So What Do You Want From Learning Analytics?

If you haven’t noticed lately, there is a growing area of concern surrounding the field of learning analytics (also sometimes combined with artificial intelligence). Of course, there has always been some backlash against analytics in general, but I definitely noticed at the recent Learning Analytics and Knowledge (LAK) conference that it was more than just a random concern raised here and there that you usually get at any conference. There were several voices loudly pointing out problems both online and in the back channel, as well as during in-person conversations at the conference. Many of those questioning what they saw were people with deep backgrounds in learning theory, psychology, and the history of learning research. But its not just people pointing out how these aspects are missing from so much of the Learning Analytics field – it is also people like Dr. Maha Bali questioning the logic of how the whole idea is supposed to work in blog posts like Tell Me, Learning Analytics…

I have been known to level many of the current concerns at the Learning Analytics (LA) field myself, so I probably should spell out what exactly it is that I want from this field as far as improvement goes. There are many areas to touch on, so I will cover them in no particular order. This is just what comes to mind off the top of my head (probably formed by my own particular bias, of course):

  • Mandatory training for all LA researchers in the history of educational research, learning theory, educational psychology, learning science, and curriculum & instruction. Most of the concerns I heard voiced at any LAK I have attended was that these areas are sorely missing in several papers and posters. Some papers were even noticed as “discovering” basic educational ideas, like students that spend more time in a class perform better. We have known this from research for decades, so… why was this researched in the first place? And why was none of this earlier research cited? But you see this more than you should in papers and posters in the LA field – little to no theoretical backing, very little practical applications, no connection to psychology, and so on. This is a huge concern, because the LAK Conference Proceedings is in the Top 10 Educational Technology journals as ranked by Google. But so many of the articles published there would not even go beyond peer review in many of the other journals in the Top 10 because of their lack of connection to theory, history, and practice. This is not to say these papers are lacking rigor for what they include – it is just that most journals in Ed-Tech require deep connections to past research and existing theory to even be considered. Other fields do not require that, so it is important to note this. Also, as many have pointed out, this is probably because of the Computer Science connection in LA. But we can’t forego a core part of what makes human education, well… human… just because people came from a background where those aspects aren’t as important. They are important to what makes education work, so just like a computer engineer that wants to get into psychology would have to learn the core facets of psychology to publish in that area, we should require LA researchers to study the core educational topics that the rest of us had to study as well. This is, of course, something that could be required to change many areas in Education itself as well – just having an education background doesn’t mean one knows a whole lot about theory and/or educational research. But I have discussed that aspect of the Educational world in many places in the past, so now I am just focusing on the LA field.
  • Mandatory training for all LA researchers in structural inequalities and the role of tech and algorithms in creating and enforcing those inequalities. We have heard the stories about facial recognition software not recognizing black faces. We know that algorithms often contain the biases of their creators. We know that even the prefect algorithms have to ingest imperfect data that will contain the biases of those that generated it. But its time to stop treating equality problems as an after thought, to be fixed only when they get public attention. LA researchers need to be trained in recognizing bias by the people that have been working to fight the biases themselves. Having a white male instructor mention the possibility of bias here and there in LA courses is not enough.
  • Require all LA research projects to include instructional designers, learning theorists, educational psychologists, actual instructors, real students, people trained in dealing with structural inequalities, etc as part of the research team from the very beginning. Getting trained in all of the fields I mentioned above does not make one an expert. I have had several courses on educational psychology as part of my instructional design training, but that does not make me an expert in educational psychology. We need a working knowledge of other fields to inform our work, but we also need to collaborate with experts as well. People with experience in these fields should be a required part of all LA projects. These don’t all have to separate people, though. A person that teaches instructional design would possibly have experience in several areas (practical instruction, learning theory, structural inequality, etc). But you know who’s voice is incredibly rare in the LA research? Students. Their data traces DO NOT count as their voice. Don’t make me come to a conference with a marker and strike that off your poster for you.
  • Be honest about the limitations and bias of LA. I read all kinds of ideas for what data we need in analytics – from the idea that we need more data to capture complex ways learning manifests itself after a course ends, to the idea that analytics can make sense of the word around us. The only way to get more (or better) data is to increase surveillance in some way or form. The only way to make more sense is to get more data, which means… more surveillance. We should be careful not to turn our entire lives into one mass of endless data points. Because even if we did, we wouldn’t be capturing enough to really make sense of the world. For example, we know that click stream data is a very limited way to determine activity in a course. A click in an online course could mean hundreds of different things. We can’t say that this data tells us what learners are doing or watching or learning – only just what they are clicking on. Every data point is just that – a click or contact or location or activity with very little context and very little real meaning by itself. Each data point is limited, and each data point has some type of bias attached to it. Getting more data points will not overcome limitations or bias – it will collect and amplify them. So be realistic and honest with those limitations, and expose the bias that exists.
  • Commit to creating realistic practical applications for instructors and students. So many LA projects are really just ways to create better reports for upper level admin. Either that, or ways to try and decrease drop-outs (or increase persistence across courses as the new terminology goes). The admin needs their reports and charts, so you can keep doing that. But educators need more than drop-out/persistence stuff. Look, we already have a decent to good idea what causes those issues and what we can do to improve them. Those solutions take money, and throwing more data at them is not going to decrease the need for funding once a more data-driven problem (which usually look just like the old problems) is identified. Please: don’t make “data-driven” become a synonymy for “ignore past research and re-invent the wheel” in educators eyes. Look for practical ways to address practical issues (within the limitations of data and under the guiding principle of privacy). Talk to students, teachers, learning theorists, psychologists, etc while you are just starting to dig into the data. See what they say would be a good, practical way to do something with the data. Listen to their concerns. Stop pushing for more data when they say stop pushing.
  • Make protecting privacy your guiding principle. Period. So much could be said here. Explain clearly what you are doing with the data. Opt-in instead of opt-out. Stop looking for ways to squeeze every bit of data out of every thing humans do and say (its getting kind of gross). Remember that while the data is incomplete and biased, it is still a part of someone else’s self-identity. Treat it that way. If the data you want to collect was actual physical parts of a person in real life – would you walk around grabbing it off of them the way you are collecting data digitally now? Treat it that way, then. Or think of it this way: if data was the hair on our heads, are you trying to rip or cut it off of peoples’ heads without permission? Are you getting permission to collect the parts that fall to the floor during a haircut, or are you sneaking in to hair cutting places to try and steal the stuff on the floor when no one is looking? Or even worse – are you digging through the trash behind the hair salon to find your hair clippings? Also – even when you have permission – are you assuming that just because the person who got the hair cut is gone, that this means the identity of each hair clipping is protected… or do you realize that there are machines that can identify DNA from those hair clippings still?
  • Openness. All of what I have covered here will require openness – with the people you collect data from, with the people you report the analytical results to, with the general public about the goals and results, etc. If you can’t easily explain the way the algorithms are working because they are so complex, then don’t just leave it there, Spend the time to make the algorithms make sense, or change the algorithm.

