VirtuaPro Demo Video

Here is a small video I made using Animoto as an example of what a quick commercial for VirtuaPro may look like. The commercial would be targeted toward homeowners who engage in multiple home repair projects. Just a quick disclaimer, VirtuaPro does not exist and I don't own any of the images, video, or audio used in this clip. This was made for educational purposes only. 

My sociotechnical plan involves using augmented reality as a learning tool. The concept involves developing a device that can provide instructions for a variaty of physical tasks. The device would be a headset like Microsoft Hololens that provides an augmented reality overlay that the user will see over their natural surrounding. The headset can track the users hands and movement and also see the users environment. Using this data, the system provides realtime instructions on how to perform various tasks like performing electrical or plumming work in the users house, or performing auto repair. The system would have tremendous commercial and personal uses from teaching homeowners how to fix a broken dishwasher, to guiding surgons during operations. A major impact that could result from the implementation of this type of device would be pulic concern over privacy. An example of how this may develop can be observed witht the history of Google Glass.

Google announced Project Glass in April of 2012 (McGee, 2015). Project Glass was name for the division at Google responsible for developing Google Glass, a wearable augmented reality device. Google Glass is a wearable computer that powers a small display window in the upper right or left corner of the users vision. The display can provide real time information based on several different factors like the users position, input from their phone, or even what they are looking at. Google Glass is equipped with a camera that records video and images that can be passed to Google search engines, captured on the users phone or cloud service, or streamed live over several social media applications. This is one of the aspects of Google Glass that lead to it being essentially abandoned as a product by Google.

When it was announced, Google demonstrated how Google Glass could instantly capture what the user was seeing by giving demos of skydivers, athletes, and regular users capturing their activites and live streaming them to You Tube, Twitter, and other social media sites. As Google Glass prototypes started being issued to Google employees for testing, growing public concern about privacy grew around the use of the device ("Global data protection authorities tackle Google on Glass privacy," 2013). People wanted to know if the system was always recording, or how they would know if a Glass users was recording them or sharing photos of them. People didn’t like the idea of possibly always being recorded.

One of the privacy issues with Google Glass was how it is engineered. The computer on the device needs to be small so it can be comfortablly worn, and it must offload processing to accomidate for being so small, and to reduce battery drain (Claburn, 2012). This means that the device must offload any recorded image or video to provide most of its augmentation features. Google Glass was being developed in a market where cell phone cameras were becoming the main way people recorded video and took pictures, and where debates about the ethicacy of recording strangers was a concern for many people. Google wasn’t very clear on when Google Glass would be recording or listening, and if users would even know if it was. Google wasn’t helped by reviewers wearing Google Glass in the shower, forgetting to take the devices off when entering restrooms and other private areas, and not being clear on when the device would send data back to Google servers. In the end, Google abandoned the project in January of 2015. They have since updated the software twice in 2017, but there is currently no commercial way to purchase Google Glass.

Using the history of Google Glass as an example of public reaction to privacy concerns dealing with augmented reality, I believe that concerns about privacy when using an augmented reality device can be broken down into two categories. The first is how the system processes and manages images, video, and sound. The second is the public understanding and perception of the use of that data. For the first category, I believe that we are seeing more devices being cloud enabled, and that this is a trend that is going to continue in the future.  While it could be argued that having a system that performs processing of recorded data locally would be more secure, I do not believe that this is a viable technical solution, and it is not the way that technology is trending toward. For the second category, public opinion about privacy may be changing. While Google Glass was generally rejected by the public over privacy concerns, devices like the Amazon Echo have become very popular. I believe that this is due to the Echo being in a persons home, and not a public space. Since my device would be used in a home or place of business, I think that this may change the way it is perceived as far as privacy is concerned. Each of these categories would require a large amount of research to be properly explored and could possibly be great dissertation topics on their own!

