Catalyzing Collaborative Learning
and Collective Action for Positive
Social Change through Systems
National University of Ireland, Galway, Ireland
National University of Ireland, Galway, Ireland
Arizona State University, USA
[A system is] any portion of the material universe which we choose to separate in
thought from the rest of the universe for the purpose of considering and discussing the
various changes which may occur within it under various conditions.
Willard J. Gibbs
The community stagnates without the impulse of the individual. The impulse dies away
without the sympathy of the community. William James
Resolving complex scientific and social problems is often impeded by three interdependent
human limitations: poor critical thinking skills; no clear methodology to facilitate group
coherence, consensus design, and collective action; and limited computational capacities. Third
level science education is designed to facilitate the development of generic critical thinking
skills, but often does so with only limited success (Kuhn, 2005). Furthermore, third level
science education generally focuses on domain-specific computational skills that do not
necessarily transfer well outside of the domain in which they are normally used, and training in
the use of systems science methodologies that facilitate group coherence, consensus design,
and collective action is rarely observed (Warfield, 1974, 2006). We believe that these
problems can be addressed by integrating within a systems science curriculum three thought
structuring technologies: Interactive Management (IM) for system design, Argument Mapping
(AM) for critical thinking, and Structural Equation and System Dynamics Modeling for
mathematical modeling. Such a curriculum would promote systems thinking and cooperative
enquiry skills in relation to basic and applied science problems, while also facilitating
collective action in the context of a multidisciplinary action research agenda.
Embedding Systems Science in the School and University Curriculum
John Warfield (1925 – 2009), past president of the International Society for the Systems
Sciences, devoted most of his career to the task of building a viable systems science. In his
view, systems science is best seen as a science that consists of five nested sub-sciences, which
can be presented most compactly using the notation of set theory (Warfield, 2006). Let A
represent a science of description. Let B represent a science of design. Let C represent a
science of complexity. Let D represent a science of action (praxiology). Let E represent
systems science. Then
A B C D E (1)
This suggests that we can learn something of systems science by first learning a science of
description (e.g., physics, chemistry, biology, psychology, sociology, economics). Then we can
learn a science of design that includes a science of description. The science of design is
fundamental if our goal is to redesign systems (e.g., the intelligent redesign of school systems
via effective knowledge import from biology, psychology, sociology and economics). The
science of design implies the use of tools that facilitate the building of structural hypotheses in
relation to any given problematic situation, a problematic situation that may call upon the
import of knowledge from any given field of scientific inquiry. Next we can learn a science of
complexity that includes a science of description and a science of design. The science of
complexity is fundamental if our goal is to integrate a large body of knowledge and multiple
disparate functional relations that different stakeholders believe to be relevant to the
problematic situation. Next we can learn a science of action that includes a science of
description, a science of design, and a science of complexity. The science of action is
fundamental if our goal is to catalyze collective action for the purpose of bringing about system
changes that are grounded in the sciences of description, design, and complexity. Broadly
speaking, if students are to learn a form of systems science that can be used to promote
successful collective action in science and society, they need to learn how the domain-based
science of description that is their primary focus of enquiry at University can be integrated in
principle with other domains of enquiry in the context of a broader science of design,
complexity, and action.
Warfield‟s vision for applied systems science is instantiated in part in the systems science
methodology he developed, Interactive Management (IM). IM is a software-assisted thought
and action mapping process that helps groups to develop outcomes that integrate contributions
from individuals with diverse views, backgrounds, and perspectives. Central to IM is a matrix
structuring process that facilitates groups in developing structural hypotheses that map systems
of interdependencies, based on the consensus-based logic of the group (see below). Although
the mathematical algorithms that underpin Warfield‟s IM software are relatively complex --
drawing in particular upon the mathematics of matrices -- the application of the software for the
purpose of generating a structural hypothesis in relation to any given problematic situation is
reasonably straightforward. In fact, the rationale for separating the computational complexity
of structuring from the process of dialogue, information search, deliberation, and voting in a
group was very explicit in Warfield‟s view. The IM software is designed to alleviate the group
of computational burden and thus allow them the opportunity to maximize the processes of
creative idea generation, dialogue (see Wegerif, this volume), information search, critical
thinking and voting in relation to key binary relations in the overall problem structure.
Consistent with Warfield‟s view, we believe that tool-mediated learning processes can be
instrumental in promoting the development of key individual talents and successful team
dynamics. The design of learning tools is critical in this context. Warfield argued that the tools
of systems science will be most effective if they integrate our capacity to share meaning using
words, represent causality using graphics, and model complexity using mathematics (see Figure
1). IM integrates all three of these components in its design. Warfield also highlights the
distinction between the mathematics of content and the mathematics of structure. IM draws
upon the mathematics of structure to convert matrix voting structures of users into a graphical
representation of the relations they have mapped in their problematique. However, in the
context of mapping problem structures or enhancement structures (i.e., problem resolution
structures) that import knowledge from domain-based sciences, it is feasible and perhaps
desirable for the purpose of model fit evaluation to estimate effect sizes for discrete functional
relation in a matrix structure and thus test the empirical validity of models. This can be done by
testing structural models or dynamic models that are analogues or extensions of the models
generated by a group in an IM session. Notably, a recent study by Chang (2010) compared the
results of IM with Structural Equation Modeling (SEM) and found a high degree of consistency
between models generated by participants in an IM session and quantitative relationships
confirmed in SEM. Although detailed mathematical specification is beyond the scope of this
chapter, consistent with Maani and Cavana (2000), we believe that IM modelling can be used
as a foundational step for groups that seek to develop consensus-based computational models in
a team setting.
