EMERGING TECHNOLOGIES: The normal distribution, with +/- three standard deviations or SD units, shows the approximate percentages that represent the diffusion of innovation. My experimental work with technologies has been in the left sixth of this curve. By today, examples of an emerging technologies would be atomic storage (now in research and development) and wearable devices (mostly innovators and early adopters). Other well-known technologies have already emerged. Today, MOOCs have already emerged (used by an early majority) and online learning is well established (now being discovered by the late majority).
EDUCATIONAL TECHNOLOGY PREDICTIONS: Since 2006, I have been making annual predictions for how emerging technologies will make a difference in Education. These are the latest (2016) taken from this slideshow.
TECHNOLOGIES AND THE HYPE BUBBLE: Partly driven by marketing exaggerations, educators often have unrealistic expectations for emerging technologies and some see every new flavor as a panacea for what ails learning. Initiailly enthusiastic about new technologies, they are soon disappointed by poor product performance. When driven in this manner, a hype bubble develops on the upslope of the normal growth curve (measuring acceptance over time). This bubble shows accelerated growth due to unbridled enthusiasm, followed by a drastic drop due to disappointment leading to disillusionment with that technology. Several educational technologies are positioned on these curves with estimated times to reach widespread and mainstream public acceptance. The hype bubble and curve are based on Gartner's Hype Cycle for media and technologies.
HISTORICAL EVOLUTION OF WEB 3.0: Toward the end of 2014, I was asked to share my thoughts on how Web 2.0 would evolve into 3.0 and what the future of the Internet would look like. Using a version of the illustration below, I chose to share a positive perspective of how this tool would help solve our global crises. This diagram traces: the history of popular technologies, the Internet's evolution (from initial military weapon through online school to eventual virtual makerspace), with corresponding iterative versions of the web.
Amazon and Netflix are good examples of commercial operations that have spanned all three iterations. In Web 1.0, their webpages were simply lists of the products and services they offered. In Web 2.0, they became a social community by asking members to review and recommend products and services. In Web 3.0, using personalized and customized algorithms, they began recommending products and services based on each user's behavior as driven by semantic data from the advice and behaviors of previous members (others also bought this book and we think you'll enjoy this movie). A natural progression in the third version will be to encourage users to co-create new products and services based on the recommendations and gaps identified by prior members.
Web 3.0 is "semantic" or data-driven where the relationships between two datum also become data. This will enable us to search semantically, which means that the language we search from becomes a modifier of our future searches. We see this to a slight degree currently, but in the future the influence will be considerably greater. Not only will the search process customize to the user (if you often search for medical terms, your future search results will have medical orientations, until you search otherwise), but the organization of web information will also adjust to the user's preferences (medical data will be more closely connected within itself and to you as an individual user). Beyond this, the search terms will not need to be precise keywords, since metaphor and synonyms will automatically be considered in the search process. Our language can be looser and more human.
Artificial intelligence will figure prominently in this iteration as more accurate search results will be returned because computers will begin to understand information contextually, just like humans. This means that the Internet will become your personal assistant, and get to know you better over time by incorporating your likes and dislikes into your online interactions. Language translation will become instantaneous allowing us to speak with other user's in their native tongues. Emotional interactions between humans and computers will be possible with brain implants and facial recognition used to read our emotional states.
All of this means that we will be more able to work together, than was ever possible before Web 3.0 with project management, and this will lead us to crowd-creation. Due to this expected enhancement of searching information and emotional interaction with one another and/or other devices or appliances online, collaboration will move toward co-creation. We will be able to invent new ideas and improve on those ideas to the point of solving real world problems like climate change. Co-creative innovation will not necessarily disrupt industries, but ought to result in jointly treasured solutions with value for all, freedom from conflicts of interest, and independent of market influences.
DISRUPTIVE TECHNOLOGY PREDICTIONS: By early 2016, software has had a history of disrupting the traditional industries such as travel agents (Kayak, Expedia, Travelocity, Trip Advisor, etc.), taxi services (Uber, Lyft, etc.), accommodation reservations (airbnb, hotels, etc.), accountancy (TurboTax), toll booth operations (EZPass), newspapers (online media), post offices (email), music (Napster, iTunes, etc.), retail (Amazon), library search (Google), encyclopedia sales (Wikipedia), and television (Netflix, Hulu, etc.).
New apps will continue this disruption in the next 0-5 years. Language translation (G. Translate), financial trading (eTrade), and job recruitment (Indeed, Monster, LinkedIn, etc.) will all improve to the point of disruption by 2020. This means eliminating many translator, trader, and recruiter jobs. Expect fast food to be ordered through voice recognition kiosks, retail cashiers to be replaced by automated checkouts, and banking to be transacted with a combination of biometric identification and self-serve options. If the minimum wage is raised for most entry level positions, companies will choose technology over workforce and save costs by significantly reducing their numbers of servers, cashiers, and tellers.
Telephony apps (Skype, FaceTime, FreeTone, etc.), operating under Voice over Internet Protocols (VOIP), allow phone and video calls to be made for free through the Internet rather than over telecom networks (which also use VOIP). This can eliminate the need for a mobile/cell phone plan payments, if Internet access is available, but might require adding a paid data plan, if one is away from the home Wi-Fi. Payment apps (electronic wallets), that work by “tapping “ a smart phone and replace the need for credit cards, will let one pay for that data plan and buy groceries, gas, and other products or services.
Drones, aerial vehicles without onboard operators (“unmanned”), will be used for product/food delivery (Amazon was experimenting with this, but the USA banned package delivery by drone in 2015) and various other flight applications. This means fewer pilots for filming aerial sequences, dusting crops, monitoring traffic, and enforcing laws. While military drones have long been used in war, consider the terror of a commercial drone being outfitted with deadly weapons for use against a civilian population.
“3D Printing” or “additive manufacturing” allows for the rapid creation of prototypes and finished products by combining and transforming raw materials into products, built layer by layer (as opposed to machining away stock materials) by devices that originally were patterned after inkjet printers. This process has increased in quality (100x) and decreased in cost (-99%) over the past decade; it is projected to continue the trend. The technology is a threat to manufacturing, because it replaces the need to order parts. Automobile, aircraft, and marine repair services can simply “3D-print” what they need and when they need it. This on-demand manufacturing reduces the wait time and need to store parts. This will have additional impacts on factory workers, supply chain, inventory management, and all forms of transportation (saving on carbon emissions). Shoes, bikes, cars, and even guns have been “3d printed.” Look forward to the first “3d-printed” house and skyscraper in the years to come. “4D printing,” where the fourth dimension is reshaping the object by adding a stimulus (light, heat, moisture, electricity, etc.), will similarly increase exponentially. Home printing will accelerate these manufacturing growth curves.
Driverless cars or autonomous vehicles bring multiple disruptions. Initially, commercial drivers, taxi operators, and chauffeurs will have to find other employment. A car or computer on wheels that can drive itself means you can do work while being driven to your next destination. You won’t have to own a car; you can simply text of call for one by phone or app and pay for the distance travelled. You won’t need to find parking or a gas station; you won’t need to get a driver’s license or insurance.
Imagine the reductions in noise and carbon emissions that will result. Accidents will be fewer (some estimates suggest a hundred times less likely) and that will save a lot of lives (some say over one million annually worldwide), thus contributing further to over-population problems. Consequently, insurance companies will be disrupted because they won’t have as many injury claim payouts and will have to drastically cut premiums. People will live in the city buildings that replace no longer needed parking structures or move further out to the distant suburbs for a more rural lifestyle.
All of this means disruption for the traditional automobile manufacturers. They also face further disruption from the rise of electric and hybrid vehicles. Electricity should become cleaner and cheaper with innovations in solar, wind, and hydro power. This will reduce reliance on carbon emitting fossil fuels and make electric vehicles common place. Automobile manufacturers must adapt to produce their own versions of electric, hybrid, and autonomous vehicles, or face eventual bankruptcy.
Artificial intelligence (AI), a computer that can think, learn, and solve problems for itself, is coming on strong. In 2011, IBM’s Watson (DeepQ&A) computer beat two of the all-time best Jeopardy contestants, and is now being used to predict cancer and make patient management decisions with greater efficacy than medical staff. Watson’s high tech use effectively frees up medical staff for more high touch patient contact. Could this be the disruption that healthcare has been waiting for? Imagine physicians and nurses able to concentrate on patient treatment after an AI machine has collected patient information and medical history, and then made an accurate diagnosis for them.