~ Ben

References:

Claburn, T. (2012). 7 potential problems with google's glasses. Informationweek - Online, Retrieved from https://proxy.cecybrary.com/login?url=https://search-proquest-com.proxy.cecybrary.com /docview/922740501?accountid=144789

Global data protection authorities tackle Google on Glass privacy. (2013). Biometric Technology Today, 2013(7), 1-3. doi:https://doi.org/10.1016/S0969-4765(13)70116-4

 McGee, Matt, (2015) The History of Google Glass. Retrieved from http://glassalmanac.com/history-

google-glass/

Serendipity and Smart Dust

Exaptation is the when an innovation that was originally designed for one purpose, is redesigned for another. A good example of this is the invention of air conditioning. Willis Carrier was trying to remove humidity from a lithographing office in 1902 (Lester, 2015). His invention was able to increase or decrease the humidity in a room and it had the additional benefit of cooling the air in room as well.

Discovery by error is likely the most common form of accidental invention. This is when a mistake during research or development returns a positive result. During software design, this is usually called turning a bug into a feature.  For example, Gmail originally had a 5 second delay when processing email. Instead of fixing the delay, developers added an ‘undo’ button that would stop the email from being sent (Leggett, 2009). This way, the error in processing the email message turned into a feature that allowed users to quickly call back an email after hitting send.

Serendipity occurs when a positive outcome is the result of a chance event. A good example of this could be getting lost but finding a great new restaurant or book store where you end up. You weren’t intending to look for a restaurant, but because of going the wrong way you found it.

In October of 2003, a graduate student at the University of California in San Diego won $50,000 as the grand prize in the Collegiate Inventors Competition for her invention of ‘smart dust’. Smart dust consists of silicon partials that can be used to detect a variety of biological and chemical agents in different media (Link, 2005). Since its invention in 2003, there have been many more proposed and applied applications for smart dust. Along with sensing the molecular structure of different objects, smart dust can be used to sense minute levels of light as well. Smart dust is being adapted to carry signals, which could result in things like wireless sensor nodes that are a cubic millimeter in size. Jamie Link was in the process of making thin multi-layer porous silicon when the silicon chip snapped. The accident released small amounts of silicon dust that held the same properties as the chip. This serendipitous event brought about this invention that has a wide range of uses in medical and environmental diagnostics and research.

References:

Leggett, M. (2009, March 19). New in Labs: Undo Send. Retrieved August 22, 2018, from

https://gmail.googleblog.com/2009/03/new-in-labs-undo-send.html

Lester, P. (2015, July 20). History of Air Conditioning. Retrieved August 22, 2018, from

https://www.energy.gov/articles/history-air-conditioning

Link, J. R. (2005). Spectrally encoded porous silicon “smart dust” for chemical and biological sensing applications. (3171107 Ph.D.), University of California, San Diego, Ann Arbor. ProQuest Dissertations & Theses Global database.

Forecasting Piracy

Forecasting is the act of predicting future trends based on past events. Typically, forecasting is used when predicting the weather. Meteorologist use past weather phenomenon as indicators on what future weather may be like. People use forecasting in almost every aspect of our lives. If you drank too much at last years Christmas party and embarrassed yourself, you may drink less at this year’s party, so you don’t suffer the same fate. What is essentially happening is that you can forecast the results of drinking too much at the party, and you adjust your actions to avoid that prediction. The concept behind this is that the future is relatively predictable, and events will tend to repeat themselves. Unfortunately, this isn’t always the case, and traditional forecasting could lead us to make the wrong decisions. Therefore, planning outside of forecasting should be implemented. Scenario planning is a different method for planning for future events. Scenario planning builds sets of likely events and then builds plans to respond to those likely events. The core concept behind scenario planning is answering the question ‘what if’ (Chermack, 2004). Scenario planning provides a benefit of allowing for the inclusion of possible events and agents of change that may be new or previously unrelated to our forecasting efforts. A good example on the differences between forecasting and scenario planning, and how this may affect business can be found by researching the music industries response to digital music and piracy in the late 1990’s.