There are probably more that I am missing, or ways that I failed to explain the ones I covered correctly. If you are reading this and can think of additions or corrections, please let me know in the comments. Note: the first bullet point was updated due to misunderstandings about the educational journal publishing system. Also see the comments below for good feedback from Dr. Bali.

Ed-Tech Retro-Futurism and Learning Engineering

I don’t know what I am allowed to say about this yet, but recently I was recorded on an awesome podcast by someone that I a big fan of their work. One of the questions he asked was what I meant on my website when I say “Ed-Tech Retro-Futurist.” It is basically a term I made up a few years ago (and then never checked to see if someone else already said it) in response to the work of people like Harriet Watkins and Audrey Watters that try to point out how too many people are ignoring the decades of work and research in the educational world. My thought was that I should just skip Ed-Tech Futurism and go straight to Retro-Futurism, pointing out all of the ideas and research from the past that everyone is ignoring in the rush to look current and cool in education.

(which is actually more of what real futurists do, but that is another long post…)

One of the “new” terms (or older terms getting new attention) that I struggle with is “learning engineering.” On one hand, when people want to carve out an expert niche inside of instructional design for a needed subset of specific skills, I am all for that. This is what many in the field of learning engineering are doing (even though having two words ending in “-ing” just sounds off :) ). But if you go back several decades to the coining of the term, this was the original goal: to label something that was a specific subset of the Ed-Tech world in a way that can help easily identify the work in that area. Instructional Technology, Learning Experience Design and other terms like that also fall under that category.