This year (2016), Google DeepMind’s AlphaGo beat the 18-time world champion in five games of Go (ancient game more complex than chess). Next year, their AlphaZero (learns games rapidly) is scheduled to do the same in chess matches against a champion or best computer program. AI “robo-lawyers” or “lawbots” are providing accurate legal advice (in many cases more accurate than by entry-level lawyers). Could this be the end of lawyers or a reduction in their numbers as with financial advisors and traders?
AI is a part of most powerful technologies from home assistants (Siri, Alexa, etc.), through credit card fraud analytics, to the operation of autonomous vehicles and self-navigating drones. Look for it to become the next major disruptor after software within 5-10 years. So what about education?
Education hasn’t changed much in the past millennium and is ripe for disruption. Around 800 years ago, before the printing press was invented in the 1440’s, education was solidly based in religion and consisted of monastic training schools. Monks taught classes by reading a single bible aloud, while monks-in-training copied down the words on paper and thus produced many more bibles for distribution. Clearly this was a precursor of the modern lecture, where the content of faculty lectures go directly to the student notebooks today, without passing through the minds of either!
When the industrial revolution came along in the 1800’s, education was institutionalized and redesigned around the efficiency of a factory. Students were organized into rows of desks and their time was broken into equal periods of learning. Education became an assembly line where one size was made to fit all and we haven’t seen transformation since. Sure, individual teachers bring change in their own tiny spheres of influence, but we haven’t had a revolution in almost 200 years and disruptions are rare.
In this century, e-learning was supposed to be the first technology disruption. MOOCs (Massive Open Online Courses) were touted to be the second. Both were over-hyped and neither had the impact that was predicted. Innovating in education is extremely difficult as the climate is more resistant to change than business or healthcare. Consider how slow the introduction of a Learning Management System has been in all levels of education. Most faculty members are comfortable using a typewriter, when a laptop could do so much more for them. Now, virtual reality is being heralded as the third disruptor and the robotic humanoid as a fourth. As pundits suggest, will VR and AI robots someday replace teachers?
The potential for disruption exists where ever education does a poor job of educating. For example, start-ups are partnering with universities to manage textbooks (Rafter), learning resources (Quizlet), and ePortfolios (Riipen). These are operational elements that have largely gone ignored in higher education. Other start-ups are addressing tutoring (InsideTrack), collaborative learning (Piazza), and credentialing (OpenBadges). These are all important components of the learning experience that have been all too neglected by tertiary educators in the past. If education leaves a gap, entrepreneurs are sure to fill it.
However, as long as education controls the credentials (high school diploma or university degree), they will capably insulate themselves against disruption. Once they lose that control, they will quickly be replaced by newer versions of the successful startups seen today (EdX, Udemy, Coursera, Khan Academy, etc.) and they will rapidly go out of business, unless they completely reinvent themselves.
Any reinvention of education must take into account that it is well overdue for another revolution. We must prepare students for the future, where the new digital information age is uncertain, challenging, change-oriented, driven by technology, and/or full of opportunities for those who are properly prepared. This will require that universities prepare citizens for the new digital information age as:
technologically competent, able to use emerging tools/techniques;
literate, communicative, and collaborative in all environments;
able to think critically and creatively to solve problems/decide;
adaptive and proactive by remaining open to transformation;
mobile and flexible by being able to quickly switch environments;
oriented to teamwork, but can act as individual change agents; and
self-differentiated learners, able to efficiently learn for themselves
For as John Dewey, the revolutionary experiential educator of a century ago, said “the challenge of education is to prepare students for their future, not our past…. …if we teach today as we taught yesterday, then we rob our children of tomorrow” (Dewey, 1916, p. 167, Democracy and Education, New York: Free Press).
ONLINE CROWD COLLABORATION: I’ve become a strong proponent of online crowd collaboration as a possible method to solve the really big problems of the world like climate change. We started looking at collaboration in large teams during my 1997-2005 research with virtualteamworks.com and in classroom crowds during the decade after this with MOOCs, SMOOCCs, and e-learning business degrees at my universities.
At a minimum, we found most collaborative collectives require the means to: communicate, maintain healthy relationships, organize information, manage projects with resources, generate ideas, make decisions, create products, and present results. The following list expands these nine functions with technologies that can be used to support collaboration. In this list, items 1 through 5 are omnipresent (widespread across all the other items), while 6 through 8 form a core that is iterative (repeated as many times as needed), and 9 is the final output. A crowd collaborating online would need tools to cover all of these functions:
communicating synchronously and asynchronously (using email, chat, messaging, discussion lists, screen sharing, and conferencing);
enhancing healthy relationships & building trust (by applying social networking, online team-building, facilitation and mediation);
organizing information (searching files, loading databases, tagging content, tracking versions, mapping concepts, and archiving in the cloud);
managing resources (scheduling time, spreadsheeting budgets, allocating resources, constructing makerspaces, and identifying experts);
generating ideas (imagining prototypes, using creative design apps, recording brainstorms, dictating voice, and translating languages);
deciding among options (building consensus, polling votes, aggregating responses, and supporting decisions);
creating products (writing wikis, editing documents, whiteboarding graphics, developing software, and fixing bugs); and
presenting results (authoring slideshows, using audio or video editors, and see also communication).
As we evolved online from team collaboration to crowd collaboration, we realized a profound need to provide additional makerspace access to our program and course participants. We called this unique combination (of collaborative processes with makerspace labs) our "ColLab" or "Collaboratory" after William Wulf. Scaling this evolution from crowd collaboration to global collaboration will eventually lead to ideas that fix the world’s troubles.
COLLABORATIVE MAKERSPACES: Makerspaces are physical and virtual fabrication labs where learners can experiment with a do-it-yourself approach to the improvement of existing tools and the invention of new ones. “Makers” support an open-source approach that favours cooperative sharing by making blueprints, sources codes, prototypes, and other constructed gadgets freely available to all makers within (and outside) the makerspace. Tinkering in electronics, robotics, and digital fabricating (with 3D scanning and 4D printing), makers have the potential to co-create impressive solutions, innovate awesome software, and engineer amazing hardware. We found the following resources to be valuable for a truly collaborative laboratory or ColLab:
materials (chemicals, energy sources, electronics, metallics, textiles, and decorations);
tools (extruding, shaping, cutting, joining, measuring, observing, protecting, and moving);
modelling (robotics, digital fabricators, micro-controllers, machines, and engineering toys);
audio lab (microphones, speakers, music synthesizers, and audio editor applications);
video lab (cameras, green screens, slideshow creators, and video editor applications);
data storage (cloud and fog library of source code, blueprints, and performance results); and
computing (laptops, tablets, printers, and applications for documenting, designing, or coding).
UTILITY FRAMEWORK FOR ONLINE TEACHING: This framework combines the best 26 teaching methods (sourced from the four educational staircase pedagogies and that can be delivered online) and applies their utility to meet or exceed the eight learning objectives (taken from two additions to the revised taxonomy of six cognitive learning objectives). This is complex, so we will discuss it one step at a time.
First, Bloom and colleagues (1956) came up with an original order for six cognitive learning objectives and this was revised some 45 years later by Anderson and Krathwohl (2001). I added two higher orders of change to their list to create a new taxonomy of eight cognitive learning objectives (2002) and determined that four different pedagogical approaches were necessary to reach each pair of learning objectives (2005). This formed the basis of the Educational Staircase model and its relationship table (2013). Further research into these concepts provided ten guiding principles for twelve best teaching practices.
Anderson, L. W., & Krathwohl, D. R. (Eds.). (2001). A taxonomy for learning, teaching and assessing: A revision of Bloom's Taxonomy of educational outcomes: Complete edition, New York: Longman.
Bloom, B.S., Engelhart, M. D., Furst, E. J., Hill, W. H., & Krathwohl, D.R. (1956). Taxonomy of educational objectives: the classification of educational goals; Handbook I: Cognitive Domain. New York, Longmans.
Priest, S. (2002). Thoughts on Virtual Teambuilding and Online Learning. Technology Think Tank. Tulsa, OK: Williams Corporation.
