Swarming the Classroom

Guest post by Dr. Jamie Schwandt

American poet and etymologist John Ciardi once remarked, “The classroom should be an entrance into the world.” In contrast, we are not providing the most optimal entrance. Derek and Laura Cabrera from Cornell University inform us that we should strive to create “Knowledge-able” students not “Information-full” students. Contemporary classrooms (from elementary school to graduate school) are structured similar to a manufacturing line and typically produce students who are full of information. However, in order to produce knowledge, we must introduce “thinking” back into the classroom. Once we do this, we will start creating students with real knowledge as “Knowledge = Information X Thinking”.[1]

One such way educators are trying to introduce “thinking” back into the classroom is by “flipping the classroom”. A flipped classroom is a teaching pedagogy which reverses old classroom teaching and homework through a form of blended learning using modern technology and practical application.[2] While a flipped classroom is an excellent approach to education, I feel as though we need to take it a step further and allow the classroom to flip itself. We should allow a classroom to emerge on its own, or as Sun Tzu famously said, “There are only five musical notes, but combinations of these five notes produce more melodies than we could possibly listen to.”

Thus, I plan to introduce a new concept called “Swarming the Classroom.” Our classroom should become a complex adaptive system (CAS) with no set leader. Instead, it should use simple rules to guide it. Using my own classroom as an example, I plan to demonstrate how I am “Swarming the Classroom” using the following simple rules:

Rule #1. Students interact locally with each other in a decentralized environment.
Rule #2. Students analyze and synthesize concepts and share mental models increasing the collective knowledge of the group.
Rule #3. Students react and adapt to changes without asking for permission by forming systems with immediate Action-Feedback-Change (AFC) Loops.

Biomimicry – Emulating Life’s Genius

Nature is able to respond to its own needs and find solutions that work. Expert in the field of biomimicry, Janine Benyus informs us, “Nature is imaginative by necessity and has already solved many of the problems we are grappling with today.” From buildings designed like termite mounds to self-healing buildings developed using bacteria in concrete; nature has found a way to solve complex problems. I am convinced that we must turn to the field of biomimicry and mimic the form and/or function of nature to solve complex problems. Biomimicry (also known as biomimetics) was coined by Otto Schmidt in the 1950s and describes the transfer of ideas and analogues from biology to technology.[3] Biomimicry can completely transform the way we operate, conduct business, and even communicate. It can be applied on three levels.

Form. For example, mimicking the structure of a hive mind.
Function. Such as mimicking how social insects amplify their collective intelligence through swarming.
Ecosystem. Using the Slime Mold as an example. Dr. Louis Rosenberg (founder of Unanimous AI) informs us a slime mold is able to navigate mazes and finds the shortest and most optimal route between two points of food.[4]

Swarm Intelligence

Furthermore, let’s examine how we can apply biomimicry to the classroom through swarm intelligence. In brief, a “swarm” is a collection of agents interacting locally (decentralized and self-organized) with one another and with their environment.[5] Swarm intelligence is an attempt to design algorithms or distributed problem-solving devices intended to mimic the collective behavior of social insect colonies.[6] Thus, swarm intelligence improves our behaviors (our outputs). Professors at Cornell University, Derek and Laura Cabrera compares this to a game of chess,[7]

The game of chess has rules simple enough for a child to master, yet there are 318 billion possible ways to play the first four moves. The behaviors (or outputs) of systems – be they a flock of starlings or biodiversity writ large, chess matches or organizations – are emergent properties of simple rules at the local level. By identifying, understanding, and applying these simple rules, we can make the outputs better.

The simple rules outlined by the Cabrera’s allow social insects (such as ants) to become a superorganism. These simple rules are as follows:[8]

Look for food. Ants randomly forage for food.
If you find food, shoot pheromones. A few find food and communicate by leaving a pheromone trail increasing probability of collective action on food piles.
Never cross a pheromone trail. Self-organizing behavior around simple rules produces collective intelligence.

