was thinking about the time you spent in using your devices. The watch (a wearable) is accessed quickly for small bits of information. A pocketable (e.g. a phone) is used for a number of seconds up to a few minutes. And a tablet tends to get accessed for longer uses (a laptop doesn’t count). Folks may well have all 3, but they use them for different things.

Sure, there are variations, (you can watch a movie on a phone, for instance; phone calls could be considerably longer), but by and large I suspect that the time of access you need will be a determining factor (it’s also tied to both battery life and screen size). Another way to look at it would be the amount of information you need to make a decision about what to do, e.g. for cognitive work.

Not sure this is useful, but it was a reflection and I do like to share those. I welcome your feedback!

Embrace Plan B

17 June 2015 by Clark Leave a Comment

The past two weeks, I’ve been on the road (hence the paucity of posts). And they’ve been great opportunities to engage around interesting topics, but also have provided some learning opportunities (ahem). The title of this post, by the way, came from m’lady, who was quoting what a senior Girl Scout said was the biggest lesson she learned from her leader, “to embrace Plan B” ;).

So two weeks ago I was visiting a client working on upping their learning game. This is a challenge in a production environment, but as I discussed many times in posts over the second half of 2014 and some this year, I think there are some serious actions that can be taken. What is needed are better ways to work with SMEs, better constraints around what makes useful content, and perhaps most importantly what makes meaningful interaction and practice. I firmly believe that there are practical ways to get serious elearning going without radical change, though some initial hiccups will be experienced.

This past week I spoke twice. First on a broad spectrum of learning directions to a group that was doing distance learning and wanted to take a step back and review what they’d been doing and look for improvement opportunities. I covered deeper learning, social learning, meta-learning, and more. Then I went beyond and talked about 70:20:10, measurement, games and simulations, mlearning, the performance ecosystem, and more. I then moved on to a separate (and delightful) event in Vancouver to promote the Revolution.

It was the transition between the two events last week that threw me. So, Plan A was to fly back home on Tuesday, and then fly on to Vancouver on Wed morning. But, well, life happened. All my flights were delayed (thanks, American) on my flight there and back to the first engagement, and both of the first flights such that I missed the connection. On the way out I just got in later than I expected (leading to 4.5 hours sleep before the long and detailed presentation). But on the way back, I missed the last connecting flight home. And this had several consequences.

So, instead of spending Tuesday night in my own bed, and repacking for the next day, I spent the night in the Dallas/Fort Worth airport. Since they blamed it on weather (tho’ if the incoming flight had been on time, it might’ve gotten out in time to avoid the storm), they didn’t have any obligation to provide accommodation, but there were cots and blankets available. I tried to pull into a dark and quiet place, but most of the good ones were taken already. I found a boarding gate that was out of the way, but it was bright and loud. I gave up after an hour or so and headed off to another area, where I found a lounge where I could pull together a couple of armchairs and managed to doze for 2.5 or so hours, before getting up and on the hunt for some breakfast. Lesson: if something’s not working, change!

I caught a flight back home in just enough time to catch the next one up to Vancouver. The problem was, I wasn’t able to swap out my clothes, so I was desperately in need of some laundry. Upon arriving, I threw one of the shirts, socks, etc into a sink and gave them a wash and hung them up. (I also took a shower, which was not only a necessity after a rough night but a great way to gather myself and feel a bit more human). The next morning, as I went to put on the shirt, I found a stain! I couldn’t get up in front of all those people with a stained shirt. Plan B was out the door. Also, the other shirt had acquired one too! Plan C on the dust heap. Now what? Fortunately, my presentation was in the afternoon, but I needed to do something.

So I went downstairs and found a souvenir shop in the hotel, but the shirts were all a wee bit too loud. I didn’t really want to pander to the crowd quite so egregiously. I asked at the hotel desk if there was a place I could buy a shirt within walking distance, and indeed there was. I was well and truly on Plan D by this time. So I hiked on out to a store and fortunately found another shirt I could throw on. Lesson: keep changing!