Figure 1. Systems science tools needs to work with our capacity to share meaning using
words, represent causality using graphics, and model complexity using mathematics.
Methods for integrating systems science tools into the curriculum are still poorly developed.
For example, less well developed in Warfield‟s thinking are: (a) strategies for importing the
facts and relations of disparate descriptive sciences into group design efforts, (b) strategies for
quantifying problematique model fit by weighting and measuring discrete relations in matrix
structures and computing statistical fit indices and further integrating with system dynamics
modeling tools (Maani & Cavana, 2000); (c) teaching the critical thinking skills necessary for
the analysis and evaluation of scientific evidence embedded in problematiques, and (d)
cultivating domain-specific systems level thinking in students at school level prior to their
entering university (Stein, Dawson & Fischer, 2010). In order to advance Warfield‟s vision of
systems science education and further develop applied systems science, we are developing a
tool and a teaching framework that integrates critical thinking and systems modelling in a
broader pedagogical framework. Below we outline a framework for systems science education
that involves the development of tools, talents, and teams. We then describe the IM approach
in more detail and how we have integrated our newly developed IM tool with an argument
mapping tool in an effort to facilitate individual talents and enhance team functioning.
Tools, Teams and Talents
Central to our framework is the development of tools, talents, and teams (see Figure 2).
logic and structure:
eg., formal logic,
graph theory, matrices
Mathematics of content:
e.g., differential equations,
used to describe
phenomena in physics,
Figure 2. Three levels in a framework for systems science education -- Tools, Talents and
Note: IM = Interactive Management, AM = Argument Mapping, SEM = Structural Equation Modelling,
SysD = System Dynamics.
We believe that developments across these three levels are reciprocally reinforcing, in the sense
that good tool design should facilitate the development of key individual talents while also
promoting effective team dynamics , much like efforts to promote effective team dynamics
should accelerate the development of individual talents and the development of tool use skills .
We also believe that systems science education should not be limited to the classroom – talents
and teams develop in a more contextualized manner when systems science education extends to
cooperative action in the context of real-world social problems, whereby students are given the
opportunity to work with community stakeholders on real world problems. In our teaching
framework, teams use their talents and tools to work on specific tasks focused on the resolution
of problems within specific territories. The notion of tasks is consistent with Warfield‟s vision
for applied systems science, which is rooted in the philosophical school of pragmatism
(Warfield, 2006) and is applied and task-oriented in its focus. The notion of territories is used
to reinforce the idea that human problems function within an ecosystem, or territory of
influence, and thus the resolution of these problems involves human action within a specific
territory. Problem description and modelling only serves the purpose to facilitate
understanding and perspective in relation to concrete adaptive action within a territory of
influence (Vennix, 1996). However, as noted above, IM modelling can be used as a
foundational step for groups that seek to develop consensus-based computational models in a
Team orientation; Mutual performance monitoring; Backup
behavior management; Adaptability; Leadership; Mutual
trust, Shared mental models; Closed loop communication
Critical, Systems, Computational
Thinking; Social Intelligence
IM, AM, SEM,
facilitated team setting using either structural equation modelling tools such as Amos
, or System Dynamics tools such as Vensim
Consistent with Warfield‟s vision and the majority of previous applications of IM, our teaching
framework focuses squarely on efforts to resolve complex social problems and our tool-
mediated teaching processes are designed to counteract the three human limitations noted
above that often impede the resolution of complex social problems: poor critical thinking skills,
no clear methodology to facilitate group coherence, consensus design and collective action, and
limited computational capacities. Below we describe the IM process in more detail, how it
advances upon other group decision support systems, and how we have integrated IM with AM
in our teaching method.
Developing Teams and Talents using Interactive Management and Argument Mapping
Over the past three decades, the use of computer-based group decision support systems has
become increasingly prevalent. Such systems are designed to increase efficiency in the context
of group decision making and problem solving. Notably, a variety of decision support tools,
such as Group Explorer and Decision Explorer have been used to help stakeholders to develop
management strategies. Group Explorer, for example, facilitates the rapid declaration by group
members of problems which they feel are negatively impacting on their goals. The tool also
allows groups to map these problems in causal maps, and work together to agree upon priority
goals (Ackermann et al, 2005; Eden & Ackermann, 2001; Williams et al, 1995). The use of tool
allows the rapid generation of ideas because group members work in parallel, not sequentially;
ideas are generated anonymously , which allows for greater participation from inhibited group
members, and the expression of ideas which may be unpopular (Kraemer & King, 1988;
Nunamker et al., 1988). Furthermore, the complex graphical representations of problem issues
are immediately available to the group (Ackermann et al, 2010).