Ant Colony Optimization

Ant colony optimization (ACO) algorithms are probabilistic techniques for solving computational problems.[9] ACO is a swarm intelligence method using metaheuristic optimizations (higher-level procedure or heuristic designed to find, generate, or select a heuristic offering a solution to an optimization problem).[10] Applications of ACO can be found in the following: traveling salesman problem, shortest route, congestion, and flexibility. Let’s briefly examine the traveling salesman problem (TSP). TSP asks the following question: “Given a list of cities and the distances between each pair of cities, what is the shortest possible route that visits each city and returns to the origin city?” ACO was initially aimed at solving this problem, where the goal was to find the shortest round-trip to link a series of cities (based on a set of ants, each ant making one of the possible round trips).[11] The initial ACO algorithm was developed according to the following rules (see Figure 1).[12]

Figure 1. ACO algorithm rules to follow[13]

Must visit each city exactly once.
Distant city has less chance of being chosen.
The more intense the pheromone trail laid out on an edge between two cities, the greater the probability that the edge will be chosen.
Having completed its journey, the ant deposits more pheromones on all edges it traversed, if the journey is short.

Computational Glue – Decentralized Environment

Figure 2. Dr. Schwandt’s Tests and Measurements Course via Loomio.org

Rule #1. Students interact locally with each other in a decentralized environment.

An ant colony is an example of a superorganism. We can mimic the simple rules of an ant colony and turn our classroom into a superorganism. When it comes to what I call “Swarming the Classroom,” we should follow the advice of the experts,

Superorganisms transform self-interested, autonomous agents into a powerful, adaptive, self-sustaining, and intelligent complex adaptive system (CAS). Rather than requiring the constant intervention of leaders, individual agents in superorganism self-organize by following simple rules that guide their actions in variable contexts. You want your organization to be like a superorganism. You want it to adapt quickly to the changing environment, be resilient when times are tough, and be dynamic and alive.[14]

Moreover, we can use a social network mechanism of indirect coordination called stigmergy. Stigmergy produces complex intelligent structures without the need for control or direct communication between agents.[15] To produces this type of interaction in my online classroom environment, I am using a free decision-making software called Loomio (www.loomio.org). I use this for a course I teach as an adjunct professor for an online statistics course at Fort Hays State University (FHSU) in Hays, Kansas. Using Loomio (see Figure 2) allows me to establish simple rules for my class and then simply step back and allow the collective intelligence of the group emerge. I am able to do this within Loomio by establishing three simple rules:

Rule #1. Use the discussion board thread to start your analysis and synthesis of a concept.
Rule #2. Use Loomio as a launching point for the creation of your mapping diagrams and build on the diagrams of others.
Rule #3. Use decision tools to propose or collaborate on a decision (such as a change to the syllabus).

Loomio serves as our computational glue allowing the class to work together in a swarm; where we collaborate, build on ideas, and make decisions together.[16] Students are able to quickly see and react to discussion threads without having to click on numerous links. My wife (also an adjunct professor) proposed an intriguing concept to use within Loomio. She gave me the idea to have students vote or propose changes to the class syllabus. In essence, I am creating a complex adaptive syllabus. This allows me the flexibility to make adaptive changes in order to optimize the syllabus. Lastly, I can initiate a poll for students to vote on what is working and not working. My students can also initiate a poll allowing me to receive immediate feedback instead of waiting for end of course evaluations.

Systems Thinking Version 2.0

Figure 3. Tests and Measurements systems diagram via Plectica.com

Rule #2. Students analyze and synthesize concepts and share mental models increasing the collective knowledge of the group.

There are four patterns that underlie all cognition. DSRP (Distinctions, Systems, Relationships, and Perspectives) are the universal pattern to the process of structing information.[17] Systems theorist Derek Cabrera developed the DSRP theory (Systems Thinking Version 2.0) and method of thinking. Cabrera informs us that DSRP consists of four interrelated patterns and each possess two opposing elements (see Table 1).[18]

DSRP (Systems Thinking Version 2.0)
Distinction (making distinctions)	Consist of identify and other
Systems (organizing systems)	Consist of part and whole
Relationships (recognizing relationships)	Consist of action and reaction
Perspectives (taking perspectives)	Consist of point and view

Table 1. DSRP Theory[19]

Cabrera offers two free platforms I use for my class: Thinkquiry and Plectica. Thinkquiry (www.thinkquiry.us) is a term used for thinking differently about how we ask questions. It moves us beyond the Socratic Method and penetrates deeper into a concept and asks deeper questions.[20] My students use these guiding questions to start breaking apart and rebuilding a concept (see Figure 4).