Priest, S. (2005). Faculty Training & Development Manual. Allentown, PA: The Wescoe School of Muhlenberg College.
Priest, S. (2013). The Educational Staircase: Alternative Pedagogies for E-learning. Collaboration for Online Higher Education Research (COHERE) Conference. Vancouver, BC, Canada, October 24th.
Second, of the many typical teaching techniques described in the staircase model, 26 had viable online versions. For example, this means that a technique like lecturing has a corresponding online equivalent, such as capturing a lecture and playing the recorded video back at a later time. While other techniques, like in person internships, apprenticeships or preceptorships, do not yet have direct online counterparts.
Third, the eight cognitive learning objectives can be divided into four pairs (1&2, 3&4, 5&6, and 7&8). Each of the 26 online teaching techniques or methods has utility or usefulness to attain a particular learning objective pair. For example, lecturing is highly effective at helping learners to remember (recall facts and define terms) and understand (explain ideas and clarify concepts), but is mostly ineffective at getting them to evaluate or create. Evaluation and creativity come more easily from “hands-on” and real-life experiential learning such as community service or capstone ventures. Furthermore, problem solving exercises are effective for online students to learn how to apply and analyze, but not necessarily at how to improve or invent. The latter requires a partnership between mentor and protoge (mentee learner).
Fourth, while these teaching methods are “pigeon-holed” into association with only one pedagogy or pair of learning objectives, each technique will have valuable overlap with their neighbouring regions. Teachers would be well advised to have these techniques in their tool kits and to use them sequentially as descibed here. While other methods exist, here are explanations for the 26 online teaching techniques that were chosen for this framework and are delivered by Internet and Communication Technologies (ICT).
Reading webpages = reading is an initial step where books have been replaced by e-texts and the webpage is the default for reading PDF files or HTML layout.
MOOC (Massive Open Online Course) = an unlimited enrollment course with no prerequisites (often free as a sampler), usually taught by a professor of elite standing, and delivered through ICT.
Slideshow = PowerPoint or KeyNote stack of “slides” or static illustrations that may or may not include embedded video/audio, hyperlinks, graphics, and transitional sequencing.
Video lecture = typical lecture or speech captured on video and archived for later observation via ICT.
Demonstration = presenting evidence in the form of firsthand experience (vicarious or observed) that can be performed live (synchronous) or recorded (asynchronous).
Passive animation = without interacting, observing a collection of still images and objects that appear to move in two or three dimensions.
Reflective discussion = contemplative conversations that highlight lessons learned, transfer learning into daily life, and maintain those changes.
Peer feedback = students provide constructive criticism and affirmative advice to one another as guided by rubrics, but may also grade assignments, critique writing or speaking presentations, and instruct/tutor their co-learners.
Questions = a request for information that hopefully results in the confirmation of knowledge, new found awareness, motivation to gain greater understanding, and/or discovery of new information.
Games = a form of playing to learn, where students learn from metaphoric connections and become more engaged in the subject matter by adding elements of friendly competition, rewards, consequences, awards, and rules/guidelines.
Active simulation = while observing, also interacting (where changes occur based on user responses) with a collection of still images, videos, and objects that appear to move in two or three dimensions.
Debate = collegial arguing of viewpoints and perspectives, usually on opposing sides of a controversial issue, for the purpose of better comprehending concepts and possibly resolving conflicts.
Problem solving = reacting to real world predicaments by deconstructing the dilemma, understanding its elements, and learning how they interact with one another in order to resolve it.
Collaborative projects = working as a team to apply knowledge in real world situations that are somewhat similar to those learned about in class.
Experiential learning = gaining meaningful and relevant knowledge by direct and purposeful “hands-on doing” of an action, followed by reflection on the lessons learned, integration of those as change in daily life, and continuation of change as growth in the face of erosive social and environmental forces.
Online capstone venture = usually in a team, learners complete a culminating experience that incorporates past learning, but in a new way or unfamiliar situation that differs from those in class.
Digital work placement = voluntary (or paid) labor that is conducted remotely through ICT and provides real-life challenges with on-the-job training.
Virtual field trip = deep and broad co-browsing excursion into websites like those of a museum, art gallery, or other educational institution.
Community service = learners work together to bring change online that benefits society or non-profit local organizations.
Laboratory = a controlled online environment where learners can practice research and development in relative safety.
Online partnership = the relatively equal or equitable relationship between the mentor and protégé (mentee) that is conducted through ICT.
Improvising = making something from whatever is available with a process that is likely to include attribute listing (inventorying characteristics) and forced relationships (recombining those characteristics in novel ways) to improve or invent something new.
Logical reasoning = the use of induction, deduction, abduction, and evaluation as means to truly know information and refine judgment of its accuracy, like examples of the Scientific Method applied to research and development.
Brainstorming = generating fresh ideas without restriction or evaluation with a process that is likely to include extended effort (continuing when finished) and deferred prejudice (not jumping on the first good idea) in order to diverge many options.
Creativity techniques = producing alternative ideas or variations by reversing features, changing sizes, and/or busting assumptions.
Imagination exercises = activities designed to get learners thinking differently, like drawing with their non-preferred hand and/or playing in a virtual makerspace.
M.O.O.C. PROS AND CONS: For those who haven't heard it before, the term "MOOC" (mook) stands for Massive Open Online Course. Massive indicates very large numbers of participants, often due to uncapped or unlimited registration. Open refers to eliminating participation barriers such as enrollment (no entry requirements means anyone with interest can participate), materials (frequently open educational resources are used and expensive textbooks are avoided), and cost (most are free). Online suggests a delivery method employing technology that ubiquitously serves up publicly accessible information. Course elements include self-paced learning (within a reasonable schedule), a fee for credentials (credit or certificate), and a single instructor (usually a professor of elite standing). A typical MOOC involves watching a captured video lecture or listening to an audio podcast, linking to additional open education resources on the web, and following-up with multiple choice quizzes or other assignments such as posting work in a blog or e-portfolio and conversing with peers in a forum or chat room. Interrelation among participants is mostly cooperative. Therefore, MOOCs represent a change in scale and not necessarily pedagogy.
MOOCs began emerging in 2008 with the first such labeled course offered from Canada. Driven by the desire to examine further cost cutting potential, several Ivy League universities joined in. By 2012, dubbed the “Year of the MOOC” by the NY Times, MOOCs were fully emerged with several early USA-based competitors like: Udacity, EdX, Coursera, Udemy, Kahn Academy, etc. Here is a summary for some pros and cons unique to the MOOC.
MOOC vs. SMOOCC: In 2012, aware of some of the notable shortcomings of MOOCs and the need to improve on them, I experimented with a Synergistic Collaborative version that I called a SMOOCC (smook). Since I was teaching E-Facilitation, a course I had frequently taught online from 2000-2005, I believed this variation should similarly practice and be highly facilitative in situ. I added more sophisticated communication tools, enabled the formation of interactive study buddies, groups and teams, encouraged transdisciplinary collaboration, and added some more bells or whistles.
In addition to the internal communication tools of chat rooms, discussion fora (forums), blogs, and e-portfolios, we added outside communication tools and collaboration applications such as: wikis, tweets, social networking media, Rich Site Summary feeds, video conferencing, web conferencing, document sharing, and several other highly specialized collaborative applications. Since much is lost in translation, study groups were formed around common native languages so as to enable effective communication of ideas (this unfortunately reduced cross cultural exchanges: an obvious MOOC benefit). The techniques of transdisciplinary collaboration, problem solving, and creative thinking, were taught and practiced to co-creatively solve practical problems related to facilitation. In doing so, participants self-facilitated their own processes by applying new learning to further next learning.
Lectures and demonstrations were live, instead of video prepared. However, these were captured and archived by the sponsoring organization for re-use by participants who missed the original or were dispersed in different time zones. Instead of presenting my singular view of concepts, I supplemented the presentations with explanations of many guest facilitators from online communities around the world in their own words (with simultaneous translations). Participants were asked to source, interpret, critique, apply and evaluate information. They discussed, aggregated, replicated, remixed, repurposed, reflected, published, distributed, and translated their findings. Participants coached and peer-assessed one another's facilitation performance, as guided by complex rubrics. However, this still needed some improvement: we need to find better ways to evaluate learning in the massive environment.