Despite the success and utility of many such decision support systems, they are not without
limitations. For example, resolving complex problems often demands that groups move beyond
the anonymity of ideas. It is vital that ideas can be clarified and integrated in a manner that
enhances group understanding (Broome & Chen, 1992). Furthermore, one potential issue
arising from computer-assisted problem solving is information overload. In a decision support
system whereby simultaneous entry of ideas by all participants in possible, the set of ideas can
quickly become overwhelming for group members, and thereby has a negative effect on
problem solving and decision making processes. Warfield (1990) highlights “The Law of
Requisite Parsimony”, which states that there is a need to control the rate at which information
is presented for processing in order for avoid overload during the design process. In order for
group decision support systems to optimize their potential, and the potential of the group, it is
necessary that they be designed in light of the limitations of human cognition.
IM was developed specifically to overcome many of the limitations described above.
Established as a formal system of design in 1980 after a developmental phase that started in
1974, IM was designed to assist groups in dealing with complex issues. The theoretical
constructs that inform IM, developed over the course of more than 2 decades of practice, draw
from both behavioral and cognitive sciences, with a strong basis in general systems thinking
(see Ackoff, 1981; Argyris, 1982; Cleveland, 1973; Deal & Kennedy, 1982; Kemeny, 1980;
Rittel & Webber, 1974; Simon, 1960). Emphasis is given to balancing behavioral and technical
demands of group work (Broome & Keever, 1989) while honoring design laws concerning
variety, parsimony, and saliency (Ashby, 1958; Boulding, 1966; Miller, 1956).
IM has been applied in a variety of situations, including assisting city councils in making
budget cuts (Coke & Moore, 1981), developing instructional units (Sato, 1979), designing a
national agenda for pediatric nursing (Feeg, 1988), creating computer-based information
systems for organizations (Keever, 1989), improving the U.S. Department of Defense‟s
acquisition process (Alberts, 1992), defining global challenges (Christakis, 1987), improving
Tribal governance process in Native American communities (Broome, 1995), managing
cultural issues in the automotive industry (Staley & Broome, 1993); and promoting
peacebuilding in divided societies (Broome, 2006). Participant testimonials at the end of
workshops, together with changes in organizational and institutional policies that resulted from
IM sessions, provide evidence for the individual learning and system change that is possible
through the IM process.
Typically, IM sessions progress through a series of steps. First, a group of key stakeholders,
usually ranging in size from 8-15 but with the possibility of involving much larger groups,with
an interest in resolving a problematic situation come together in a situation room and are asked
to generate a set of „raw‟ ideas (commonly 50 – 200) about what might potentially have a
bearing on the problem they all agree exists. Group discussion and multi-voting procedures
help the group to clarify the sub-set of ideas that bear upon the most critical problem
issues. Next, using IM software, each of the critical issues are compared systematically in pairs
and the same question is asked of each in turn: “Does A influence B?”. Unless there is majority
agreement that one issue impacts upon another, the relation does not appear in the final
analysis. After all the critical issues have been compared in this way, IM software generates a
problem structure (or problematique) showing how the issues are interrelated. The
problematique can be displayed for discussion by the group. The problematique becomes the
launch pad for planning solutions to problems within the problem field. The logical structure of
problems is visible in the problematique and when generating solutions, action plans are aimed
at resolving problems in a logical and orderly manner. When the group is happy that they have
modeled both the problem field and the best possible set of solutions, the IM session closes and
the group leaves with a detailed action plan, a specific set of goals to work on, and the roadmap
and logic describing how all the various plans and goals of each member will work together to
resolve the original problem. Notably, the IM methodology can be used to structure problems,
objectives, options, competencies, and so on, using a variety of different relational statements
(e.g., aggravates, enhances, promotes, supports, etc.).
More detailed descriptions of IM sessions are provided in Warfield and Cardenas (1994) and
Warfield (2006). We have also made use of the IM methodology in a conference setting
involving upward of 100 participants, specifically, to structure barriers to wellbeing in Ireland
(Hogan & Broome, 2012) and objectives designed to enhance the wellbeing of the people of
Ireland (Hogan & Broome, 2013).
By way of example of an application of IM within an educational context, presented below is
the outcome of a session conducted in a Thinking, Modelling and Writing in Psychology
module in NUI Galway, in response to the trigger question, What are the most important skills
and dispositions of good critical thinkers?. Table 1 illustrates the top ranked skills and
dispositions of good critical thinkers, as voted upon by the students. Students then used the IM
software to structure the interdependencies among the highest ranked skills and dispositions
(Figure 3).The problematique is to be read from left to right, with paths in the model
interpreted as „significantly enhances‟.