Figure 4. DSRP guiding questions via Thinkquiry.us

In order to break apart and rebuild concepts, my students use these guiding questions to build systems diagrams/maps via www.plectica.com (see Figure 3). Plectica replicates how our brain works by visually organizing parts that can be combined and connected to each other to form a more complete picture.[21] My students create and share these maps to think (cognition) about their thinking (metacognition).

Moreover, my students not only build their own maps, they also create collective maps using simple rules. For example, as the instructor, I initiate a discussion thread on Loomio providing a link to the collective map. I then provide simple rules within the map itself. I do not control or dictate any specific part of the map outside of the simple rules. I simply start the map and allow a collective intelligence to emerge. Let’s examine the simple rules I use for the collective map.

Rule #1. Add something to the map (i.e. a Distinction, parts of a System, Relationships, and/or Perspectives).
Rule #2. Analyze and synthesize the concept.
Rule #3. Place your name in the summary of each addition and tag your additions within the map.

That’s it. Outside of these three simple rules, the rest is up to them. It’s amazing to see what emerges from this. If we examine these rules even closer, you start to notice the similarities to a superorganism. Think of the simple rules for an ant colony discussed earlier or the simple rules for a flock of birds. My class is really no different than the rules for a flock of birds.[22]

Rule #1. Maintain a constant distance from nearest neighbors.
Rule #2. Adjust direction based on nearest neighbors.
Rule #3. Avoid predators.

Emergent Intelligence – Brain of Brains

Figure 5. Action – Feedback – Change (AFC) Loop

Rule #3. Students react and adapt to changes without asking for permission by forming systems with immediate Action-Feedback-Change (AFC) Loops.

As humans, the most optimal way to improve is to intuitively act (based on our perception of reality or our mental model), recognize that we are constantly receiving feedback from reality when our mental model crash into reality, and then to change by forming a new mental model. This is a description of the continuous Action-Feedback-Change (AFC) Loop (see Figure 5) I have developed to help myself understand how we improve and adapt.

Furthermore, as individuals we are smart, yet as a group we are even smarter. When we form groups, we amplify our intelligence. Let’s briefly examine how Dr. Louis Rosenberg compares individuals to systems (see Figure 6),[23]

“A brain is a system of neurons so deeply interconnected that an intelligence forms. A swarm is a system of brains so deeply interconnected that a superintelligence forms. Simply put, a swarm is a brain of brains and it can be smarter than any individual member.”

Figure 6. Brain and Swarm via UNU

Dr. Rosenberg’s comparison is fascinating. What’s even more amazing is how we can use this comparison for a classroom environment (see Table 2).

Rosenberg	Schwandt
Individuals are complex systems of neurons.	Students are complex systems of neurons.
We can amplify our intelligence by forming systems with immediate feedback loops.	We can amplify the collective intelligence of the class by forming systems with Action-Feedback-Change (AFC) Loops.
A brain of brain combines the knowledge, wisdom, and insight of the group to converge on a unified solution.	A classroom combines the knowledge, wisdom, and insight of the group to introduce “Thinking” back into the classroom.

Table 2. Rosenberg – Schwandt “Brain of Brains” classroom comparison

Another simple rule I use for my students allows them to freely move through a learning module without limiting them. I allow my class to determine, as a group, when to complete classroom work (think back to my discussion of the complex adaptive syllabus). Essentially, they have the entire semester to complete each assignment (from the first learning module to the last). I found that by removing strict timelines, students complete nearly all the assignments with this approach when compared to the traditional approach. Additionally, the quality of their work is better. Furthermore, each learning module lines up with Thinkquiry and Plectica; thus, each student must complete the module to understand how to deconstruct each concept.

By allowing each student to complete each learning module without my permission, I found the entire group produces a higher collective intelligence. Students who move ahead faster are able to increase the intelligence of the slower moving students. They are operating like that of a river. Let’s briefly examine the perfect algorithm for this metaphor – the River Formation Dynamics Algorithm (RFD).

Metaphor. RFD is inspired by how water forms rivers through erosion of the ground and depositing sediments allowing good solutions to emerge. A set of drops placed at the starting point is subjected to a gravitational force attracting them to the center. As a result, these droplets are distributed throughout their environment seeking the lowest point or the sea. This allows riverbeds to form.