Participants engaged in both synchronous and asynchronous learning experiences that were interactive and collaborative. Participants had study buddies. Study groups were purposefully formed from these partnerships. Several study groups were combined into collaborative teams. Each team was provided with a different real-life facilitation problem that was plaguing the sponsoring organization. Participants were asked to solve it by initially working together in study groups and then again in the larger collaborative team. Study groups co-created initial ideas and then cross fertilized several of these ideas into a single solution from the collaborative team. Teams presented their solutions to the entire course and these were refined by input from any participant. The refined solution to each problem was implemented by the sponsoring organization.
Participants paid a small fee to enroll, although the sponsoring organization paid all costs associated with course development and delivery, including a textbook for each participant. If a participant completed the course, they received a full refund. If not, the fee was donated in the participant’s name to a charity associated with the sponsor. This increased retention with over three quarters of the class completing and more than half passing the course. Eventually, many participants met in person to socialize and further their facilitation development.
Additional motivation was provided in the form of regularly updated feedback, progress, games, and rewards. Each participant received feedback from peers and by automated grading after every synchronous or asynchronous session and all were tracked by myself and several teaching assistants. The progress of each participant was continually mapped or measured by completion of specific competencies and these data were also tracked by us. In combination, these two added motivation to seek additional learning and/or practice to gain competence. Gamification was helpful for some content, while rewards worked for others. Following a competency framework with a stamped passport approach, rather than points or badges, allowed participants to see their path to completion without competing with co-learners.
This SMOOCC was similar to a "connectivist" MOOC (rather than the "extended" MOOCs of commercial providers) in that new learning was connected to old learning and the learning and connections were managed through other connections among people in a social network. The SMOOCC is unique in that it purposefully structures for collaborative connections and maintains these through the practice of facilitation (the point of this particular course). Therefore, I think my next step will be to experiment with delivering a more openly facilitated SMOOCC on Innovation and Sustainability. Perhaps pre-formed intact groups will participate and make a practical difference or contribution in their local parts of the world.
In conclusion, a great deal of criticism has been leveled at MOOCs. This is common for all potentially disruptive innovations in education. Given time, I am confident that MOOCs will become more sophisticated in ways that parallel the past evolution of touted potential disruptions such as: online learning, technology enhanced face-to-face instruction, experiential education, television, radio, and early organized classrooms. Whether MOOCs reach their full potential for disruption and eventually challenge the credential delivery of institutionalized education remains to be seen.
THE ELEARNING SOPHISTICATION SCALE: I began measuring elearning quality and had crude scales for sophistication and adoptive criteria in 2011. A few years later, Bryan Fair, my Head of eLearning at BCIT, helped refined the scale for determining the degree of sophistication in the format of online courses. The values range from 0=static pages to 10=Adaptive Interaction (this dynamic scale will change over time as new technologies emerge). Note that most current university courses are "canned" (0 through 2) and are simply poor quality education from the classroom ported into the online learning environment. On the flip side, I was extremely impressed with the 3D modeling functions (9) that we had at the Learning and Teaching Center and how we could create a jet engine model or human anatomy that learners could practice with on their mobile devices before they went live in the labs. The cost savings in wear and tear of parts and organs paid for the 3D model development.
Adjacent to this scale is a little acronym we used to remember the selection criteria which ought to be applied to any new technological tool under consideration for adoption in elearning. STUDENTS should come first!
FOUR DIGITAL CONTENT DELIVERY CHANNELS: Based on the above Sophistication Scale, and not on styles of learning, we agreed that all of our online campus course designs, program webpages, and elearning curricula would be recreated for delivery of digital content on all four channels: activity, textual, auditory, and visual. Each of these four has some suggested ways to achieve this for both digital instruction and assessment content.
HALLMARKS OF THE E-LEARNING CAMPUS: So far, e-learning has evolved through three generations. The first generation was all about one-way transmission of information for the simple delivery of knowledge. The second generation improved to become two-way or transactional with the interactive exchange of information. Yesterday, learners could ask questions, break out into private discussion spaces, and provide emotive feedback (I’m bored, confused, or left behind). The third generation, being developed now, is becoming more collaborative in process and content. Today, it allows learners to teach one another, complete specialized group combination projects, and co-create team solutions to problems posed by their teachers.
When advising higher education institutions on setting up their E-learning campuses, I tend toward the future and present them with this model that emphasizes the collaborative third generation. As diagrammed in the third iteration of a Sierpinski Triangle (a fractal triangle divided into composite triangles only three times), the model has three important pieces highlighted in yellow:
A. Tools for COLLABORATION, including types of collaborative behaviour, kinds of collaborative groups, and ways to share information to enable collaboration;
B. SUPPORT services, such as the help desk, bookstore, library, career planning, and others; and
C. PLATFORM resources, like an online Learning Management System (LMS), and tools for synchronous (same time) or asynchronous (different times) instruction.
This model really emphasizes collaboration, because this is the next missing piece that is presently being addressed by programmers. Each of these three pieces is further broken down into three components highlighted in orange, each with triple blue contributing elements.
Behaviour and interaction during collaboration require mutual trust, respect, and reciprocity; should be ruled by self-determined guidelines from group norming; and follow a process that is best moderated by a neutral and objective facilitator.
Groups for collaboration may range from study cohorts that may teach each other, through problem solving teams that address real world case studies, to a community that assists one another with technology concerns and instructional issues.
Information sharing includes various methods of communicating content, different means to monitor and organize data for tracking the progress of collaboration, and assorted techniques for co-creating, editing, and publishing collaborative outcomes.
The help desk offers troubleshooting services for learners’ technology issues, develops self-help resources for them to consult before making contact for additional assistance, and treats all of their contacts and needs with a customer service attitude.
The bookstore and library work together to provide i-books and e-texts, give access to online journals and reference materials, and keep the repository of learning objects and open education resources that are reused by faculty in their instruction and course design.
The other services that must be addressed are career planning and internship placements, storage space for the creation of personal e-portfolios, and plagiarism detection testing for learners to check their assignments for “re-used” content before submission to be graded.
The LMS must be: robust enough to function in an online environment beyond classroom work, able to assess learning, interactive with users, adaptive to learning progress, able to track and report gains, and supportive of curriculum development by faculty and others.
Learners should be provided with access to, and training in the use of, asynchronous tools such as: solo web browsing, email, a discussion list or forum, video with accompanying audio, and voice mail (phone messages) as a minimum to attend e-learning courses.
In addition, they should receive access to, and training in the use of, asynchronous tools like: group co-browsing, chat, messaging, voice over internet protocol (online telephony), and web-based video conferencing as a minimum to participate in an e-learning campus.
MEASURING ELEARNING QUALITY: In 2012, I identified 8 critical factors to determining the quality of e-learning and each factor has a different method of evaluation. The factors and their respective evaluation methods are:
PLATFORM: apply the selection/adoption criteria (thermometer above) to assess the quality of the LMS being used
SUPPORT: examine the technological infrastructure to judge its efficacy around speed, latency, capacity, bandwidth, etc.
DESIGN: use the Standards from the QM Higher Education Rubric of Quality Matters to appraise the course design
DELIVERY: employ process (formative) and outcome (summative) evaluation to measure course delivery quality
CONTENT: get opinions from a panel of expert stakeholders: professionals, faculty, staff, students, and alumni
FORMAT: apply the sophistication scale (0-10 ruler above) to determine how well the course content is formatted
STUDENT: ask learners to conduct a self-review of their readiness and preparation to learn in an online environment
FACULTY: ask master teachers to peer-review their colleagues during course delivery and online instruction
E-LEARNING DOCTORAL CURRICULUM: In 2010, I was asked to develop a doctoral-level e-learning curriculum for a US online university. I designed a 60-credit degree (equivalent of an intensive residential commitment held full time over two years) that could be delivered part-time through online settings over several years. Due to the rapidly transforming environment of E-learning, curricula such as this would need updating every six months.
Students take four required core courses for twelve credits. They then select one of three client groups to concentrate on and six more optional courses from a field of twelve offerings for a total of 21 elective credits. A further nine credits constitute their comprehensive oral exams with a chosen pairing of two research courses. This is capped off with 18 credits of dissertation, proposal, completion, and defense.