Table 1. Top Ranked skills and dispositions for CT
Top Five Skills
Top Five Dispositions
1. The ability to clearly say what it is you
want to say
1. The willingness to detach from one‟s own
2. The ability to evaluate the strengths and
weaknesses of an argument
2. The willingness to recognise limited knowledge
or uncertainty (e.g. we may not have enough
knowledge of a topic to confidently think
critically about it)
3. The ability to converse and engage with
others to expound personal views and
3. The willingness to systematically write an
essay in order to achieve a goal
4. The ability to logically say what you want
to say in a concise manner
4. The willingness to question one‟s own
assumptions and thinking
5. The ability to draw a conclusion about a
topic based on its context and what we
know about the topic already
5. The willingness to listen properly
Figure 3: Sample Enhancement Structure of Skills and Dispositions Required for Critical
Thinking. (Designed by Thinking, Modelling and Writing students as part of an introduction to
CT and IM. Paths in the model are to be interpreted as „significantly enhances‟)
We have recently integrated our IM software with argument mapping (AM) software.
Although Warfield recognized that the critical thinking skills of participants in an IM
session are often limited, he rarely discussed the particulars of these skills and how they
might be developed in parallel with training in the use of IM. In the context of resolving
problems that call upon the knowledge of diverse stakeholders, it is important to
recognize that informed judgments in relation to key system relations imply the ability
to think critically and reflectively in relation to one‟s own knowledge and the
knowledge presented by others (Facione, 1990; Kuhn, 2005)
As defined in The Delphi Report (Facione, 1990), critical thinking involves:
“…purposeful, self-regulatory judgment which results in interpretation, analysis, evaluation,
and inference, as well as explanation of the evidential, conceptual, methodological,
criteriological, or contextual considerations upon which that judgment is based.” (p. 3)
While a variety of training techniques can be used to enhance critical thinking skills, a
recent meta-analysis by Alvarez-Ortiz (2007) suggests that the explicit use of argument
mapping training is one of the most effective methods of training critical thinking skills.
Furthermore, with research studies demonstrating the largest gains in knowledge growth
and critical thinking skills deriving from cooperative enquiry (Johnson & Johnson,
2009), and with computer supported argument mapping tools now widely available and
widely applied in third level education (van Gelder, Bissett, & Cumming, 2004), it is
not difficult to see how the development of critical thinking skills through cooperative
enquiry using argument mapping tools can fit within Warfield‟s vision for systems
science education. Specifically, if one considers each of the binary relations in a larger
structural hypothesis (or problematique) to represent a specific claim, then it is easy to
see how a structural analysis and evaluation of the evidence used to support this claim
can be unpacked through an argument map. Furthermore, with easy access to the Web
of Science and other search engines, it is possible for students working together to
analyse and evaluate a particular claim in a structure, specifically, by sourcing available
knowledge and considering the credibility, relevance, and logical significance of this
knowledge to the relation under investigation. For example, students who participate in
an IM session may agree after open deliberation that (a) the ability to question one‟s
own assumptions and thinking significantly enhances (b) one‟s capacity to evaluate the
strength and weaknesses of an argument. This claim can be evaluated on deeper level in
the context of an unfolded argument map, specifically, by sourcing available knowledge
and considering the credibility, relevance, and logical significance of this knowledge to
the relation under investigation (see Figure 4).
Figure 4: Argument Map Exploring How the Ability to Question One‟s Assumptions and
Thinking Enhances Evaluation Skills.
As it is currently used, IM is a deeply engaging and cooperative process. However, when it is
further merged with cooperative argument mapping (AM) work, the cooperative enquiry
process is transformed into a process that explicitly links the science of design with the
sciences of description in Warfield‟s scheme. More specifically, students who are mapping out
a problematic situation are called upon to source and evaluate scientific evidence to support
their beliefs as to the nature of discrete paths of influence in a problematique. Also, for
problematic situations that draw upon multiple sciences of description, it is evident that
students working in multidisciplinary teams will be exposed to arguments from multiple
scientific domains and will have to learn to analyse and evaluate these arguments in
cooperation with others. While it might be assumed that only those with specialized knowledge
of domain-specific science content will be able to analyse domain-specific arguments and
evidence, we believe that the knowledge and perspectives of students from multiple scientific
backgrounds will help to enhance the creativity and the overall quality of evaluation and
inference work. This can be accomplished through a generic systems science module that is run
in parallel with all other domain-specific science education programmes at university, with
classes consisting of small groups of 12 – 20 students from multiple disciplinary backgrounds
working together on common problems. Within this framework it is possible to cultivate key
talents of critical thinking, systems thinking and computational modelling skills.
Notably, as is the case with Decision Support Systems such as soft systems methodology
(Checkland, 1989) strategic choice (Friend, 2001) and strategic options development and
analysis (Eden & Ackerman, 2001) , effective tool design needs to be coupled with effective
instructional design and effective management of teams in order for individual talents to be
cultivated and coordinated in a group project setting (Eden & Ackermann, 2006). While the
learning sciences have provided a great deal of insight into the cultivation of key talents at the
individual level, including the cultivation of CT skills using argument mapping training
programmes (Dwyer, Hogan, & Stewart, 2013), more work is needed to understand the optimal
conditions needed to coordinate talents in a group project setting. Systems science education as
Warfield envisioned it requires a perspective on the nature of effective teams and how to
sustain effective team performance.