For many years, the music industry sold music in albums. If someone wanted to listen to their favorite song, they had to buy the whole album. Sometimes songs were so popular that they’d be released as a single for less money, but it was often the case that the album had to be purchased. Piracy of music existed by creating illegal copies of these albums, first with devices that could press copies of records, to cassette tape recorders, and then digital compact disk (CD) writers. The music industry would combat these forms of piracy, as they saw each copy as a lost sale (Marshall, 2004). The years of music sales and distribution locked the music industry into forecasting the same actions to yield the same profits and success that they were used to. This all changed with the rise of digital piracy. The music industry knew that most people would prefer to purchase individual songs, but the music industry made more money when they purchased albums. This is the reason why singles weren’t as popular, not because they wouldn’t sell but because they weren’t as profitable. When music changed to a digital format, it was much easier for people to pull the individual songs they wanted and to share them between computers over the internet. Figure 1 shows how digital single downloads dramatically increased over physical CD sales and even full album downloads as digital music became more accessible over time. The first mainstream music piracy application called Napster, made digital music piracy easily accessible for people with only a moderate amount of technical knowledge. The music industry reacted to digital piracy the same way they reacted to record pressing machines in the 1950’s. They condemned the practice and raised prices in part citing lost sales due to piracy. According to their forecasting models, this was the tried and true response. The music industry had more than enough information and time to capitalize on this new distribution method but failed to do it due to poor forecasting. Instead of adopting and commercializing sharing apps like Napster, they fought them only to have hundreds of copycat programs replace the few that started. By the time the music industry decided that digital downloads were a permanent change in music distribution, the illegal methods of doing so were so refined and easy to use that they couldn’t create a system that was preferable to piracy.

Figure 1: Music Sale Trends (Rocket, 2018)

The music industry failed to capitalize on one of the biggest softball opportunities presented in any industry. They had hundreds of thousands of digital products that were in high demand. They had a model system in Napster in how to distribute their products, and they had a trend in technology that supported this new business model. The deck couldn’t be stacked more favorably in their favor, and they blew it. Instead of realizing that the market was changing and conducting any predictive modeling or scenario planning, the music industry stuck to their guns. They pushed legal action against pirates, they increased costs of physical media, and reduced access to single song digital download. In 2002, the music industry was on the verge of collapse. Consumers weren’t interested in buying the physical CD’s they were selling, and they had failed to adopt a distribution system that provided digital single song access like piracy applications had been doing for the previous few years. It wasn’t until Apple Itunes, Google Music, and other digital purchase and streaming sites became available that this trend started to reverse. Figure 2 shows that digital single song sales far surpass all physical sales previously recorded.

Figure 2: Digital Downloads (Cumberland, 2013)

Its very possible that the music industry could have capitalized on the changes in music distribution and customer demands if they would have conducted scenario planning to complement their forecasting. Scenario planning could have answered the ‘what if’ questions that could have allowed the music industry to have an adaptive strategy to embrace digital music access like iTunes and Google music did earlier (Marshall, 2004). The shift to digital streaming has reduced major music labels influence on the industry away from them and towards providers like Apple and Google. Since the need to produce physical copies of digital media has almost been eliminated, small producers can go direct to distributers like Google and get their music direct to the customers. The failure of the music industry to plan for this scenario resulted in them losing their hold on the music industry. Scenario planning can account for the social impact of change and build possible responses that can account for those changes. In the case of the music industry, scenario planning could have been the answer to keep the music industry in the same dominant position while adapting the social changes in digital music consumption.

References:

Chermack, T. J. (2004). A Theoretical Model of Scenario Planning. Human Resource Development Review, 3(4), 301-325. doi:10.1177/1534484304270637

Cumberland, R. (2013, June 13). The new music business model how did the industry get here and what's next? Retrieved August 19, 2018, from https://www.bemuso.com/articles/thenewmusicbizmodel.html

Marshall, L. (2004). The Effects of Piracy Upon the Music Industry: a Case Study of Bootlegging. Media, Culture & Society, 26(2), 163-181. doi:10.1177/0163443704039497

Music Industry Sales, Piracy and Illegal Downloads – Better or Same? (2013, July 03). Retrieved August 19, 2018, from http://www.rockitboy.com/blogs/music-industry-sales-piracy-and-illegal-downloads-better-or-same/