DEFINING DEGREES OF TECHNOLOGY: When folks speak about eLearning, they tend to think only about online forms of learning with technology, but I believe that eLearning also includes the technology-enriched classroom and the blending of fully online with technology-enriched. In other words, eLearning is the spectrum below of the three columns on the right. One important area of teaching that is often excluded from consideration in eLearning is experiential education (fieldwork, labs, internships, etc.) and so the definitions below (from 2009 work with my Dean of Faculty, Joe Lucero) include experiential elements like labs and field work.
EDUCATION ANALYTICS: Like any form of evidence-based inquiry and/or research, education analytics is simply the measurement, collection, analysis, interpretation, and reporting of data that are useful in understanding the way people learn and in discovering the best experiences and environments for teaching. Generating the large amounts of data necessary to run these analyses, and subsequently drive decisions, requires deep mining of big data sets, while simultaneously retaining students' rights to privacy.
These days, with very large data sets and "Cloud Computing" power availability, ACADEMIC analytics (as above, but for administration beyond teaching and learning) has been common. In fact, when I was an academic administrator, my laptop's background screen was a dashboard displaying real-time aggregate data on enrollment numbers, retention percentages, probationary or at-risk students (see below), and unsatisfactory academic performance. I was also able to get a snapshot of faculty performance and online teaching behaviors. These academic analyses permitted my staff to intervene in problematic situations and apply solution-focused advising methods.
Recent advances in "Fog Computing" have served to make LEARNING analytics increasingly possible and this has led to adaptive learning (where curriculum contents and teaching delivery methods change with the needs of the learner). Now when I teach a course, albeit online, I get similar analyses sent to my phone about students who have not completed assignments, like posting to discussion boards, have performed poorly on problem solving quizzes, or have not contributed equitably to their team-based projects. This helps me redirect my attention to their learning and make the adaptive experience more personalized for them.
I drew this diagram, in 2013, to help faculty understand the main differences. The benefits of the Cloud are obvious: cost savings, independent security, great scalability, high performance, no maintenance, efficient peak load sharing, reliable, agile, flexible, and mobile. However, some drawbacks include: lengthy processing times (especially for big data), large bandwidth usage to upload and download between the Cloud and personal devices, and slowed delay times due to remote locations of the data center. In a symbiotic-like relationship, the personal device becomes the input/output interface between people and the Fog/Cloud network. Fog computing provides: real-time data processing, temporary data caching, and computational offloading. The Cloud offers: machine learning, massive parallel data processing, and mining or management of big data sets.
Note that Higher Education, in this diagram, is the Ivory Tower Lighthouse scanning the fog and clouds for four kinds of analytics (higher education, institutional, academic, and learning) applied over four levels (government, executive, administrative, and teaching) and used to describe and diagnose or to predict and prescribe.
IDENTIFYING & WORKING WITH “AT RISK” STUDENTS: In 2010, at two institutions, we used canonical discriminant function analysis to predict which online students would thrive (perform well above average), survive (perform on average), struggle (perform below average), and leave (drop out of) their e-learning courses. We fed dozens of variables into our computers and the statistic (like factor analysis) produced two functions or factors that combined to accurately predicted student performance.
The first factor was called academic resiliency and was composed of variables like grade point average, age, technoliteracy, and past performance in similar courses or class assignments. The second was called engagement behavior and included variables such as times spent online, attendance, participation, study habits, willingness to seek/accept help, desire to improve, and known life challenges. Eventually, we developed predictive algorithms from these data and outcomes. With some variation, but as expected, students who were thriving had high resiliency and high engagement, while those who were surviving showed high on one or both factors as in the diagram. On the other side, students who were failing had low resiliency and low engagement, with those who were struggling showed low on one or both factors.
The failing and struggling students were deemed “at risk” and were assigned to a specially trained academic coach who provided extra services beyond those accessible to the general student body. These extra services included additional study groups, skills seminars, training for success, tutoring, professional career clubs, and social events designed to increase collaborative learning. These coaches also counseled students on addressing life challenges and improving their engagement through solution-focused methods of student advising. For more on the latter, read about solution-focused leadership.
The end results were a significant increase in the number of students saved in a course and a significant increase in those retained by the institution. This contributed to increasing enrollments and maintaining accreditation.
WEARABLE TECHNOLOGIES: When I was running and hiking at university, I had a calculator watch that I used to figure out how long, far, and fast I had gone. Since then, my sister has had a succession of insulin pumps to counter her diabetes. The pumps administered the correctly calculated dosage of insulin required. Later ones measured blood sugar levels and more recent ones provided output to her smartphone. These are very early examples of wearable technology: computing devices that are attached to, or integrated within, the human body.
Wearable technologies achieve at least one of several purposes: informing, entertaining, identifying, controlling, monitoring, and protecting. Like all modern computers, these devices inform (access navigation or climate data) and entertain (record or play video or audio). Unlike some computers, these devices identify (keep or biometrically access passport files or concert tickets) and control (wallet finances or body functions). Uniquely, these devices monitor (measure heart rates or exercise impact) and protect (administer medication or manage stress or pain). To these ends, I have been helping a few fashion designers (students of my fashion designing spouse) to explore these new opportunities with smart/e-textiles and so I prepared this infographic to cover their range of options.
ARTIFICIAL INTELLIGENCE & MACHINE LEARNING: I received a set of Matryoshka nesting dolls from a Russian friend; you know the kind that sit inside one another and open up endlessly with a new doll each time one is opened. In 2012, this is how I viewed the terms: Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning. Each one is a subset that sits fully inside the other as shown in the diagram.
Artificial Intelligence (AI) is the ability of a machine (excluding humans and other animals) to think by demonstrating at least one of the following types of intelligence: knowledge, perception, logic, comprehension, creativity, analysis, reasoning, ethics, decision making, problem solving, sound judgment, self-awareness, and learning.
Machine Learning (ML) is a type of AI where algorithms (written rules of procedure) underlie the thinking of computers by providing them with the ability to learn (improve with practice) by “rewriting” themselves in terms of their pattern recognitions, statistical analyses, or predictions.
Deep Learning is a type of ML that involves the creation and use of neural networks (computer systems modeled after human brain/nervous system) and data hierarchies (information organized in order of relationships among the components of the greater database) in a manner that parallels human thinking and permits machines to respond to sensory stimuli (touching, tasting, smelling, seeing, hearing, and speaking) similarly to the ways humans would.
AI and ML hold great potential for E-learning in terms of custom designing curricula and providing adaptive feedback to learners. Obviously the more cognitive the material, the more impactful these will be. Motoric skills and socio-emotional content are currently more difficult to E-learn than cognitive, and AI and ML will have the greatest benefit to latter until that situation changes. E-learning becomes even more just-in-time and on-demand than it was before AI and ML.
In short, AI and ML identify patterns or trends in learning that might indicate an unnecessarily confusing question or concept for a significant number of learners in the class (leading to course improvements) and/or extract data from the past performance of learners, analyze these, and predict what each learner needs for added emphasis. Also, AI and ML can constantly enhance their identification, improvement, extraction, analysis, and prediction systems by automatically learning from successes and failures to become almost intuitive. Here are some examples of how E-learning can benefit from AI and ML.
Learning mistakes: When a learner makes an error, either in answering a question or responding to a new concept, AI and ML will provide targeted information with additional testing to ensure that learning adapts and endures.
Avoiding unnecessary repetition: AI and ML can determine gaps in learners’ knowledge and substitute in for the missing information with targeted “lessons” for them and can “skip” some of their lessons that appear to be already learned from the way learners responded to previous questions or concepts, hence not wasting valuable learning time and effort.
Personalizing delivery: For learners who respond better to textual, auditory, visual, or activity channels, AI and ML can increase the amount of delivery by these preferred channels for difficult concepts and/or decrease the amount of preferred channel delivery for concepts that are easily understood, thereby broadening the learners’ abilities to respond on other channels.
Adapting pace: In addition to adapting learning content for particular learners, AI and ML assist educators with accommodating slower learners and engaging quick learners by customizing the pace for each learner, accelerating when learners rapidly learn content, and decelerating when they have difficulties learning.
Identifying risks: AI and ML can crunch enormous amounts of data from past academic performances to identify those learners who will be most at risk for failing a particular course or assignment and therefore be in need of additional attention from a tutor.