(And for those that just don’t like the idea of the term “engineering” attached to the word “learning” – I get it. I just don’t think that is a battle we can win.)

However, there seems to be a very prominent strain of learning engineer that are trying to make the case for “learning engineering” replacing “instructional design” / “learning experience design” / etc or becoming the next evolution of those existing fields. This is where I have a problem – why put a label that already had a specific meaning on to something else that also already had a specific meaning, just in the pursuit of creating something new? You end up with charts like this:

Which are great – but there have also been hundreds of blog posts, articles, and other writings over several decades with charts almost exactly like this that have attributed these same keywords and competencies to instructional design and instructional technologist and other terms like that. I have a really dated Master’s Degree portfolio online that covers most of these except for Data Scientist. Data Science was a few years from really catching on in education, but when it did – I went and got a lot of training in it as an instructional designer.

There are also quotes like this that are also frequently used for instructional designers as well:

And also tongue-in-cheek lists exactly like this for IDs:

(except for #4 – no instructional designer would say that even jokingly because we know what the data can and can’t do, and therefore how impossible that would be :) )

One of the signs that your field/area might be rushing too fast to make something happen is when people fail to think critically about what they share before they share it. An example of this would be something like this:

Did the person that created this think about the significance of comparing a fully-skilled Learning Engineer to “white” and a totally unskilled Learning Engineer to “black”? We really need a Clippy for PowerPoint slides that asks “You put the words ‘Black’ and ‘White’ on a slide. Have you checked to make sure you aren’t making any problematic comparisons from a racial standpoint?”

But there are those that are asking harder questions as well, so I don’t want to misrepresent the conversation:

There are also learning engineers that get the instructional design connection as well (see the Ellen Wagner quote on the right):

Although as an instructional designer, I would point out we aren’t just enacting these – we were trained and given degrees in these areas. The systems we work for currently might not formerly recognize this, but we do in our field and degree programs. Of course, instructional designers also have to add classroom management skills, training others how to design, convincing reluctant faculty, mindfulness, educational psychology, critical pedagogy, social justice, felt needs, effects of sociocultural issues such as food insecurity, and many other fields not listed in the blue above to all of those listed as well. Some might say “but those are part of human development theory and theories of human development and systems thinking.” Not really. They overlap, but they are also separate areas that also have to be taken into account.

(Of course, there is also the even larger field of Learning Science that encompasses all of this and more. You could also write a post like this about how instructional designers mistakenly think they are the same as learning scientists as well. Or how Learning Science tried to claim it started in 1990s when it really has a longer history. And so on.)

I guess the main problem I have is that instructional design came along first, and went into all of these areas first, and still few seem to recognize this. To imply that instructional design is a field that may also enact what learning engineers already have could possibly be taken as reversing what actually happened historically. I am still not clear if some learning engineers are claiming to have proceeded ID, to be currently superseding ID, or to have been the first to do what they do in the Ed-Tech world before ID. If any of those three, then there are problems – and thus the need for Ed-Tech Retro-Futurism.

Learning -Agogies Updated

A few years ago, I created a list of learning -agogies as a reference for myself and anyone else interested. I didn’t have time to finish it and left some of the non-epistemological -agogies defined. So I decided to make a more completed and updated list, but housed on a page that I can update as needed in the future. Making a blog post every time someone proposes a new -agogy would just end up being confusing. So if you want to make any additions to this page, let me know:

Learning -agogies

As you can see, I added learnagogy, dronagogy (which I still say should be dronology), and several of the other words I mentioned but didn’t define in the original post.

Why Trust Google’s Algorithms When You Can Teach?

You have heard it said “If you can Google it, why teach it?”, but I want to ask “why trust Google’s algorithms when you can teach?” I Google things all the time, so I am not saying to stop using Google (or your preferred search engine). But is it really safe to let our learners of any age just Google it and let that be it? I want to push back against that idea with some issues to consider.

When we say “Google it,” we need to be clear that we are not really searching a database and getting back unfiltered results from complete data curated by experts (like you would get in, say, a University library), but allowing specific Google algorithms to filter all the web content it can find everywhere for us and present us with content based on their standards. There is often little to anything guaranteeing those results are giving us accurate information, or even trying to, say, correct a typo we don’t notice that gets us the wrong information (like adding the word “not” when you don’t realize it). But how often do people think through the real differences between Google and a library when they refer to Google as the modern day global library?