Based on the work of Salas, Sims and Burke (2005), we highlight eight aspects of effective
teams that need to be cultivated as part of a systems science education programme. Salas, Sims
and Burke (2005) suggest that a (1) team orientation, (2) mutual performance monitoring, and
(3) backup behavior management are key competencies of effective team members. Effective
teams also display (4) adaptability or flexibility and (5) effective team leadership (see Table 2).
These „Big 5‟ of effective teams are supported in turn by three coordinating mechanisms: (a)
mutual trust, (b) shared mental models, and (c) closed loop communication.
Table 2. Eight aspects of effective teams that need to be cultivated as part of systems
1. Team Orientation – a preference for working with others and also a tendency to enhance
individual performance through the coordination, evaluation, and utilization of task inputs
from other members.
2. Mutual Performance Monitoring – team members maintain an awareness of team
functioning by monitoring fellow members work in an effort to catch slips/mistakes
3. Backup Behaviour Management – anticipate other team members needs through accurate
knowledge about their responsibilities. Includes ability to shift workload among members to
achieve balance during periods of high workload or pressure
4. Adaptability – adjust strategies based on information gathered from the environment and
through the use of backup behaviour and reallocation of intra-team resources.
5. Team Leadership – ability to direct and coordinate team activities, assess performance,
assign tasks, develop team knowledge, skills, and abilities, motivate team members, plan
and organize, and establish a positive atmosphere
Supporting Coordinating Mechanisms
A. Mutual Trust – The shared belief that team members will perform their roles and protect
the interests of their teammates.
B. Shared Mental Models – An organizing knowledge structure of relationships among task
components the team in engaged in and how the team members will interact
C. Closed-loop communication – The exchange of information between a sender and a receiver
irrespective of the medium. Involves (a) the sender initiating a message, (b) the receiver
receiving the message, interpreting it, and acknowledging its receipt, and (c) the sender
following up to insure the intended message was received
Both process and outcomes analyses suggest that the IM methodology facilitates effective team
dynamics in the context of appropriate feedback and facilitation (Harney, Hogan and Broome,
2012, 2014). For example, Harney, Hogan & Broome (2012) investigated the effect of open
versus closed IM voting and dispositional trust on perceived consensus, objective consensus
and perceived efficacy of IM technology. Two groups of 15 undergraduate students came
together to structure the interdependencies between positive and negative aspects of social
media. Participants high and low on dispositional trust were identified and were randomly
assigned to either an open or closed voting condition. Those in the closed voting group were
not permitted to discuss the problem relations, but consensus votes were recorded by the group
design facilitator. This scenario simulated an online voting system where participants
converged upon a decision without direct contact or open dialogue in advance of voting. The
open group were allowed to discuss the relations before voting. Notably, the results of this
study indicated that participants with higher dispositional trust, and those in more open
working groups, reported higher levels of perceived consensus and higher levels of perceived
efficacy of the IM technology.
These results, combined with results from studies showing increased learning gains in
more open and interactive groups (e.g. Ada, 2009) suggest that open discussion and dialogue
are critical factors in the success of collaboration using tools such as IM. Harney, Hogan &
Broome (2014) investigated the effects of generic versus metacognitive feedback on levels of
perceived and objective consensus and argumentation style of participants high and low in
dispositional trust, in the context of an Interactive Management (IM) session. Four groups of
undergraduate psychology students (N = 75) came together to discuss the negative
consequences of online social media usage. After screening for trust scores, participants high
and low on dispositional trust were randomly assigned to either a generic or metacognitive
feedback condition. In each feedback condition, an independent facilitator was given a specific
set of prompts or instructions which could be used as part of the feedback process. The generic
feedback condition used generic prompts to maintain discussion during idea generation and
idea structuring phases of IM, for example, “Does anyone else have an opinion on this idea?”.
The facilitator in the metacognitive feedback group also provided process-level and self-
regulatory feedback prompts that aimed to promote deeper analysis and evaluation of ideas and
problem relations, for example, “Is there any evidence to support your claim?”
Levels of perceived consensus, objective consensus, and perceived efficacy of the collaborative
learning methodology were measured before and after the IM session. Results indicated that
those in the metacognitive feedback condition, and those with higher levels of dispositional
trust, reported higher levels of perceived consensus in response to the group design problem.
Furthermore, those in the metacogntive feedback condition also reported significantly higher
levels of perceived efficacy of the ISM process compared to those in the generic feedback
condition. Finally, analysis of the dialogue from the IM sessions revealed that those in the
information feedback condition exhibited higher levels of sophistication in their arguments, as
revealed by the Conversational Argument Coding Scheme (CACS) (Seibold & Meyer, 2007).