Practicing with chatbots: Chatbots provide automated conversation (either by voice like Siri and Alexa, or by text such as when you think you are chatting with a person, but it is actually a robot conditioned to respond to certain word combinations), send reminders to learners, and provide practice for written response to questions and assignments. Today, chatbots make recommendations based on preferences (Amazon and Netflix as examples) and can direct learning with recommended courses or units. Tomorrow, chatbots will evolve into virtual tutors that will be able to teach and assess learning, as well as direct it.
Sensing emotions: Eventually, AI and ML (through deep learning) will be able to sense the discouragement or frustration in a learner, identify their tendency to compete, or understand the joy they receive through celebration, and then reinforce or reengage those learners by responding accordingly.
Motivating learners: The individualized E-learning experienced described here motivate learners by highlighting their goals, suiting their pace, availability, or convenience, and helping them to feel like their time isn’t being wasted on irrelevant and ineffectual education.
Saving time and money: The above benefits assure that learners’ time is spent learning and that actual learning time is reduced so they are “off-line” (away from the job) less than with traditional or classroom-based forms of learning and this saves money for their sponsoring organizations.
Optimizing analytics: Often educators must spend a considerable amount of energy to analyze big data in order to see where to put their limited resources, however, AI and ML can do this for them, so they have more resources to allocate toward helping others learn.
Automating tasks: Those same educators spend considerable time with crucial, but tedious and repetitive tasks that AI and ML can automate such as scheduling, delivery, and assessment, and this will allow educators to concentrate their efforts on the human side of generating unique learning plans for each individual learner with the help of AI and ML doing the big data analysis.
Developing content: In my experience, development takes twice as much time for E-learning content than for traditional face-to-face classroom content on average. AI and ML (through deep learning) can instantly classify massive fields of content to accelerate the instructional design process and save costly development time associated with humans examining content and repurposing old learning objects for new courses.
Changing curricula: In disciplines where subject matter evolves continually, courses should be updated regularly and content must be repurposed to do so. AI and ML can assist with all of this, but are particularly useful in ongoing course improvement from the analysis of big data associated with successes of curriculum and instruction to deliver efficacious learning. Any curricular changes can be communicated by chatbots to past learners as a form of continuing education.
Translating education: Finally, AI and ML can aid in the rapid and accurate translation of learning into other languages, where the need to check with a human translator is becoming considerably less as processes improve.
Returning on investment: All of the above benefits translate into greater profitability for higher education institutions and other sponsor organizations in terms of time, energy, and money.
In the end, we have to make certain that high tech is complimented with high touch so that AI and ML don’t dehumanize E-learning. AI and ML should be used as tools (the defining element of technology) to amplify human intelligence and not as the ultimate goal of hyped technological progress. E-learning has been revolutionary in terms of cost savings, convenience, and simplicity, but one size has had to fit all. As AI and ML improve, E-learning can be increasingly custom fitted to the unique learner.
AUGMENTED REALITY & VIRTUAL REALITY: Reality exists on a spectrum from actual, through tangible and augmented, to virtual (with mixed reality as a combination of augmented and virtual). Each category depends upon the degree of computer generated enhancement or alteration of what is seen. Here are the definitions I like (with genuine examples from my experience in parentheses):
Actual = This is the real world environment without any computer influence or generation (even if it is a movie viewed through 3D glasses, this is actual reality for our purposes, despite computers being used to create the movie);
Tangible (TR)= Here the real world environment is changed in some way and the change is measured and recorded by computers that then generate a representative environment (examples include 3D models that are drawn in response to students working on actual jet engines and using a 360 degree camera to photograph a home for sale and then creating a realistic “open house” tour for prospective buyers who cannot visit in person);
Augmented (AR)= In this instance, computer generated information is added as an overlay to the environment as viewed through a device (one example shown is Wave Rock in Western Australia, where your smart phone senses GPS location and describes the name with additional information available on geomorphology and aboriginal history – – while another example is your tablet recognizing a famous painting through visual image search and listing the title, author, and description of particular brushstrokes by text or with audio transcription);
Virtual (VR)= The environment is entirely computer generated and a user can be fully immersed through goggles or semi-immersed on a big screen, making this a noted step toward experiential learning online due to the opportunities for inconsequential risk taking and unparalleled reflections on that adventure experience (fantasy worlds, such as fairies or goblins, microscopic pond life, outer space, and even violent war, can be simulated convincingly in VR); and
Mixed (MR)= This combines some augmented overlay with some virtual environment (when you view the night sky through the camera on your device, you see the stars identified with information accessible about each, but you also see the constellations drawn into a fictitious space environment with enlarged satellites that wiz by and the location of the sun at your feet on the other side of our planet – – when viewing a museum exhibit through your device, close examination can describe the visible content through augmented interpretative information and draw in the virtual identification of hidden invisible content inside).
To understand these five in comparison, consider your human body viewed in the mirror without (actual) enhancements to see only your outside skin. When you walk on a treadmill and are filmed doing so, computers generate a (tangible) representation of your movement through an avatar used to analyze your motion, gait, and any biomechanical issues. When you perform a close up “selfie” of your face, the phone can identify your eye colour, skin blemishes, hair growth, and changes in mole/freckle appearance, through visual recognition and provide (augmented) information on its screen to aid with grooming or alert the need to see a physician. Imagine in the future that, with certain software apps, you can scan your arm by tablet and “see” inside to view the (virtual) bones, muscles, nerves, or vessels and to identify injuries or diseases. With the display of (mixed) information, the malady can be diagnosed with a recommended treatment. These are the coming examples of AR, VR, and MR.
APP ADDICTION BY DESIGN: Early in my life, I was accepted to medical school and was destined to become a physician. As a kinesiology undergrad, I studied human metabolism, neurochemistry, and environmental medicine (high altitude and under water physiology). Later in life, I worked with clients who were addicted to alcohol, drugs, gambling, and other vices. I understood that the addictive process was difficult to change and society clearly didn’t want addiction consuming its citizenry. When I started designing groupware (specifically for virtual team-building), I recognized that successful software has all the properties of an addictive process. While administering e-learning courses, I noticed those same gamification characteristics were being exploited to encourage users to sustain their time spent online.
A few years ago, I closed my accounts and removed several social media apps and games (Facebook, Instagram, Twitter, Candy Crush, etc.) from my devices, when I realized I was starting to be dependent on them. The trick in education is to balance gamification positives against addiction negatives. Let’s take a look at the addictive qualities that are purposefully included in modern application design.
I use the term addiction deliberately, because I believe withdrawal and relapse can occur with cessation of technology addiction, thus making it more than mere dependence, habit, obsession, or impulse. For our purposes, we will define addiction as compulsive participation despite negative consequences. If a user concentrates on an app without noticing the passage of time (a whole hour went by!), or app usage gets in the way of life, health, work, growth, and real time relationships with friends or family, then this can easily become addiction. The average professor will speak volumes about students disengaged from lectures and fixated on their smart phones. That same addiction has transferred to the workplace, where employees waste work time on social media. It’s like having your own personal slot machine!
The initial and most obvious quality to consider is the trigger-action-reward sequence. The user receives a notification (trigger) that someone has commented on their posting for a popular social media site. The user immediately opens the app (action) for that site and reads the positive comment resulting in a Dopamine release in the brain that causes a good feeling (reward). This first quality is accentuated and accelerated by almost all applications from AOL’s “you’ve got mail!” to smartphones’ badges and alerts.
Reward is the next quality of concern. When the usage of an app seems inherently pleasurable, it will be sought out more frequently, regardless of any potentially damaging ramifications that may result. Think of how flashing lights and captivating sounds mesmerize app users by overwhelming these two senses so that all other messages (that they might normally receive on either sensory channel) are tuned out. Again, this slot machine approach provides enjoyable rewards that distract from daily life functions.
Reinforcement is the final quality of concern. When the user receives feedback for sharing a photo or tweeting a comment, their occasional activities can become continuous habits. Getting as many “likes” as possible becomes the goal for addicted users. The need for social approval encourages us to seek the largest network of friends. Think about why the color red is used to alert users that hypnotic feedback awaits. Reinforcement increases the likelihood that an addicted user will continue to seek compulsive participation even in the face of harmful drawbacks. They will be unable to stop playing slot machines.