We have all heard the news stories that found everything from promotion of neo-Nazi ideals to climate change denial within Google search and auto-correct results. Things like that are huge problems within themselves, but the issues I am getting at here are how Google search results are designed to drive clicks by giving people more of what they want to hear, regardless of whether it is factual or not. Even worse, most internet search engines are searching through incomplete data that is already biased and flawed, adding to existing inequalities when it uses that data to produce search results. People with more money and power can add more content from their viewpoint to the data pool, and then pay to multiply and promote their content with search engines while diminishing other viewpoints. Incomplete, biased, flawed… all are terms that really don’t do the problem they describe justice here.

When you are an educator of learners at any level – why leave them to navigate through a massive echo-chamber of biased and incomplete search results for any information about your field? Why not work with them to think through the information they find? And when they do need to memorize things (because not every job will let you Google the basics on the spot), why not look into research on how memorization before application helps things like critical thinking and application? To be honest, as many, many others have pointed out, Google has only increased the need to teach rather than “just Google it.” But can we change the societal narrative on this on before it is too late?

Updating Types of Interactions in Online Education to Reflect AI and Systemic Influence

One of the foundation concepts in instructional design and other parts of the field of education are the types of interaction that occur in the educational process online. In 1989, Michael G. Moore first categorized three types of interaction in education: student-teacher, student-student, and student-content. Then, in 1994, Hillman, Willis, and Gunawardena expanded on this model, adding student-interface interactions. Four years later, Anderson & Garrison (1998) added three more interaction types to account for advances in technology: teacher-teacher, teacher-content, and content-content. Since social constructivist theory did not quite fit into these seven types of interaction, Dron decided to propose four more types of interaction in 2007: group-content, group-group, learner-group, and teacher-group. Some would argue that “student-student” and “student-content” still cover these newer additions, and to some degree that is true. But it also helps to look at the differences between these various terms as technology has advanced and changed interactions online – so I think the new terms are helpful. More recently, proponents of connectivism have proposed acknowledging patterns of “interactions with and learning from sets of people or objects [which] form yet another mode of interaction” (Wang, Chen, & Anderson, 2014, p. 125). I would call that networked with sets of people and/or objects.

The instructional designer within me likes to replace “student” with “learner” and “content” with “design” to more accurately describe the complexity of learners that are not students and learning designs that are not content. However, as we rely more and more on machine learning and algorithms, especially at the systemic level, we are creating new things that learners will increasingly be interacting with for the foreseeable future. I am wondering if it is time to expand this list of interactions to reflect that? Or is it long enough as it is?

So the existing ones I would keep, with “learner” exchanged for “student” and “design” exchanged for “content”:

  • learner-teacher (ex: instructivist lecture, learner teaching the teacher, or learner networking with teacher)
  • learner-learner (ex: learner mentorship, one-on-one study groups, or learner teaching another learner)
  • learner-design (ex: reading a textbook, watching a video, listening to audio, completing a project, or reading a website)
  • learner-interface (ex: web-browsing, connectivist online interactions, gaming, or computerized learning tools)
  • teacher-teacher (ex: collaborative teaching, cross-course alignment, or professional development)
  • teacher-design (ex: teacher-authored textbooks or websites, teacher blogs, or professional study)
  • group-design (ex: constructivist group work, connectivist resource sharing, or group readings)
  • group-group (ex: debate teams, group presentations, or academic group competitions)
  • learner-group (ex: individual work presented to group for debate, learner as the teacher exercises)
  • teacher-group (ex: teacher contribution to group work, group presentation to teacher)
  • networked with sets of people or objects (ex: Connectivism, Wikipedia, crowdsourced learning, or online collaborative note-taking)

The new ones I would consider adding include:

  • algorithm-learner (ex: learner data being sent to algorithms; algorithms sending communication back to learners as emails, chatbot messages, etc)
  • algorithm-teacher (ex: algorithms communicating aggregate or individual learner data on retention, plagiarism, etc)
  • algorithm-design (ex: algorithms that determine new or remedial content; machine learning/artificial intelligence)
  • algorithm-interface (ex: algorithms that reformat interfaces based on input from learners, responses sent to chatbots, etc)
  • algorithm-group (ex: algorithms that determine how learners are grouped in courses, programs, etc)
  • algorithm-system (ex: algorithms that report aggregate or individual learner data to upper level admin)
  • system-learner (ex: system-wide initiatives that attempt to “solve” retention, plagiarism, etc)
  • system-teacher (ex: cross-curricular implementation, standardized teaching approaches)
  • system-design (ex: degree programs, required standardized testing, and other systemic requirements)

