Education Systemics - Causal Loop Mapping

Published Jan 23, 2020

This investigation answers the question: How does the system behave?

The actor map helped with the development of a causal loop diagram: a map of the phenomena of education reform that illustrates how the relationship between these actors might lead to difficulty (or success) in reform movements.

This map is centred on the provision of innovation education in the province. Each stakeholder of the actor map was used to generate phenomena that could impact the delivery of innovation education in Newfoundland and Labrador. How these phenomena related to and influenced one another was then mapped, and salient feedback loops and systemic archetypes (patterns of system behaviour observed in different systems with similar consequences and potential solutions) were identified.

Explore the causal loop diagram

A total of 31 elements are linked together with 50 connections depicting the phenomena contributing to innovation education in the public education systems of Newfoundland and Labrador in the map linked above.

Immediately, several feedback loops are obvious. The Low Definition (R1) loop–arguably single-handedly responsible for the context of the present research–is a reinforcing cycle driving the current lack of emphasis on innovation skills. Low innovation capacity means that we are collectively lacking in our understanding of innovation, which leads to a lack of understanding of innovation skills and competencies and therefore a lack of emphasis on educating those skills and competencies, leading to poor innovation capacity. As this is a reinforcing cycle, however, improving innovation education may reverse the direction of this trend.

A reinforcing loop linked to the Low Definition loop is We Teach What We Know (R2), in which a lack of innovation education leads to a lack of innovation educators. This then doubles back, limiting our capacity for innovation education. As above, introducing more innovation education into the system will impede the force of this cycle. 

A third reinforcing loop is The New Economy (R3), which links Newfoundland and Labrador’s natural resource-dependent economy to the recognition of our limited innovation capacity. When oil prices drop, the provincial economy experiences economic shock, and the public becomes more aware of the degree to which the economy is tied to resource exports. This increases the public’s perception of our innovation gap, spurring a search for solutions and thus a recognition that we can do more innovation education. Over time, this will result in reform for innovation education, improving our innovation capacity and reducing our dependency on resource exports. 

Innovation-driven Growth (R4) is the fourth and final reinforcing loop of note. As with The New Economy, this loop explains how growth in innovation skills can lead to new innovation opportunities. As innovation capacity improves, it is likely that work in knowledge economy jobs will proliferate. This leads to a growing need for innovation skills, increasing pressure to produce more innovators, which leads to increased innovation capacity.

One balancing loop is worth highlighting. The Limited Resources (B1) loop shows how our current natural resource-driven economy limits our ability to become an innovation-driven economy. When economic shock hits because the price of oil drops, public spending–the main source of education funding–becomes limited by austerity. This reduces the capacity for our educational institutions to spend time and resources on innovation education reform. In turn, our capacity to provide innovation education is limited, and so is our innovation capacity – leaving us continually linked to oil exports.

A systemic archetype – a pattern of behaviour that appears across systems, with similar consequences and similar solutions – also appears. R&D, Not Innovation is a balancing loop nested within a reinforcing loop, an example of the Fixes that Fail archetype. The perception of an innovation gap sparks a search for solutions. One of those solutions is to turn to the classical notion of innovation as R&D, and to increase R&D spending and activity in order to improve innovation capacity. This alleviates the perceived innovation gap, but does not produce meaningful gains in innovation itself. Meanwhile, the conflation of R&D and innovation takes us further away from a concrete definition of innovation, leaving us without a solid understanding of innovation skills and competencies and thus an under-emphasis of the provision of innovation education.  Andrei Sulzenko discusses this tendency for policymakers to treat R&D as the solution, and suggests searching for other solutions to Canada’s innovation gap instead.

Centrality analysis can also reveal insights in causal loop maps.

Reach efficiency metrics, for instance, reveal several low-hanging fruit for changemakers in innovation education.

One such element is innovation learning from outside of the public education system, circumventing the messiness of trying to change the public system by injecting innovation education directly into learners through other programs and services. This is easily done–there is little to stop someone from offering programs to those who want to take them–and could prompt public institutions to recognize the market and develop their own competing solutions for it.

Another high reach efficiency element is the lack of emphasis on innovation skills and competencies. Increasing the public’s awareness that innovation is something that can be learned may inspire other actors to recognize the deficiency of innovation education in our system, causing change.

The third-most reach-efficient phenomena is a low price of oil–only appearing high on this list because the real complexity of influences impacting this phenomena is not included in this map. Still, it points to the significant impact an oil-driven economic boom can have on innovation education if its profits are spent on the right things.

Eigenvector centrality (how well-connected an element is to other well-connected elements; an element’s overall influence within the system) does not highlight many elements of utility here. The results are intuitive: innovation education, innovation capacity, and the perceived innovation gap rank the highest on this metric, but these elements are difficult to impact (hence why they are the focus of the present research). Torque centrality may give us more information.

Elements with high torque should be relatively easy to impact (as they are not densely influenced by other phenomena in the map), but will impact the rest of the map substantially. These are key leverage points of change.

Torque analysis highlights several key elements. First on the list is the low price of oil, which I will dismiss as it is not actually easy to influence (as described above). The foremost leverage points in the system, then, are other calls for reform and accessible and practical models for innovation education – both with identical torque. 

Other calls for reform is actually a reverse-leverage point: it points toward the many forces trying to influence the education system at any given point. Examples might include the growth of both entrepreneurial programs and social entrepreneurship on Canadian campuses of late. These forces compete, making it difficult for decision-makers to prioritize any one reform movement. This means there is a need for “co-opetition” (co-operative competition): partnership between actors looking to change the system in order to link or at least weave together their efforts such that they do not undermine each other. The high level of torque this element has reveals its forceful potential to undermine reform efforts.

Accessible and practical models for innovation education, if accepted by reformers, would eliminate confusion about innovation and its subsidiary skills and competencies, making it easy for educators to adopt and build innovation education into their curricula. The high torque value of this element certainly validates the present research, although I caution that this could be a case of “falling in love with the solution”. It is certainly suspicious that research resulting in a model of innovation education would suggest that creating a model of innovation education is one of the most powerful forces available to change the system!

Ranked fourth for torque is generational shifts in work, a phenomena pointing to the need for futures analysis in this problem space. This element captures the notion that some economic changes will take place only as the previous generation exits the workforce. This particularly influences educators through a form of bias: current educators teach what they know, as the system worked for them, failing to recognize that the conditions of the world have shifted since they were educated and new approaches are necessary for the 21st century. 

Finally, betweenness centrality helps to reveal potential bottlenecks in systemic change. Innovation capacity and innovation education rank high on the list, again potentially because these phenomena are central to the system I have mapped. This is not necessarily meaningless, however: it is also an indication that many influences hold these components in place, and they will likely change slowly as a result.

Three other high-betweenness elements are the recognition of innovation skill deficiency, the perceived innovation gap, and the search for solutions to the innovation gap. As suggested immediately above, their high levels of betweenness show that these elements are influenced by many forces. As such, they are bottlenecks or single points of failure in systems change. There is an intuitive to logic to this. If the system does not perceive an innovation gap, for instance, it is not likely to engage in any change effort, regardless of what else is happening in the system. The same is true for our recognition of a skill deficiency in innovation and for a single-minded search for solutions (e.g., R&D spending is the only answer). Thus, changemakers looking to improve the system’s innovation education must monitor these phenomena closely, making sure to mitigate these forces’ influence on any reform effort.

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