In addition to obvious disease risks associated with mobile devices (bacterial infection, electrosensitivity, radiation sickness, and radio frequency cancer), these technologies also bring formidable other risks to the addict. These include, but are not necessarily limited to: increased financial debt from overage use, changes in sleeping/eating/toiletry habits, psychological distress, anxiety or depression, and shifts in social behaviors due to dependence on cyber-communications. For example, some users would rather use, or even pretend to use, their smart phones in order to avoid human interaction at social events. We understand how these devices have improved our education, business, and entertainment lives, but are completely oblivious as to how they are transforming our social, emotional, and psychological lives.
These addictive properties are so pervasive and persuasive in modern apps, that users spend enormous amounts of time looking at their devices. So much so, that we now have apps (Moment, BreakFree, AppDetox, etc.) to track our usage and let us know if they think we are addicted or not. The Internet is full of discussion around addiction to dating apps (busy or socially adverse users don’t actually go out on dates in person, they just enjoy the “rush” of flirting with many users and the power of “ghosting-off” others). A new industry dedicated to overcoming behavioral technology addictions is growing rapidly.
I worry that our Wireless Mobile Devices (WMDs) are becoming weapons of mass distraction for a young population that is all too readily addicted to Dopamine exploitive apps. Things can only get worse once virtual and augmented reality become commonplace. Right now, we see a generation of users who: cannot use language correctly, are unable to publicly say something offensive, but find online anonymity makes posting that same offense very easy, and want to make a positive difference in the world, but are all too easily drawn into destructive and divisive debates online, because they lack critical thinking skills. Here are some signs or symptoms that one might be addicted along with some counteractive strategies.
The question remains as to whether or not technological addictions are uniquely dissimilar enough from other addictions so as to diagnose or treat them differently. The majorities of people who present with technology addictions also have one or more underlying mental health disorders and may have other addictions being fueled through technology. To clarify these interactions, I have categorized types of addictions into three defined classes (along with some examples in the diagram below).
Internetindependent: the addiction does not require online connectivity to manifest, will take place without this linkage, and not change with ample access.
Internetco-dependent: the addiction may not need an online connection to occur, but the presence of some connectivity can easily enable the addiction.
Internetdependent: the addiction fully requires online connectivity to manifest and cannot take place without this access.
SYSTEMS DEVELOPMENT (6D) PROCESS: Many of these "life-cycles" exist for software engineering. This is ours. In the late 1990's, working with John Chen (patent holder for Microsoft Exchange) and Brandon Albers (Programmer Extraordinaire) on virtualteamworks.com (described below), we adapted this process for our early collaborative work developing software and hardware systems. The 6Ds are:
DEFINE = Assess needs by multiple sources and methods to create blueprints that specify all sponsor requirements.
DRAFT = Engineer by coding (for software) and/or constructing (for hardware) prototypes of the system (either/both).
DEBUG = Fix functions that don't work to attain a final working product for release (over the immediate short term).
DEPLOY = System is put into play, where sponsor provides feedback and data are gathered on system performance.
DEEPEN = Long term improvement of system (by repeating 1 - 4 in order) to create new versions with increased value.
DISPOSE = The criteria underwhich a system is retired and how past data generated by that system are then handled.
Since we have found this approach useful in developing systems beyond hardware and software, I continue to teach it in higher education and management situations and apply it as a variation of organizational development, where the enterprise is a system.
GEOTEAMING: Initially, John Chen and I met near Seattle around 1997 and formed virtualteamworks.com with several others. John was an ex-Microsoft executive with some software patents and technology success stories behind him. He wanted to do more in experiential training and I wanted to do more with technology. So in those days we traded a lot of great ideas. In early 2000, he was looking for a team-building activity he could do with very large groups. From one of my books, I suggested using GPS receivers to locate clues in a kind of orienteering journey and we came up with GeoTeaming: GPS-based scavenger hunts for competing and cooperating groups.
At the same time, GeoCaching was taking off in the USA and just happened to be based in Seattle. In late 2000, John and I met with the GroundSpeak team (managing the movement) and got their support and blessing for the idea. For the next four years, we divided our attention between virtualteamworks.com and GeoTeaming.com, until the sale of the former, when John solely took on the latter and grew it in a big way. Today, every team-building company has some form of a GPS game that copies some (but not all) of what he does.
TEAM-BUILDING: I've been building teams since the early 1980's and feel like I have experientially found a progressive method that seems to work for most groups. I liken the process to that of constructing a house. Initially, the foundation is laid and then the walls are framed atop this base. Next, a roof is added to cover everything and the finish touches are applied. In this house metaphor for team-building, the foundation is composed of trust, communication, and cooperation (without these three as a solid base, all subsequent development efforts can fail). The walls are the elements of a high performing team (these will vary by situation, but the partial list shown is a good start). The roof is leadership with its qualities (again these vary by situation with some examples shown). The finishing touches are empowering the team to grow independently.
Foundation: I start with trust and communication. These are reciprocally related and erosion of one erodes the other. However, improving one does not necessarily mean the other improves consequently. Therefore, I tend to concentrate on learning experiences that alternate their emphasis from one to the other until both trust and communication have reached functional levels. Once this first milestone has been passed, I search for some demonstrated evidence of cooperation (conditionally sharing some resources and operating together to change something for the benefit of selected members) and enable the transition of this toward collaboration (unconditionally sharing everything and working together to co-create something new for the benefit of all members and/or others). The appearance of sustained collaboration indicates passage of a second milestone.
Walls: With this foundation in place, and the second milestone behind us, further development can occur with the many elements of teamwork, such as: planning, role clarity, common goals, change, commitment, problem solving, decision making, risk taking, diversity, safe space, co-creation, conflict resolution, creativity, consensus, accountability, respect, encouragement, sharing, ethics and empathy. I tend to independently address these one at a time with simple group inititative activities and other challenges, and then test these elements in synergy using more complex group initiative activities and challenges. In the growth of most teams, they typically tend to reach a breakthrough moment or tipping point where their resistance to change falls away and they embrace their new levels of functional performance. This third milestone signals an easier transition toward functionality and suggests the time to address leadership.
Roof: Once the team is performing well and most behavioral elements are functional, I tend to deliberately shift leadership around to different members by purposefully selecting leaders for specific tasks. By doing so, the team learns to share leadership based on the strengths of its members. With a functional and high performing team, we can begin exploring leadership components such as: influence, style (autcocratic, democratic, and abdicratic), orientation (task or relationship), and conditional favorability (group unity, team competence, leader experience, decision consequence, and environmental risk).
Finishing touches: After the fourth milestone of functional leadership has been surpassed, I like to leave teams empowered and independent. Sometimes this means teaching them to self-facilitate and/or continuing to develop without assistance. On other occasions, this means helping them deal with their issues as they arise and without the need for a facilitator to enable their processes.
TEAM RESEARCH: As Executive Director of CATI, my colleagues and I conducted many studies on the efficacy of experiential teamwork training and leadership development. For a more detailed summary of the research studies (1990-1997), download the 30 CATInate Abstracts. Here are some of the more interesting outcomes.
Virtual Teaming: Virtual teams showed significant improvements in their teamwork over a six month period after participating in an online team-building program. Six component subscales of teamwork (trust, communication, cooperation, problem solving, decision making, and tasking) also showed similar increases.
The purpose of this study was to track changes in teamwork and some of its composite elements over time for virtual teams engaged in an online team-building program. Three experimental groups (n= 4, 6 & 9) received the treatment, while two control groups (n=7 & 8) did not. The treatment was a four day (30 hour) teambuilding program conducted entirely online and composed of group problem solving tasks followed by debriefing discussions focused on improving virtual teamwork behaviors. Subjects (N=34) were members of five new virtual teams, located in 9 nations across Europe, speaking several languages and working in English within the manufacturing industry. They completed the vT50 six times: every 2 months over 10 month period with the treatment delivered between the second and third completion of the vT50. This valid and reliable instrument measured teamwork and six components on an interval scale from 0 (never) to 100 (always) for fifty self-report behavior frequencies as observed in other team members.