Traditional Forecasting vs Scenario Planning

Traditional forecasting:

Forecasting is one of the steps that is taken when planning for the future. It is a process of using current and past information to attempt to predict what may happen in the future. A family planning for a trip may use the amount of money spent of food for previous trips to forecast how much they may spend during the future trips. Meteorologists forecast future weather events in part by using data of past events. Traditional forecasting encompasses commonly used forecasting methods like the naïve forecasting method, casual forecasting, and the Delphi method of forecasting. While these approaches vary, each one uses past data in some way to attempt to predict future events (Porter, 2011). For example, casual forecasting attempts to use related data to predict a future event. If a movie has high ticket sales, we can assume that the action figure toys for that movie will sell well. The Delphi method uses the opinions of experts of past events to predict future events. A drawback to this type of forecasting is that it only prepares the participants for events that have already been observed, since the data is based primarily off past data. Using the meteorologist example, if data is coming in that the meteorologist doesn’t have a model to base their forecasting from, they can’t predict the weather. This is usually parodied in movies during a cataclysmic event when the resident expert is asked what is going to happen and turns to the camera and says “I don’t know!” The next time that happens, we now know to scream out in the theater “Traditional forecasting does not take previously unobserved phenomena into account during analysis!!” I strongly recommend not doing this.

Scenario Planning:

Scenario planning is a unique approach to predicting future events and trends. Instead of using past data to attempt to predict future events, scenarios are developed that represent what may happen in the future. Those scenarios are played out to their logical conclusions, and decisions are made based on the results. A good example would be when planning a fire escape plan for a building. The planners can develop scenarios based on where the fire may be. For example, if the fire is near the main escape path from the building, the planners can work through that scenario and determine an alternate path out (Wade, 2012). The strength of this approach is that a scenario can be determined from what could possibly happen, instead of what has happened. Using another Hollywood example, in the movie World War Z a plague sweeps across the world and only Israel is prepared. In the movie, the Israeli government identified this possible scenario early and planned through the logical conclusion. Using traditional forecasting, this would probably not be the results since this outbreak was the first of its kind. A weakness for scenario planning is the opposite of traditional forecasting to where it is not based on past data and can be wildly subjective and miss the mark of what really happens when the scenario plays out. Scenario planning also breaks down in planning for short term or specific planning (Coates, 2016). In the book Foundation by Issac Asimov (1951), humanity builds a massive super computer that can use traditional forecasting to predict the future for several thousand years. Throughout the book, we find that the scenarios built by the supercomputer during a massive war were way off when planning short term actions, and the techs monitoring the system added changes whenever they thought the system was wrong. Although fictional, this is a good example of this flaw with scenario planning.

References

Wade, W. (2012). Scenario Planning: A Field Guide to the Future. John Wiley & Sons. 

Porter, A. (2011). Forecasting and Management of Technology, Second Edition. John Wiley & Sons.

Asimov, I. (1951). Foundation. Gnome Press.

Coates, J. F. (2016). Scenario planning. Technological Forecasting and Social Change, 113, 99-102. 

Innovation by Accident

Not every product we used today was planned out perfectly from the start. It is often the case that products are discovered accidently, or the way they are used commercially have nothing to do with the original intent of the products creator. Famous examples of this are things like the slinky toy, x-rays, and the microwave. Each of these inventions were created during research and engineering efforts to solve other problems. The slinky came into existence when an engineer dropped a large industrial spring, x-ray imaging and the microwave were both invented while researching radio waves. There are other instances where a byproduct or quick fix action ends up becoming a prominent feature of a system. Two technologies that fit this description are Network Address Translation (NAT) and Short Message Service (SMS) text messaging.