Well… that gets too long. But I suspect that a lot of the new additions listed would fall under the job category of what many call “learning engineering” maybe? You might have noticed that it appears as if I removed “content-content” – but that was renamed “algorithm-design,” as that is mainly what I think of for “content-content.” But I could be wrong. I also left out “algorithm-algorithm,” as algorithms already interface with themselves and other algorithms by design. That is implied in “algorithm-design,” kind of in the same way I didn’t include learners interacting with themselves in self-reflection as that is implied in “learner-learner.” But I could be swayed by arguments for including those as well. I am also not sure how much “system-interface” interaction we have, as most systems interact with interfaces through other actors like learners, teachers, groups, etc. So I left that off. I also couldn’t think of anything for “system-group” that was different from anything else already listed as examples elsewhere. And I am not sure we have much real “system-system” interaction outside of a few random conversations at upper administrative levels that rarely trickle down into education without being vastly filtered through systemic norms first. Does it count as “system-system” interaction in a way that affects learning if the receiving system is going to mix it with their existing standards before approving and disseminating it first? I’m not sure.

While many people may not even see the need for the new ones covered here, please understand that these interactions are heavily utilized in surveillance-focused Ed-Tech. Of course, all education utilizes some form of surveillance, but to those Ed-Tech sectors that make it their business to promote and sell surveillance as a feature, these are interactions that we need to be aware of. I would even contend that these types of interaction are more important behind the scenes of all kinds of tech than many of us realize. So even if you disagree with this list, please understand that these interactions are a reality.

So – that is 20 types of interaction, with some more that maybe should have been included or not depending on your viewpoint (and I am still not sure we have advanced enough with “algorithm-interface” yet to give it it’s own category, but I think we will pretty soon). Someone may have done this already and I just couldn’t find it in a search – so I apologize if I missed others’ work. None of this is to say that any of these types of interactions are automatically good for learners just because I list them here – they just are the ones that are happening more and more as we automate more and more and/or take a systems approach to education. In fact, these new levels could be helpful in informing critical dialogue about our growing reliance on automation and surveillance in education as well.

What Does It Take to Make an -agogy? Dronagogy, Botagogy, and Education in a Future Where Humans Are Not the Only Form of “Intelligence”

Several years ago I wrote a post that looked at every form of learning “-agogy” I could find. Every once in a while I think that I probably need to do a search to see if others have been added so I can do an updated post. I did find a new one today, but I will get to that in a second.

The basic concept of educational -agogy is that, because “agogy” means “lead” (often seen in the sense of education, but not always), you combine who is being led or the context for the leading with the suffix. Ped comes from the Greek word for “children,” andr from “men,” huet from “self,” and so on. It doesn’t always have to be Greek (peeragogy, for example) – but the focus is on who is being taught and not what topic or tool they are being taught.

I noticed a recent paper that looks to make dronagogy a term: A Framework of Drone-based Learning (Dronagogy) for Higher Education in the Fourth Industrial Revolution. The article most often mentions pedagogy as a component of dronagogy, so I am not completely sure of the structure they envision. But it does seem clear that drones are the topic and/or tool, and only in certain subjects. Therefore, dronology would have probably been a more appropriate term. They are essentially talking about the assembly and programming of drones, not teaching the actual drones.

But someday, something like dronagogy may actually be a thing (and “someday” as in pretty soon someday, not “a hundred years from now” someday). If someone hasn’t already, soon someone will argue that Artificial Intelligence has transcended “mere” programming and needs to be “led” or “taught” more than “programmed.” At what point will we see the rise of “botagogy” (you heard it here first!)? Or maybe “technitagogy” (from the Greek word for “artificial” – technitós)?

Currently, you only hear a few people like George Siemens talking about how humans are no longer the only form of “intelligence” on this planet. While there is some resistance to that idea (because AI is not as “intelligent” as many think it is), it probably won’t be much longer before there is wider acceptance that we actually are living in a future where humans are not the only form of “intelligence” around. Will we expand our view of leading/teaching to include forms of intelligence that may not be like humans… but that can learn in various ways?