Related research suggests that computer-supported collaborative learning (CSCL) can facilitate
creative, efficient and effective problem solving that promotes both intellectual development
and social interaction (Stahl, Koschmann & Suthers, 2006; Engelmann and Hesse (2010); see
also Pinkart, this volume). As noted by Salas, Sims and Burke (2005), one important lesson
that can be derived from existing research is that effective teams require more than just task
work (e.g., interactions with tasks, tools, machines, and systems). Teams do more than simply
interact with tools; they require the ability to coordinate and cooperatively interact with each
other to facilitate task objectives though a shared understanding of the team‟s resources (e.g.,
members‟ knowledge, skills, and experiences), the team‟s goals and objectives, and the
constraints under which the team works. Essentially, teams also require teamwork, and we
believe that the application of systems science tools embedded in a systems science curriculum
can be used not only to cultivate cognitive talents such as critical and systems thinking and
computational modelling skills, teamwork can be used to enhance social intelligence and
effective team dynamics.
A logical approach to systems science education
Implementing systems science education may not be a simple endeavor, but if we follow the
suggestions of Warfield, it is relatively straightforward: we can learn something of systems
science by first learning a science of description (e.g., physics, chemistry, biology, psychology,
sociology, economics). Students arriving to university to study science will have a foundational
understanding of science that will develop over time in the right conditions. We can further
enhance this understanding by offering students a parallel systems science training programme.
For example, in the context of a three or four year science education program, students can first
learn argument mapping and critical thinking skills that reinforce and deepen their
understanding in relation to the facts and relations of any given domain-based science.
Furthermore, the largest effect sizes in terms of growth in critical thinking ability will be
achieved if there is a cooperative enquiry component to the teaching of science and related
argumentation skills. Therefore, we suggest that during the first year of any science education
programme, students enrolled in the systems science education programme will take a generic
argument mapping training module. This module will include both individual analysis work
and cooperative enquiry work, with the goals of developing the skills of analysis, evaluation,
and inference, graphicacy skills, and generative knowledge search and knowledge import skills.
The second year of the programme would build upon the critical thinking and graphicacy skills
students have acquired by providing instruction in the sciences of design, complexity, and
action, specifically, by extending their cooperative enquiry to the collective design of
problematiques, enhancement structures, and option fields that pertain to increasingly complex
scientific and social problems. The second year would also include training in structural
equation and system dynamics modelling and incorporate a focus on problems that require
knowledge input from students working in disparate domains of science.
The third year of the programme would take students into the field, working on real-world
social problems in collaboration with service learning and community based research faculty in
the University. Students learn about the multidisciplinary nature of systems science and the
very real challenge of implementing systems science in the context of real world problems.
Students work with community stakeholder and content experts and become increasingly
effective team members and applied systems scientists who come to appreciate the power and
potential of collective intelligence and collective action, and the totality of their potential
influence as a member of a team.
Notably, applied systems science seeks more than description and explanation of scientific
problems. Consistent with the principles of functional contextualism, applied systems science
seeks control over environmental contingencies that influence problematic situations (Chiesa,
1994; Skinner, 1972). However, when designing a systems science education curriculum, tasks
and territories need to be manageable in terms of complexity, difficulty, and scale, such that
students can bring about positive social change within their territory of influence within a
reasonable timeframe (e.g., within a semester or a full academic year). Tasks are selected in
partnership with community-based learning or service learning coordinators, and students
advance their understanding of the science of design by learning how to use systems science
tools in the context of team-based projects focused on real-world, ecologically valid tasks (e.g.,
how to improve volunteer satisfaction in the „meals-on-wheels‟ service for older adults in
Galway city). The ecosystem or territory of task activity opens students to the reality and
complexity of local problems, but complexity is managed by selecting a sub-set of problems in
the problem field (e.g., modeling interdependencies between the 12 most critical problems in a
problem field and generating options to resolve fundamental drivers of negative influence in
the problem field).
Training in applied systems science needs to begin in the classroom and focus on developing
teams and talents and specific tool skills. However, there needs to be focus on meaningful tasks
from the outset of training. Training might well begin with a classroom focus on description
and explanation of both basic and applied science problems that are related in a very clear way
to real world problems (e.g., modeling factors associated with community resilience based on a
review of the empirical literature), but move from here to modelling similar dynamics with
community stakeholders. In this way, cooperative enquiry and cooperative action come to be
understood across the full spectrum of scientific activity, from basic to applied research to real
world problem solving that must be conducted in a social and political context that places
constraints on applied systems science and collective action.
We believe that the appropriate design and successful delivery of a systems science curriculum
that embeds tasks in a territory outside of the classroom and exposes students to real-world
project timelines and the potential stress and failure associated with real-world constraints on
collective action, will facilitate the transfer, generalization, and reinforcement of both mastery
and affiliation motive systems within the group. Mastery and affiliation motive systems are
central to successful cooperative and collaborative dynamics and fundamental to successful
problem solving in science and society. Cooperative relationships are characterized by
reciprocity, mutual respect, discussion, perspective taking, and a coordination of each
individual‟s views with those of others (Wright, 1982). When students have the opportunity to
share their views they are more likely to develop a stake in the process and therefore become
motivated to learn and work toward common goals (Wells and Arauz, 2006).