   

Network Address Translation

To understand the accidental impact of NAT, we must first briefly review the history of the Internet. The Internet that we use today was first developed as a communications network for the United States military. The Advanced Research Projects Agency (ARPA) built ARPAnet in 1969 as a way to connect military mainframe computers together. The original addressing scheme for ARPAnet was 8 bit and allowed for 256 different host addresses. The original ARPAnet started with 4 hosts and quickly grew to 213 hosts by 1981 (Bort, 1995). Realizing the limitations of the ARPAnet addressing scheme, Robert Khan and Vinton Cerf started working on a new addressing scheme. The fourth iteration of their work produced IPv4 addressing. IP stands for Internet Protocol and v4 is the fourth version they created. IPv4 is a series of 4 8 bit addresses that can support 4,294,967,296 unique addresses (Bort, 1995). This version was a tremendous upgrade from the original 256 possible supported hosts, but due to the rapid expansion of the Internet in the 1990’s, the mismanagement of address allocation, and other technical issues with routing and traffic management, the Internet was facing a real problem or running out of space that needed to be addressed. IPv4 address space was very large and there needed to be a way to route traffic to the appropriate networks around the Internet. The original solution was to build classes into the address space. IPv4 classes are simply a way to identify the size of a network based on the first 8-bit value in the address. The classes were broken up into 4 classes:

·         Class A –

o   First bit value of 0 – 127

o   126 networks with 16,777,214 hosts each

·         Class B –

o   First bit value of 128 – 191

o   16,384 networks with 65,534 hosts each

·         Class C –

o   First bit value of 192 – 223

o   2,097,152 networks with 254 hosts each

·         Class D & E –

o   First bit value of 224 – 255

o   Used for multicasting and R&D

This wasn’t a perfect solution because the amount of addresses per class were not scalable. For example, there are only 126 class A networks that could be given out, and each one had almost 17 million usable hosts. There are over 2 million class C networks that can be given out, but each one only has 254 available hosts. During the 1990’s, many large companies were given class A networks and didn’t use anywhere near the amount of hosts they had available, so those addresses were essentially lost. Meanwhile, smaller companies grew and required more and more class C networks as their demand for hosts increased. The class C networks weren’t given out in sequence, so companies had network addresses that weren’t mathematically close to each other, which increased the difficulty of routing Internet traffic. The problem was quickly becoming unmanageable.  

IPv6 was developed to expand the total address space for the Internet. It could support 340,282,366,920,938,463,463,374,607,431,768,211,456 total addresses (Loshin, 2001)! The problem was that IPv6 wasn’t drafted until 1998 and it was taking much too long to become a standard. A temporary solution needed to be found. NAT was developed to temporarily solve the issue with lack of address space but ended up solving many other issues that it essentially delayed the IPv6 rollout of IPv6 for almost 14 years! NAT is a protocol that runs on a router that borders an internal network and the Internet (Trowbridge, 1997). What NAT does is simply translate IP addresses on the internal network with IP addresses being used on the Internet. The feature that makes NAT so useful is that this translation can be one to many. This means that an organization can host multiple systems internally while only using one address to access the Internet. NAT adds information into the header information of network traffic that is used to assign that traffic an internal and external IP address to use. This way one external IP address can be used to provide Internet connectivity to multiple hosts. NAT inadvertently solved many of the problems with IPv4. Since IPv4 addresses could be reused internally, corporations only needed a few valid IP addresses to provide Internet connectivity to all their hosts. NAT averted the risk of running out of addresses so successfully that IPv6 could be delayed for years with almost no repercussions. This allowed IPv6 development to continue and provided a very robust addressing solution that should allow for sustainable address space for years to come. There were several other factors that dealt with IP addressing that contributed and augmented NAT, such as Classless inter-domain routing, that can be explored to provide a more detailed picture as to how NAT helped change the way the internet worked.