Hard to say, but we will probably be finding out sooner than a lot of us think we will. So maybe I shouldn’t be so quick to question dronagogy? Will drone technology evolve into a form of intelligence someday? To be honest, that just sounds like a Black Mirror episode that we may not want to get into.

(Feature image by Franck V. on Unsplash)

“Creating Online Learning Experiences” Book is Now Available as an OER

Well, big news in the EduGeek Journal world. I have been heading up a team of people to work on new book that was released as an OER through PressBooks today:

Creating Online Learning Experiences: A Brief Guide to Online Courses, from Small and Private to Massive and Open

Book Description: The goal of this book is to provide an updated look at many of the issues that comprise the online learning experience creation process. As online learning evolves, the lines and distinctions between the various classifications of courses has blurred and often vanished. Classic elements of instructional design remain relevant at the same time that newer concepts of learning experience are growing in importance. However, problematic issues new and old still have to be addressed. This book aims to be a handbook that explores as many of these issues and concepts as possible for new and experienced designers alike, whether creating traditional online courses, open learning experiences, or anything in between.

We have been working on this book on and off for three or more years now, so I am glad to finally get it out to the world. In addition to me, there were several great contributing writers: Brett Benham, Justin Dellinger, Amber Patterson, Peggy Semingson, Catherine Spann, Brittany Usman, and Harriet Watkins.

Also, on top of that, we recruited a great group of reviewers that dug through various parts and gave all kinds of helpful suggestions and edits: Maha Al-Freih, Maha Bali, Autumm Caines, Justin Dellinger, Chris Gilliard, Rebecca Heiser, Rebecca Hogue, Whitney Kilgore, Michelle Reed, Katerina Riviou, Sarah Saraj, George Siemens, Brittany Usman, and Harriet Watkins.

Still skeptical? How about an outline of topics, most of which we did try to filter through a critical lens to some degree:

  1. Overview of Online Courses
  2. Basic Philosophies
  3. Institutional Courses
  4. Production Timelines and Processes
  5. Effective Practices
  6. Creating Effective Course Activities
  7. Creating Effective Course Content
  8. Open Educational Resources
  9. Assessment and Grading Issues
  10. Creating Quality Videos
  11. Utilizing Social Learning in Online Courses
  12. Mindfulness in Online Courses
  13. Advanced Course Design
  14. Marketing of an Online Course

So, please download and read the book here if you like: Creating Online Learning Experiences

There is also a blog post from UTA libraries about the release: Libraries Launch Authoring Platform, Publish First OER

And if you don’t like something you read, or find something that is wrong, or think of something that should have been added – let me know! I would love to see an expanded second edition with more reviewers and contributing authors. There were so many more people I wanted to ask to contribute, but I just ran out of time. I intentionally avoided the “one author/one chapter” structure so that you can add as much or as little as you like.

Can the Student Innovate? An #OLCInnovate Reflection

The 2nd OLC Innovate conference is now over. I am sure there will be many reflections out there on various aspects of the conference. I hope to get to reflect on my presentation on learning pathways and some of the ideas that attendees shared. But I wanted to first dig into one of the more problematic aspects of the conferee: the place and role of students.

The biggest problem related to students at the conference was how they were framed as cheaters at every turn. Chris Gilliard wrote a blog post that explores this aspect in depth. I was able to finally meet and hang out with Chris and many others at Innovate. Those of us that got to hang out with Chris got to hear him pondering these issues, and his blog post makes a great summary of those ponderings.

The other student issue I wanted to reflect was also part of what Chris pondered at the conference as well:

Of course, as soon as I tweeted that, we found there were a few sessions that had students there. But for the most part, the student voice was missing at OLC Innovate (like most conferences).

At some levels, I know how difficult it is to get students at conferences. Even giving them a discounted or free registration doesn’t help them with expensive hotel or travel costs. Sponsoring those costs doesn’t help them get a week off from class or work or both to attend. Its a daunting thing to coordinate. But considering the thousands of attendees at OLC Innovate representing tens or hundreds of thousands of learners out there, surely some effort to find the money would have brought in a good number if the effort had been there.