Since no significant differences were found between the two control groups or among the three experimental groups by non-parametric Kruskal Wallis Test, these were combined into a single control (n=15) group and a single experimental (n=19) group for parametric analysis. Kurtosis, Skew, and Homogeneity were found to be within acceptable limits of normality. Two-way (2 groups by 6 tests) Analysis of Co-variance used initial testing outcomes as the adjusting covariate and Scheffe post hocs to determine specific differences. The ANCOVA demonstrated interactive effects indicating that all observed changes in teamwork and the six subscales between the control and experimental groups occurred immediately after and were due to the online team-building program. This virtual teambuilding program was effective at raising teamwork by 27 points on a 100 point scale, but without follow-up programming to sustain those changes, and to fortify the virtual team, that gain ended up losing 5 points in the months that followed.
Face-to-face Teaming: These ten selected studies (1-10 from top left to bottom right) overview some of the research and its outcomes.
Experiential learning about teamwork was more effective than the classroom learning at improving teamwork, where the classroom outcomes were short lived and the experiential outcomes lasted about one year before returning to baseline.
For team building programs to be effectively utilized back at the office, they should be conducted on intact work units, rather than random samplings, and resources should be dedicated to encourage practice of team behaviors after return to work.
Follow-up procedures have a significant impact on transfer of learning, where most gains appear to erode after 6 months, but self-facilitating teams (those that learn to analyse success and failure like a facilitator) continue to improve their teamwork.
This type of programming may help companies to change their motivational climate with shifts from an autocratic bureaucracy (control-expert influence-dependency orientations) to an empowered team environment (achievement-affiliation-extension).
Sequencing of group before individual activities was critically important to creating effective teamwork, because an inappropriate order of activities (individual before group) can actually retard the development of a high performing team.
Program duration impacted teamwork development (several short programs provided slower greater gains, while one lone program of equal contact time provided quicker lesser gains), while program setting (camp vs. hotel) didn't impact teamwork.
Program design impacted teamwork development (custom tailored programs appeared to provide greater and more sustained gains in teamwork, than off-the-shelf programs), while program location (indoor vs. outdoor) didn't impact teamwork.
Combined staff paires of adventure facilitators paired with corporate trainers appear to provide the best organizational team building outcomes in Corporate Adventure Training programs when compared with either facilitator or trainer pairs alone.
A mix of metaphoric debriefing (first half of program) and isomorphic framing (second half) shows the greatest teamwork acquisition and retention when compared with all isomorphic framing, all metaphoric debriefing, and no debrief or frame.
Problem-focused and solution-focused approaches appear equivalently effective at increasing teamwork in functional groups, however, for dysfunctional groups, problem-focus was less effective and solution-focus was enormously successful.
One other study of merit was ahead of its time. In response to industry concerns about rising cardiac arrests for males on ropes courses, we were able to predict their highest heart rates attained from an equation of their age, height, weight, body girths, time to walk a mile and heart rate after walking a mile with 64% explained variance:
Highest heart rate = 192.731 + 0.521 (Heart rate after mile walk) – 1.039 (Age) +
5.818 (Time to walk the mile) – 35.226 (Height) – 68.106 (Chest ÷ Waist)
SIX SUBSCALES OF TRUST: In this team research, an additional stream of studies examined TRUST and its 6 subscales. These six are:
A=Acceptance of others' thoughts, feelings, behaviors, and ideas,
B=Believability that people are genuine during interactions,
C=Confidentiality of secrets or emotions by others,
D=Dependability of others for getting the job done,
E=Encouragement of others while taking risks, and
F = Financial willingness to loan money or objects of value to others.
Trust is developed by surprisingly simple means, but must be increased in simultaneous reciprocity with improving communication (see foundation section of the Team-building House above). To begin, trust is developed by simply doing what you say and not doing the opposite that might harm others. You must "walk your talk" or "practice what you preach" and do NOT expect people to "do what you say instead of what you do" because your actions will always speak louder than your words. Clearly state your intent and follow through by actually doing what you promised, so as not to betray trust.
Go above and beyond the call of duty by including these methods: continually update or inform others, admit mistakes, keep secrets, lead by example, listen, hold to accountabilities, respond on time, collaborate rather than compete, share the credit and take the blame, but always treat others as you would like to be treated yourself. Be truthful, ethical, open, consistent, transparent, loyal, fair, accurate, genuine, empathetic, and be the first to offer your trust. Each time you are involved in entrusting exchanges, take time to reflect and dicuss with others as an accelerant to cement trust.
Transferability is possible among the six subscales, but the degree of transference varies greatly with the parties involved in the trusting relationship. For example, if one believes (B) the truth of other individuals, then one is more likely to share secrets (C) and money (F) with them, or encourage (E), accept (A), and rely (D) on them, than with obvious liars. We have found that enhancing or strengthening some of the subscales through risk taking activities can have benefits in other areas of trust. Here are some of our research findings.
Linear Stepwise Regression Analysis predicted Overall Trust from 5 of the 6 subscales (AECBD) with 48.2% explained variance.
Physicality influenced the development of trust with greater gains in D and E for physical activities than non-physical ones.
Using clients to belay develops trust between partners (ACDE) better than using facilitators or technicians (which reduced B).
The ropes course had a profound effect on the enhancement of self-confidence. Specific debriefing (focused on self-confidence) was more effective than general debriefing (about various process topics) for ABC, but not DE.
Both group initiatives and ropes courses were effective at improving organizational trust and BCD, however, group initiatives were better at enhancing A than ropes courses and ropes courses were better at enhancing E than group initiatives.
Touch plays an important role in the development of interpersonal trust in groups and ABC, where males and females view trust differently and they gain and lose trust differently in relation to touch.
TWELVE PRECURSORS TO TEAM SYNERGY: We've all heard the "1+1>2" analogy of synergy. It means that putting the right people together under the right circumstances can lead to results where the "whole becomes so much more than just its constitutent parts." When I was a young facilitator, I asked a collection of experts "what are the right circumstances?" Their answers helped me identify twelve precursors that must be present to achieve synergy in teams and these guided my work well beyond the team-building house above. Where these twelve precursors are present and supported, teams can achieve innovation and greatness!
The initial six precursors relate to the human side of the equation and are no surprise, since many are present in the team-building house described earlier. The team must be functional (high performing, with constructive conflict, but without destructive conflict). Destructive conflicts can be erosive forces against synergy and should be avoided. The team must have a solid foundation of trust, cooperation, and communication. Their process for collaboration must be well facilitated (with agreement on how and when to solve problems, make decisions, reach consensus, use judgement, create new ideas, have fun, enjoy play, etc.). Their leader must be proficient: capable of managing team members' disparate personalities and any destructive conflicts that arise from these. This is difficult to do, since some level of constructive conflict is necessary for innovation. Balancing these two types of conflict is "the great quest" and unbearable burden of most team leaders.
The final six precursors are environmental conditions that determine willingness and ability to synergize. The team has to have previously experienced some form of communal adversity. If they have survived tough times together, then they are more likely to see the need for synergy and the value of innovation. Open dialogue means that different feelings and thoughts can be expressed and received freely and honestly. Safe space refers to a culture or climate that: supports well calculated risk taking, welcomes truth and negative information, and provides opportunities to contribute or dissent without fear of repercussions. Safe space allows for the truth to surface and permits genuine interaction. Exhibited by individuals toward one another, nurturing values of C.A.R.E. (Compassion, Appreciation, Respect & Empathy) are those "golden ground rules for treating others" that sustain healthy relationships within the team. Deliberate diversity is necessary to cross pollinate ideas. If everyone had the same perspective, then you wouldn't need a team: one individual would suffice for decisions, solutions, and tasks. However, one person means team synergy will be impossible and innovation will be limited. Finally, the team needs permission that genuinely empowers them to make a difference. Without this, their motivation will be lacking and every failure will be a fatal one.
We've all seen teams of exceptional people who fail to reach innovation because they lack one or more of these precursors. With the decreasing presence of precursors, synergy becomes increasingly difficult to attain. In turn, these synergy failures costs time, money, energy, and other valuable resources. One of the unmeasured losses in the amount of frustration talented people will feel and how those feelings get reinforced with subsequent failures. Repeatedly frustrated talent ends up leaving the organization (literally and figuratively) in favor of more functional creative opportunities. To prevent this, ensure all twelve precursors are present and accounted for.