Text Messages

Text messaging has quickly become the standard way of communicating with mobile phones over the last 15 years, and it wasn’t a feature that was planned to be used for commercial use at all. Telephone lines have historically been analog systems, which means that they use waveforms to transmit voice and data instead of digital data such as bits. Early telephone systems handled all aspects of the call using signals that could be sent through the same wires that were used to send the voice waveforms. For example, phones ring by having a telephone switch send a higher than normal amount of electricity to the phone, which used to activate an electro-magnetic bell in the phone and made the phone ring. As telephones and telephony systems became more complex, the signals passed on the wire did as well. The signaling data eventually had to move off the voice transmission lines, and onto separate lines for management, which is called out-of-band signaling. Large phone switches would communicate things like timing, line availability, and other management information over out-of-band signaling. In 1984 Friedhelm Hillerbrand and Bernard Ghillebaert realized that this signaling traffic wasn’t always being used (Kuerbis, van Stolk-Cooke, & Muench, 2017). They developed a way to send ASCI characters along the signaling lines when they were not being used, which let them send text messages from phone switches to end users. The signaling formats that would send the messages could only support messages of 128 characters at a time, and both Hillerbrand and Ghillebaert believed that end users would only be able to acknowledge the message. Global System for Mobile communications (GSM) met in 1985 and started the process of developing the concepts behind Short Message Service (SMS), which is the standard used to send text messages. Since all phone traffic requires signaling data, providers could give text message access to their customers while incurring almost no cost developing or implementing the service. They were simply using a resource they already had in a different way.

SMS allowed for broadcasts to phone, like Hillerbrand and Ghillebaert first envisioned, but also point to point messaging between phones. SMS messaging was first commercialized by Nokia in 1994 and gained popularity with the advent of smart phones like the iPhone. In 1999 text messaging between networks became possible and SMS messaging dramatically increased. The average mobile phone user sent about 35 text messages a month in 2000, by 2010 200,000 text messages were being sent every minute, and over 6.1 trillion texts were sent that year(Steeh, Buskirk, & Callegaro, 2007)! Text messaging packages with cellphones started as an expensive perk and are now a necessity for any phone plan. Text messaging remains one of the largest examples of companies charging customers premium prices for a service that cost them almost nothing to implement. It has also become the de facto way to communicate today and it was never intended for that use!

References:

Bort, J. (1995). The address mess. InfoWorld, 17(46), 75.

Kuerbis, A., van Stolk-Cooke, K., & Muench, F. (2017). An exploratory study of mobile messaging preferences by age: Middle-aged and older adults compared to younger adults. Journal of Rehabilitation and Assistive Technologies Engineering, 4, 2055668317733257. doi:10.1177/2055668317733257

Loshin, P. (2001). Network address translation. Computerworld, 35(8), 60.

Steeh, C., Buskirk, T. D., & Callegaro, M. (2007). Using Text Messages in U.S. Mobile Phone Surveys. Field Methods, 19(1), 59-75. doi:10.1177/1525822x06292852

Trowbridge, D. (1997). A natty solution to a knotty problem. Computer Technology Review, 17(2), 1-1,6+.

Decision Making Techniques

Futuring and Innovation

CS875-1803C-01

Benjamin Arnold

Professor:  Rhonda Johnson

Most projects involve group decision making at some level. Most of the time, this is handled in an informal way through meetings or email correspondence. While an informal approach may work for some groups, others groups may benefit from a more structured approach to group decision making. The Delphi technique is a group decision-making method named after the Oracle of Delphi who was a mythical Greek fortune teller. The Delphi technique was developed in 1959 by Olaf Helmer, Norman Dalkey, and Nicholas Rescher, for the RAND Corporation. The Delphi technique uses anonymous input and structured flow of information between participants to protect against things like personal bias or a bandwagon effect of a specific idea to supplant other valid information (Helmer-Hirschberg, 1967). Experts in a given field are provided questionnaires that are designed to capture the expert’s information and opinions about an issue. The opinions are converted into an approach to address the issue. That approach is refined with continuous, anonymous feedback as the process continues.