I actually made the story part of my presentation. I made the point that just like in my case, organizations need not only optimal execution of the plans, but then also the ability to innovate if the plan isn’t working. And L&D can (and should) play a role in this. So, help your people be prepared to create and embrace Plan B (and C and…however many adaptations they need to have).

And one other lesson for me: be better prepared for tight connections to go awry!

Trojan Mice?

6 May 2015 by Clark Leave a Comment

One of the mantras of the Learning Organization is that there should be experimentation. This has also become, of course, a mantra of the Revolution as well. So the question becomes, what sort of experiments should we be considering?

First, for reasons both pragmatic and principled, these are more likely to be small experiments than large. On principled reasons, even large changes are probably better off implemented as small steps. On pragmatic reasons, small changes can be built upon or abandoned as outcomes warrant. These small changes have colloquially been labeled ‘trojan mice‘, a cute way to capture the notion of change via small incursions.

The open question, then, is what sort of trojan mice might be helpful in advancing the revolution? We might think of them in each of the areas of change: formal, performance support, social, culture, etc. What are some ideas?

In formal, we might, for one, push back on taking orders. For instance, we might start asking about measures that any initiatives will be intended to address. We could also look to implementing some of the Serious eLearning Manifesto ideas. Small steps to better learning design.

For performance support, one of the first small steps might be to even do performance support, if you aren’t already. If you are, maybe look to broadening the media you use (experiment with a video, an annotated sequence of pictures, or an ebook). Or maybe try creating a portal that is user-focused, not business-silo structured.

In the social area, you might first have to pilot an exterior social network if there isn’t one. If there is, you might start hosting activities within it. A ‘share your learning lunch’ might be a fun way to talk about things, and bring out meta-learning. Certainly, you could start instituting the use within L&D.

And with culture, you might start encouraging people to share how they work; what resources they use. Maybe film the top performers in a group giving a minute or two talk on how they do what they do. It’d be great if you could get some of the leadership to start sharing, and maybe do a survey of what your culture actually is.

The list goes on: in tech you might try some microlearning, a mobile experiment, or considering a content model (ok, not actually build one, that’s a big step ;). In strategy, you might start gathering data about what the overall organization goals are, or what initiatives in infrastructure have been taken elsewhere in the org or are being contemplated.

The point is to start taking some small steps. So, I’m curious, what small steps have you tried, or what ones might you think of and suggest?

Making ‘sense’

24 February 2015 by Clark 1 Comment

I recently wrote about wearables, where I focused on form factor and information channels. An article I recently read talked about a guy who builds spy gear, and near the end he talked about some things that started me thinking about an extension of that for all mobile, not just wearables. The topic is sensors.

In the article, he talks about how, in the future, glasses could detect whether you’ve been around bomb-making materials:

“You can literally see residue on someone if your glasses emit a dozen different wavelengths of microlasers that illuminate clothing in real time and give off a signature of what was absorbed or reflected.”

That’s pretty amazing, chemical spectrometry on the fly. He goes on to talk about distance vision:

“Imagine you have a pair of glasses, and you can just look at a building 50 feet away, 100 feet away, and look right through the building and see someone moving around.”

Now, you might nor might not like what he’s doing with that, but imagine applying it elsewhere: identifying where people are for rescue, or identifying materials for quality control.

Heck, I’d find it interesting just to augment the camera with infrared and ultraviolet: imagine being able to use the camera on your phone or glasses to see what’s happening at night, e.g. wildlife (tracking coyotes or raccoons, and managing to avoid skunks!). Night vision, and seeing things that fluoresce under UV would both be really cool additions.

I’d be interested too in having them able to work to enlarge as well, bring small things to light like a magnifying glass or microscope.