But beyond that, it seemed that in many places the whole idea of students even being able to “innovate” was left out of some definitions of innovation. Not all, of course. Rolin Moe brought his Innovation Installation back to OLC Innovate, which served as a welcome space to explore and ponder the difficulties in defining “innovation” (those pesky-post modernists always wanting us to “deconstruct” everything….) Rolin did an excellent job of looking at situating the definition of innovation as an open dialogue – a model I wish more would follow:

The definitions of innovation became problematic in the sessions and keynotes. The one that really became the most problematic was this quote from one keynote:

(I am also not a fan of the term “wicked problems”)

The context for this definition was the idea that innovation is a capability that is developed, and really only happens after a certain level of ability is obtained (illustrated by a pianist that has to develop complex technical skill before they can make meaningful innovative music). The idea that some creativity/innovation isn’t “good” was highlighted throughout the same keynote:

For context, here is the list of “Innovation Capabilities” that were shared:

There was also various other forms of context, all of which I thought were good angles to look at, but still very top-down:

This was capped off by the idea that there are “good kinds” of innovation and “bad kinds” of innovation, and we should avoid the bad innovations:

Of course, the master of all meme media Tom Evans made a tool to help us make these decisions:

What one person sees as a “bad” innovation might be a “good” innovation to another. Not sure how to make the determination in such an absolute sense.

There was also an interesting terms of “innovation activist” that was thrown in there that many questioned:

I get that many want a concrete definition of innovation. But I think there are nuances that get left out when we push too strongly in any one direction for our definitions. For example, I agree that innovation is a capability that can be trained and expanded in individuals. But it is also something that just happens when a new voice looks at a problem and comes up with a random “out of the blue” idea. My 6 year old can look at some situation for the first time and blurt out innovative ideas that I had never heard of. Of course, he will also blurt out many ideas that are innovative to him, but that I am already aware of. And there lies the difficulty of defining “innovation”….

Whatever innovation is, there is a relative element to it where certain ideas are innovative to some but not to others. Then there is the relative element that recognizes that innovation is a capability that can be cultivated, but cultivation of that capability is not necessarily a prerequisite to doing something “innovative.”

In other words, any definition of innovation needs to include the space for students to participate, even if they are new to the field that is “being innovated.” The list of Educational Capabilities pictured above is very instructor/administrator/leader centric. Some of those items could be student-centered, but the vocabulary on the slide seems to indicate otherwise. But ultimately I guess it goes back to whether one sees innovation as absolute or relative to begin with. If Innovation (with a capital “I”) is absolute, then there are definitely some things that are innovative at all times in all contexts and some things that aren’t, and therefore Innovation is a capability that has to be developed and studied in order to be understood before participating. But if innovation (with a lower case “i”) is relative, then anyone that is willing to can participate. Including students. But you rarely (at any conference) see the student voice represented in the vendor hall. And as with any conference, how goes the vendor hall, so goes the conference….

Is Innovation Contextual or Absolute?

When discussing the concept of truth, many people will make the distinction between “truth” (lower case t) and “Truth” (upper case T), where “Truth” refers to ultimate truth that is true for all, and “truth” referring more to contextual truth that may be true for some but not others. Or, to simplify, absolute Truth and relative truth.

In many ways, I see the same need to differentiate between “Innovation” and “innovation” when discussing the overall concept of innovation. Of course, I’m not sure if I really want to make such a problematic connection between innovation and truth. But I think there is something to determining whether someone is referring to absolute innovation or relative innovation. There are ideas and tools that are new to everyone and therefore count as absolute innovation, and then there are ideas and tools that are not new to everyone, but are new to those that are just discovering them.

For example, online learning is a concept that has been around for decades. It is not absolutely Innovative in a general sense. But to schools that have no online courses, their first online courses will be innovative in their context. Or to a person that has avoided going online in general (or didn’t have access to the internet), the ability to take online courses will also be innovative to them.

Of course, even the idea of “absolute innovation” is problematic. Virtual Reality seems like a new, innovative idea to most…. but the truth is, the concept of virtual reality has been around for some time. Maybe you can more accurately say that the idea of a more widely-available digitally-created simulation-based computer-run semi-immersive interactive virtual reality is innovative in general to anyone. A lot of dashes there.

And I have also intentionally not spelled out how I am defining innovation beyond “something new” for this article. Another problematic area.