Another group decision-making technique is Forced Ranking, which is also known as the Kepner-Tregoe Decision Matrix. Forced Ranking is a decision making technique that uses a decision matrix to force a ranking among possible alternative solutions. In this technique, several possible solutions for an issue are identified. Those solutions are then scored using a weighted value that is determined using a decision matrix. The decision matrix lists various criteria that can be used to determine important factors that are addressed by each of the proposed solutions. The factors are ranked in importance be being given a weight. Then each solution is given a rating as to how well it addresses each of the issue criteria. The weighted rating is the weight value times the rating value for each criterion (Welch, 2002). All weighted ratings are tallied and the solution with the highest culminate weighted score is determined to be the best solution for the issue. This technique also attempts to eliminate bias and possible bandwagon effects by separating the participants from the solution. The weighted ranking systems add objective metrics to possibly subjective criteria (Bens, 2005). Also, having the participants break downgrade different aspects of a solution instead of the solution as a whole provides a method where the solutions are looked at objectively and with more rigor by each participant.

A third approach is the OODA loop. OODA stands for observe, orient, decide, and act. It is a decision cycle developed by Col John Boyd for the United States Air Force to think about conflict situations. Boyd believed that since reality was always changing, any model of that reality would also have to constantly change. This model is like the other two examples in that it attempts to bring order to unknown variables. The first part of the OODA loop is to observe these changes. The concept is to be in a constant state of readiness to adapt to changes. The second stage is orientation, which is perhaps the most critical step in the process. Orientation means bringing the observations to bear and processing that information efficiently to prepare to decide (Enck, 2012). Boyd suggested that having a robust background of several disciplines would be an advantage at this stage. In a group setting, this is where the group would call on the individual expertise represented in the group to successfully process the information that was observed. The last two stages are relatively straightforward. Decide on a course of action and then act on it. Three key takeaway from these stages is to commit to the decision, complete the action, and ensure that any feedback information is retained when restarting the loop. A unique factor about implementing an OODA Loop is that it is ineffective if not used constantly. A team cannot start up an OODA Loop process for a single situation and then stop after the first action. The OODA Loop process works best when it is more a constant state the team is in and is always being practiced. After every action, observation continues.

References

Helmer-Hirschberg, Olaf,Analysis of the Future: The Delphi Method, Santa Monica, Calif.: RAND

Corporation, P-3558, 1967. As of July 18, 2018:https://www.rand.org/pubs/papers/P3558.html

David A. Welch, Decisions, Decisions - The art of effective decision making. Prometheus Books, 2002 (ISBN 1-57392-934-4)

Ingrid Bens, Facilitating with Ease - Core skills for facilitators, team leaders and members, managers,

 consultants and trainers. Jossey-Bass, 2005 (ISBN 0-7879-7729-2)

Enck, R. E. (2012). The OODA Loop. Home Health Care Management & Practice, 24(3), 123-124.

doi:10.1177/1084822312439314

Code Literacy - Horizon Report for Higher Education

The 2017 Horizon Report for Higher Education addresses the subject of Coding as a Literacy as a key short-term trend in K-12 education. Short term trends are described as technology adoptions that are trending within a one to the two-year time frame. The report states that computer science is currently one of the fastest growing industries and that coding literacy is quickly becoming a critical skill across many different career fields, to include many non-technical fields. The report states that many education programs around the world are including basic coding in their curriculum (Freeman, Adams, Cummins, 2017). I found this article interesting because I regularly volunteer for several programs in and around Texas that provide coding camps for children. One program called Youth Code Jam focuses on teaching computer science concepts to children with mild learning disabilities like autism but is also open to anyone who wishes to attend. As a parent of two school-aged children and as a regular volunteer, I do not believe that our public education system is focusing enough effort on teaching code to children. I believe this is due to several forces that negatively impact adopting coding literacy as a priority in the school curriculum. Teachers need to be educated in coding themselves, the cost of resources to teach coding needs to be addressed, and the availability of resources to teach coding is also a challenge.