It made me think about all the senses we could augment. I was thinking about walking our dogs, and how their olfactory life is much richer than ours. They are clearly sensing things beyond our olfactory capabilities, and it would be interesting to have some microscent detectors that could track faint traces to track animals (or know which owner is not adequately controlling a dog, ahem). They could potentially serve as smoke or carbon monoxide detectors also.

Similarly, auditory enhancement: could we hear things fainter than our ears detect, or have them serve as a stethoscope? Could we detect far off cries for help that our ears can’t? Of course, that could be misused, too, to eavesdrop on conversations. Interesting ethical issues come in.

And we’ve already heard about the potential to measure one’s movement, blood pressure, pulse, temperature, and maybe even blood sugar, to track one’s health. The fit bands are getting smarter and more capable.

There is the possibility for other things we personally can’t directly track: measuring ambient temperatures quantitatively, and air pressure are both already possible and in some devices. The thermometer could be a health and weather guide, and a barometer/altimeter would be valuable for hiking in addition to weather.

The combination of reporting these could be valuable too. Sensor nets, where the data from many micro sensors are aggregated have interesting possibilities. Either with known combinations, such as aggregating temperature and air pressure help with weather, or machine learning where for example we include sensitive motion detectors, and might be able to learn to predict earthquakes like supposedly animals can. Sounds too could be used to triangulate on cries for help, and material detectors could help locate sources of pollution.

We’ve done amazing things with technology, and sensors are both shrinking and getting more powerful. Imagine having sensors scattered about your body in various wearables and integrating that data in known ways, and agreeing for anonymous aggregation for data mining. Yes, there are concerns, but benefits too.

We can put these together in interesting ways, notifications of things we should pay attention to, or just curiosity to observe things our natural senses can’t detect. We can open up the world in powerful ways to support being more informed and more productive. It’s up to us to harness it in worthwhile ways.

Rethinking Redux

11 February 2015 by Clark 1 Comment

Last week I wrote about Rethinking, how we might want and need to revise our approaches, and showed a few examples of folks thinking out of the box and upending our cherished viewpoints. I discovered another one (much closer to ‘home’) and tweeted it out, only to get a pointer to another. I think it’s worth looking at these two examples that help make the point that maybe it’s time for a rethink of some of our cherished beliefs and practices.

The first was a pointer from a conversation I had with the proprietor of an organization with a new mobile-based coaching engine. Among the things touted was that much of our thinking about feedback appears to be wrong. I was given a reference and found an article that indeed upends our beliefs about the benefits of feedback.

The article investigates performance reviews, and finds them lacking, citing one study that found:

“a meta-analysis of 607 studies of performance evaluations and concluded that at least 30% of the performance reviews ended up in decreased employee performance.”

30% decrease performance? And that’s not including the others that are just neutral. That’s a pretty bad outcome! Worse, the Society for Human Resource Management is cited as stating “90% of performance appraisals are painful and don‘t work“. In short, one of the most common performance instruments is flawed.

As a consequence of tweeting this out, a respondent pointed to another article that he was reminded of. This one upends the notion that we’re good at rating others’ behavior: “research has demonstrated that each of us is a disturbingly unreliable rater of other people‘s performance”. That is, 360 degree reviews, manager reviews, etc., are fundamentally based upon review by others, and they’re demonstrably bad at it. The responses given have reliable biases that makes the data invalid.

As a consequence, again, we cannot continue as we are:

“we must first stop, take stock, and admit to ourselves that the systems we currently use to reveal our people only obscure them”

This is just like learning styles: there’s no reliable data that it works, and the measurement instrument used is flawed. In short, one of the primary tools for organizational improvement is fundamentally broken. We’re using industrial age tools in an information age.

What’s a company to do? The first article quoted Josh Bersin when saying “companies need to focus very heavily on ‘collaboration, professional development, coaching and empowering people to do great things’“. This is the message of the Internet Time Alliance and an outflow of the Coherent Organization model and the L&D Revolution. There are alternatives that are more respectful of how people really think, work, and learn, and consequently more effective. Are you ready to rethink?

#itashare

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