So why does all this matter? It probably doesn’t for most. I first ran into this issue 6-7 years ago as a chair for a proposal review committee for an “emerging technologies” track at a conference. The track description relied heavily on the term “innovation” to delineate between “emerging technology” and “latest and greatest technology” (because that was another track). We had submissions that ranged from using the (just recently-released at the time) Google Wave in classrooms to teaching with PowerPoint. Where does one draw the line between “current” and “emerging” based on the criteria of “innovation”?

Well, long story short… you don’t if you want to keep everyone happy :) You let people self-define whether they are innovative or not in their context and then let them take the heat if the session attendees don’t agree that their idea was innovative in general.

So it might surprise people that as an “Innovation Coordinator,” I don’t just look at things like virtual reality and learning analytics. I also look at many established instructional design and digital presence ideas. I also look at low tech ideas on how to be a human in a digital age. Even more shocking to some is how I talk about how throwing a handful of dirt at a poster board on the ground to demonstrate the “Big Bang” to 8th grade students as being one of the more innovative ideas I utilized back when I was an 8th Grade Science teacher. Sure, I also created my own online course hub that I hand-coded in html in the summer of 2000 long before most were putting K-12 material online. But I also had to find a way to help 8th graders visualize the Big Bang on a $200 a year total budget (classroom material, science equipment, everything – $200). So what did I do? I put a white poster-board on the ground, grabbed a handful of dirt, pebbles, and grass in my hand, and did a 2 minute demo on what the Big Bang would look like. It was effective. It was cheap. It was innovative in that context.

I definitely wish there was more focus on looking at innovation beyond the coolest, newest, most expensive gadgets, apps, programs, ideas, etc. How do we innovate when cost is a barrier? When technology access is non-existent? When we need to transfer online lessons to face-to-face classes? We have all kinds of media outlets that look at Innovation the moment “it” happens – any new device, tool, idea, app. But what does innovation look like in a contextual situation, where budgets are small, resources are constrained, and technology access is limited? And not just current situations, but situations that have historically lacked in these areas? How do we innovate access to technology itself? How do we innovate the cost of technology? There is a much wider and more nuanced conversation about innovation to be had.

Being a Human Shopper in a Digital Online Shopping Age

So with a new year, our research lab is going to focus on writing and setting goals for the upcoming year. Our main question at LINK Lab is “What does it mean to be human in a digital age?” I thought this would be a great place to start with processing what my goals should be, so I began my quest to write goals for the year with that question in mind. Then some national news this week helped bring some clarity to how my personal goals would relate to our main question.

This week was full of news that Sears, Macy’s, JC Penny’s and other big name stores are laying off workers and closing stores. Many people have been posting this news on various social media outlets with the general response of “I like to shop online better anyways.” Of course, I do as well. But I have noticed over the last few years that I still make a point to go buy some things in person that I could easily buy online, even while I still buy many things online.

For me, this is one way I am unconsciously pushing back against the increasing loss of control that comes along with living in a digital world. For instance, I know the exact pair of blue jeans that will always fit me from a certain store no matter what. I could easily buy those jeans online, and know that they will be the right pair for me. But I still find myself wandering into the local mall to buy new jeans when I need them. Something in me is pushing back against the digital age to still connect with being a human. Shopping in person is a very human experience. You get to touch and observe the exact product you will buy before buying it.

When you buy something online, you lose control over what you get. It will probably end up being the right thing, but you still lose that control until it arrives at your door. For me, to still be a human shopper in a digital online shopping age means to take control over some things and go do what a human would do. This may be shopping in brick and mortar stores in person, or driving myself somewhere when I could have gotten an Uber, or drawing a picture on a piece of paper instead of blogging about an idea(I have a really interesting idea for a drawing to do about my pathways work – hope I get time to draw that out soon). Its not that online shopping or Uber or blogging are bad – I just need to do things for me that remind me of what it means to be human. That might be different for different people.

To bring this back to work, for me, the aspect of “what does it mean to be human in a digital age” that interests me the most is the tension between control and agency.

In a learning context for projects to be researched, that interest would manifest itself in a question something along the lines of “What happens when learners have more agency over their learning journey?”

edugeek-journal-avatarThis question is obviously a work in process that will probably be refined over the next few weeks. I hope to get some decent goals out of this overarching question that would apply to pathways, virtual reality, publications, etc. But it is a starting point for me at least.