Consistent with Warfield‟s vision for systems science, Wells and Arauz (2006) argue that
cooperative group work can be more productive than individual work. Group-generated
knowledge can be a resource for individual understanding; individual understanding and action
capacity can be a resource for the collective action of the group; students can revise their own
perspectives in light of differing perspectives; higher-level collective and coordinated skill
structures can derive from lower-level individual skill structures; coordinated perspectives can
facilitate systems thinking along multiple paths of influence; sub-groups and individuals can
focus on different aspects of a problematic situation and maximize the power of collective
In addition to further tool development requirement, there are a number of challenges moving
forward, including the development of a deep understanding of how best to facilitate groups in
both classroom and online learning environments, how best to train and prepare facilitators, and
how best to increase the efficiency of working groups and possibly reduce the monitoring
burden on facilitators using information technologies that support key processes (e.g.,
individual and group feedback) and the development of key products (e.g., models, simulations,
reports, action agendas). Overall, we are very excited about the prospect for the development
of a new applied systems science module that facilitates systems thinking, collective
intelligence, and collective action focused on the resolution of an increasing variety of social
problems. We also recognise that there are many challenges, but consistent with Warfield‟s
view, we believe that these challenges and problems are the primary catalyst of creativity and
the design of new solutions that help us to work together to solve shared problems.
Ackermann, F., Andersen, D. F., Eden, C., & Richardson, G. P. (2010). Using a group decision
support system to add value to group model building. System Dynamics Review, 26(4),
Ackoff, R. L. (1981). Creating the corporate future: Plan or be planned for. New York: John
Wiley and Sons.
Alberts, H. (1992, March). Acquisition: Past, present and future. Paper presented at the
meeting of the Institute of Management Sciences and Operations Research Society,
Argyris, C. (1982). Reasoning, learning, and action: Individual and organizational. San
Ashby, W. R. (1958). Requisite variety and its implications for the control of complex
systems. Cybernetica, 1(2), 1-17.
Boulding, K. E. (1966). The impact of the social sciences. New Brunswick, NJ: Rutgers
Broome, B. J. (2006). Applications of Interactive Design Methodologies in Protracted
Conflict Situations. Facilitating group communication in context: Innovations and
applications with natural groups.: Hampton Press.
Broome, B. J., & Chen, M. (1992). Guidelines for computer-assisted group problem-solving:
Meeting the challenges of complex issues. Small Group Research, 23, 216-236.
Chang, N. (2010). Using Structural Equation Modelling to Test the Validity of Interactive
Management. Western Political Science Association 2010 Annual Meeting Paper.
Available at SSRN:http://ssrn.com/abstract=1580590
Chiesa, M. (1994). Radical behaviorism : the philosophy and the science. Boston: Authors
Checkland, P. B. (1989). Soft systems methodology. Human systems management, 8(4), 273-
Christakis, A. N. (1987). Systems profile: The Club of Rome revisited. Systems Research, 4,
Cleveland, H. (1973). The decision makers. Center Magazine, 6, 5, 9-18.
Coke, J. G., & Moore, C. M. (1981). Coping with a budgetary crisis: Helping a city council
decide where expenditure cuts should be made. In S. W. Burks & J. F. Wolf (Eds.),
Building city council leadership skills: A casebook of models and methods (pp. 72-85).
Washington, DC: National League of Cities.
Deal, T. E. & Kennedy, A. A. (1982). Corporate cultures: The rites and rituals of corporate
life. Reading, MA: Addison-Wesley.
Dwyer, C., Hogan, M.J., Stewart, I. (2013). An examination of the effects of argument
mapping on students' memory and comprehension performance. Thinking Skills and
Creativity, 8, 11 - 24.
Eden, C., and Ackermann, F. (2001). Strategic Options Development and Analysis - The
Principle. In J. Rosenhead & J. Mingers (Eds.), Rational Analysis for a Problematic
World Revisited (pp. 21-41). Wiley: Chichester.
Engelmann, T., Baumeister, A., Dingel, A., & Hesse, F.W. (2010). The added value of
communication in a CSCL-scenario compared to just having access to the partners‟
knowledge and information. In J. Sánchez, A. Cañas, & J.D. Novak (Eds.), Concept maps
making learning meaningful: Proceedings of the 4th international conference on concept
mapping, 1, 377–384. Viña del Mar, Chile: University of Chile.
Engelmann, T. & Hesse, F.W. (2010). How digital concept maps about the collaborators‟
knowledge and information influence computer-supported collaborative problem solving.
Computer-Supported Collaborative Learning, 5, 299–319.
Facione, J. (1990). Critical Thinking: A Statement of Expert Consensus for Purposes of
Educational Assessment and Instruction: The California Academic Press.
Fischer, K. W., & Bidell, T. R. (2006). Dynamic development of action, thought, and
emotion. In W. Damon & R. M. Lerner (Eds.), Theoretical models of human
development. Handbook of child psychology (6th ed., Vol. 1, pp. 313 - 399). New York:
Feeg, R. (1988). Forum of the future of pediatric nursing: Looking toward the 21st century.
Pediatric Nursing, 14, 393-396.
Forrester, J. W. (2007). System dynamics - a personal view of the first fifty years. System
Dynamics Review, 23(2-3), 345-358.