Classroom Application. By providing open access to all learning modules and through simple guiding rules via Loomio, I have provided a starting point for my students. Essentially, I have created a set of droplets allowing my students to explore the education environment. Students are able to interact and build on the collective group (without actively engaging anyone individually) leading to an intelligent group. The initial droplets lead to an overall better course, which in turn optimizes the course for the next set of students (i.e. the complex adaptive syllabus).

Finally, swarm intelligence mimics superorganisms (such as a flock of birds, and allows a vast number of independent organisms to act in unison. Using swarming methods inside the classroom is a way to completely reshape our educational model. Essentially, it takes flipping the classroom to an entirely new level. It’s like jumping off a diving board and continuously flipping until you hit the water. Swarming the Classroom puts “Thinking” back into the equation. In the end, it’s like creating a Wikipedia page. As the instructor, I start the page. By using simple rules, I then allow the class to create the page and continue to improve it as a collective group. Thus, allowing “Thinking” to emerge increasing the collective intelligence of the class.

AUTHOR BIO: Dr. Jamie Schwandt is an American author, statistics professor, TV show host, motivational speaker, lean six sigma master black belt, and a major in the United States Army. Schwandt is a former foster child and the host of Dreaming Big with Dr. Schwandt. His education includes a Bachelor of Science and Master of Science from Fort Hays State University, where he was selected as the 2016 Young Alumni of the Year. In May 2013 he completed a Doctor of Education (Ed.D.) from Kansas State University. He is best known as the author of the books Succeeding as a Foster Child a Workbook (2017), Finding Your Hero (2015), and Succeeding as a Foster Child (2014).

REFERENCES:

[1] Derek Cabrera and Laura Colosi, Thinking at Every Desk: Four Simple Skills to Transform Your Classroom (New York: W.W. Norton & Company, 2012).

[2] Balaji Alagurajan, “Flipping the Classroom in ELT Context,” International Journal of Scientific Research and Review (January 2018).

[3] Julian F.V. Vincent et al., “Biomimetics: its practice and theory,” Journal of the Royal Society 3, no. 9 (August 2006).

[4] Louis Rosenberg, “Human Swarming and the future of Collective Intelligence,” July 20, 2015, https://www.singularityweblog.com/human-swarming-and-the-future-of-collective-intelligence (accessed February 22, 2018).

[5] Eric Bonabeau, Marco Dorigo, and Guy Theraulaz, “Swarm Intelligence: From Natural to Artificial Systems,” Santa Fe Institute Studies in the Sciences of Complexity (1999).

[6] Ibid.

[7] Derek Cabrera and Laura Cabrera, Flock Not Clock: Align People, Processes, and Systems to Achieve Your Vision (New York: Plectica Publishing, 2018).

[8] Ibid.

[9] Wikipedia, “Ant colony optimization algorithms,” https://en.wikipedia.org/wiki/Ant_colony_optimization_algorithms (accessed on February 22, 2018).

[10] Ibid.

[11] Ibid.

[12] Ibid.

[13] Ibid.

[14] Derek Cabrera and Laura Cabrera, Flock Not Clock: Align People, Processes, and Systems to Achieve Your Vision (New York: Plectica Publishing, 2018).

[15] Wikipedia, “Stigmergy,” https://en.wikipedia.org/wiki/Stigmergy (accessed on February 22, 2018).

[16] Louis Rosenberg, “Human Swarming and the future of Collective Intelligence,” July 20, 2015, https://www.singularityweblog.com/human-swarming-and-the-future-of-collective-intelligence (accessed February 22, 2018).

[17] Derek Cabrera and Laura Cabrera, Systems Thinking Made Simple: New Hope for Solving Wicked Problems (New York: Odyssean Press, 2015).

[18] Ibid.

[19] Ibid.

[20] Thinkquiry, http://www.thinkquiry.us (accessed February 20, 2018).

[21] Cabrera Research Lab, https://www.crlab.us (accessed February 20, 2018).

[22] Derek Cabrera and Laura Cabrera, Systems Thinking Made Simple: New Hope for Solving Wicked Problems (New York: Odyssean Press, 2015).

[23] Louis Rosenberg, “Human Swarming IEEE Blended Intelligence,” 2015, https://www.youtube.com/watch?v=2yAkFTimnqI&t=717s (accessed February 23, 2018).