I believe that there are current technologies that can successfully address each of these issues. To start with the issue of cost, we often use Raspberry Pi mini-computers as platforms for many of our Youth Code Jam events. Raspberry Pi’s are small single board computers with video and audio output, USB and network interfaces, and Wi-Fi capability. The computers are very inexpensive and make great platforms to teach coding literacy on. There are many different projects and applications that students can attempt and learn from. In my daughter’s public school, I am trying to start a program where each student is given a Raspberry Pi as a personal coding platform. The Pi is small enough that they can carry it in their backpack and take it to classes with them. Each class then just needs monitors, keyboards, and Wi-Fi access for the students to use the Pi to access the internet and learn to code. This leads to the second solution of availability. There are several amazing resources online that teach coding at a beginner level. The Horizon report has a link to Common Sense Education that lists the 29 highest rated online resources for learning code. Sites like Code.org and Code Academy provide lessons in many popular programming languages where students can work through lessons at their own pace and be graded on their progress (Code, 2017). These online resources address the last issue of teacher education. I believe that allowing teachers to use these online resources as part of a formal curriculum would alleviate some of the burden of them becoming proficient at coding before they can properly educate their students (Learn, 2017). With online resources like Code Academy, the teachers can learn alongside their students and can rely on the lessons that have already been created for the site instead of having to build lesson plans for a subject that they are still learning themselves.

https://upload.wikimedia.org/wikipedia/commons/thumb/9/97/Raspberry_Pi_3_B%2B_%2839906369025%29.png/300px-Raspberry_Pi_3_B%2B_%2839906369025%29.png

 

References:

Freeman, A., Adams Becker, S., Cummins, M., Davis, A., and Hall Giesinger, C. (2017). NMC/CoSN

Horizon Report: 2017 K–12 Edition. Austin, Texas: The New Media Consortium.

Code.org: Anybody can Learn. (2017). Retrieved July 18, 2018, from http://code.org

Learn to code - for free. (2017). Retrieved July 18, 2018, from http://codeacademy.com

First Post

Hi, this is the first post for my blog for the class Futuring and Innovation (CS875-1803C-01) at Colorado Technical University.  I'm working towards getting a Doctor of Computer Science degree and I'm currently in the third quarter of my first year. I'm excited for the challenges that this course and this degree have to offer!

Throughout my career, I have tried to be in a state of constant improvement and learning. I believe that it is an essential part of being a computer scientist to always have the attitude of a student. With technology changing daily, it is imperative that computer scientists stay up to date with current innovations, so they can apply them in their work.

I started my career as a Russian linguist for the US Air Force. Once I realized that I truly did not want to do that job for a living, I transferred to be a communications specialist, which lead me to being a Systems Administrator at the headquarters for the Air Intelligence Agency. This job allowed me to find a rather small and select office to work in, that was working with offensive and defensive cyber weapons. In 2005 I completed my enlistment and was hired on as a contractor with Northrop Grumman to work in the same office. I worked my way up the ranks and was recruited by MITRE to be lead systems engineer for offensive cyber development operations. MITRE provides full education benefits for their employees. I have already received my bachelor’s in computer science and my master’s in information assurance and network defense. I signed up for my doctorate a week after I was hired by MITRE!

I'm very excited to complete this degree. A doctorate in computer science will allow me to move up within my company, and with my government sponsors. I was really excited to start Futuring and Innovation and I believe this will be one of my favorite classes in my degree. I would like to focus this blog on interesting information that I find that could be used to further my understanding of my research topic. I'm currently working with various DoD organizations to implement agile development methodologies into cyber weapon development. Most government regulations are built around a traditional waterfall development approach, and I am working to change that. The challenge is to find a way to incorporate stringent government oversight into a development method that was designed to remove unnecessary oversight, review, and documentation from the development process. So far it has been an uphill battle, but I am making progress. At this time, I've published two papers on this subject and I've developed a strategy that has been accepted for use on one of our programs. With luck, I can use the information I learn in this class to help with my work and use the lessons I'm learning at work to help guide some of the research I conduct for this class!

I'm also an avid tinkerer, and I will always jump on an opportunity to automate something! I have a house full of Raspberry Pi's that open my garage doors and turn on my lights! I fly drones whenever I can, and I make terrible Python code in rube goldberg-esque attempts to solve simple problems with Arduino boards! I'm excited at the possibility of finding some like-minded students that will geek out with me! Thanks for reading!

~ Ben