Friend, J. (2001). The strategic choice approach. In J. Rosenhead & J. Mingers
(Eds.), Rational Analysis for a Problematic World Revisited (pp. 115-150). Wiley:
Harney, O., Hogan, M.J., Broome, B. (2012). Collaborative learning: the effects of trust and
open and closed dynamics on consensus and efficacy. Social Psychology of Education,
Heckhausen, J. (2000). Evolutionary perspectives on human motivation. American Behavioral
Scientist, 43(6), 1015-1029.
Heckhausen, J., & Schulz, R. (1995). A Life-Span Theory of Control. Psychological Review,
Hogan, M.J. (2012). Well-Being in Ireland: Overcoming Barriers to Well-Being in Ireland,
Conference Report, NUI, Galway.
Hogan, M.J. & Broome, B. (2013).Wellbeing in Ireland – Designing Measures and
Implementing Policies, Conference Report, NUI, Galway.
Johnson, D. W., & Johnson, R. T. (2009). An Educational Psychology Success Story: Social
Interdependence Theory and Cooperative Learning. Educational Researcher, 38(5),
Keever, D. B. (1989, April). Cultural complexities in the participative design of a computer-
based organization information system. Paper presented at the International Conference
on Support, Society and Culture: Mutual Uses of Cybernetics and Science, Amsterdam,
Kemeny, J. (1980). Saving American democracy: The lesson of Three Mile Island.
Technology Review, 83, 7, 64-75.
Kraemer, K. L., & King, J. L. (1988). Computer-based systems for cooperative work and
group decision making. ACM Computing Surveys (CSUR), 20(2), 115-146
Kuhn, D. (2005). Education for thinking. Cambridge, Mass.: Harvard University Press.
Kuhn, D., Goh, W., Iordanou, K., & Shaenﬁeld, D. (2008). Arguing on the computer: A
microgenetic study of developing argument skills in a computer-supported environment.
Child Development, 79, 1311 - 1329.
Maani, K.E and Cavana, R.Y. (2000). Systems Thinking and Modelling: Understanding
Change and Complexity, Prentice Hall, Auckland.
Mikulincer, M., & Shaver, P. R. (2007). Attachment patterns in adulthood: Structure,
dynamics, and change. New York Guilford Press.
Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our
capacity for processing information. Psychology Review, 63, 81-97.
Nunamaker, J. F., Dennis, A. R., Valacich, J. S., Vogel, D., & George, J. F. (1991). Electronic
meeting systems. Communications of the ACM, 34(7), 40-61
Piaget, J. (1952). The Origins of Intelligence in Children (M. Cook, Trans.). Oxford:
International Universities Press.
Piaget, J. (1955). The child's construction of reality. [London]: Routledge & Paul.
Richerson, P. J., & Boyd, R. (2005). Not by genes alone : how culture transformed human
evolution. Chicago ; London: University of Chicago Press.
Rittel, H., & Webber, M. (1974). Dilemmas in a general theory of planning. DMG-DRS
Journal, 8, 31-39
Salas, E., Sims, D. E., & Burke, C. S. (2005). Is there “big five” in teamwork? Small Group
Research, 36(5), 555–599
Sato, T. (1979). Determination of hierarchical networks of instructional units using the ISM
method. Educational Technology Research, 3, 67-75.
Simon, H. A. (1960). The new science of management decisions. New York: Harper & Row
Skinner, B. F. (1972). Beyond freedom and dignity. London: Cape.
van Gelder, T. J., Bissett, M., & Cumming, G. (2004). Cultivating Expertise in Informal
Reasoning. . Canadian Journal of Experimental Psychology, 58 142-152.
Stahl, G., Koschmann, T., & Suthers, D. (2006). Computer-supported collaborative learning:
An historical perspective. In R. K. Sawyer (Ed.), Cambridge handbook of the learning
sciences (pp. 409-426). Cambridge, UK: Cambridge University Press
Stein, Z., Dawson, T., & Fischer, K. W. (2010 ). Redesigning testing: Operationalizing the
new science of learning. In M. S. Khine & I. M. Saleh (Eds.), New Science of Learning:
Cognition, Computers and Collaboration in Education (pp. 207–224). New York:
Vennix, J. (1996). Group Model-Building: Facilitating Team Learning Using System
Dynamics. Wiley, Chichester.
Warfield, J. N. (1974). Structuring complex systems. Columbus, Ohio,: Battelle Memorial
Warfield, J. N. (1990) A science of generic design: Managing complexity through systems
design. Salinas, CA: Intersystems.
Warfield, J. N., & Cardenas, A. R. (1994). A handbook of interactive management (2nd ed.).
Ames: Iowa State University Press.
Warfield, J. N. (2006). An introduction to systems science. Singapore: World Scientific.
Wells, G., & Arauz, R. M. (2006). Dialogue in the classroom. Journal of the Learning
Sciences, 15, 379–428.
Werner, H. (1957). Comparative psychology of mental development. New York:
International Universities Press.
Werner, H., & Kaplan, B. (1962). Symbol formation. New York: Wiley.