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McKim and McKendree  Advancements in Agricultural Development 
  Volume 1, Issue 1, 2020 
  agdevresearch.org 

1. Aaron J. McKim, Assistant Professor, Michigan State University 
Natural Resources Building, 480 Wilson Road, Room 131, East Lansing, MI 48824 
amckim@msu.edu, https://orcid.org/0000-0002-0600-3611 

2. Robert “Buddy” McKendree, Assistant Professor, Michigan State University 
Natural Resources Building, 480 Wilson Road, Room 131, East Lansing, MI 48824 
rbm@msu.edu, https://orcid.org/0000-0003-0351-777X 

38 

Metacognition, Systems Thinking, and Problem-Solving Ability 
in School-Based Agriculture, Food, and Natural Resources 

Education 
 

Aaron J. McKim1, R. Bud McKendree2 

 

 

 

 
 

 

 

Abstract 
Problems within agriculture, food, and natural resource (AFNR) systems are increasingly complex, 
expanding the need for students to develop problem-solving abilities alongside an understanding of 
their own thinking. In this effort, we explored the problem-solving abilities, metacognition, and 
systems thinking of current AFNR secondary school students. A descriptive correlational study design 
was employed. Data were collected via an online, Qualtrics survey. Student responses on the 
metacognition, systems thinking, and problem-solving ability survey items suggested opportunities 
for increased focus on these areas throughout their educational experience. In addition, statistically 
significant relationships between metacognition and systems thinking and systems thinking and 
problem-solving ability reinforced the importance of these topics within AFNR Education. 
Recommendations emerging from this work include additional emphasis on metacognition, systems 
thinking, and problem-solving skills within secondary school AFNR classrooms; utilization of specific 
strategies to increase systems thinking; operationalizing specific strategies to increase metacognitive 
knowledge and metacognitive regulation; along with a cross-cutting recommendation for teachers to 
make their thinking more explicit during instruction to increase metacognition, systems thinking, and 
problem-solving skills among learners.  
 
Keywords 
Path Analysis; Transformational Classrooms; Self-Society Framework; Complex Problems; 
Interdisciplinary Thinking  



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Introduction and Problem Statement 

The students of today need to be prepared to solve the problems of tomorrow. In agriculture, 
food, and natural resources (AFNR), the problems of tomorrow (e.g., impacts of climate change, 
water quality, soil degradation) are increasingly complex due to the vast network of changing 
systems which impact, and are impacted by, AFNR (Fischer, Shah, Tubiello, & van Velhuizen, 
2005). Therefore, educational systems in AFNR must provide students with opportunities to 
develop their own problem-solving abilities (National Academies of Science, Engineering, & 
Medicine, 2018). Research on the development of problem-solving skills suggests the need for 
transformed learning spaces, which encourage student understanding of their own thinking 
processes alongside understanding the dynamic systems inherent within authentic and complex 
problems (Augustine, 2006).  
 
Given the abundant opportunities, and necessity, for problem solving within AFNR systems, 
AFNR Education must expand current knowledge related to problem solving ability (National 
Academies of Science, Engineering, & Medicine, 2018; Sterling, 2001). In this effort, the current 
study explores the problem-solving abilities of current AFNR secondary school students 
alongside two elements of transformed learning environments, metacognition (i.e., thinking 
about thinking) and systems thinking (i.e., understanding the dynamic and complex nature of 
problems and/or phenomena). A more robust understanding of metacognition, systems 
thinking, and problem-solving abilities among AFNR students is expected to empower AFNR 
Education by identifying variables which potentially contribute to the problem-solving abilities 
of learners.   
  

 
Theoretical and Conceptual Framework  

 
The framework for this study brings together literature from three conceptual underpinnings of 
the current study: (a) metacognition, (b) systems thinking, and (c) problem-solving ability. 
Through a description of each concept, potential relationships are uncovered and a conceptual 
model of identified relationships is developed. 
 
Metacognition 
Metacognition is defined as recognizing and regulating your own thinking (Hughes, 2017). 
Metacognition is comprised of two distinct elements: (a) metacognitive knowledge: knowing 
the optimal ways you process information (Jacobs & Paris, 1987; Schraw, 1998) and (b) 
metacognitive regulation: actively regulating your thinking (Brown, Bransford, Ferrara, & 
Campione, 1983). The major challenge associated with metacognition is the “hidden” nature of 
thinking (National Research Council, 2000). While covert, metacognition has been found to 
increase learning among students (Gourgey, 1998; Pate & Miller, 2011; Wang, Haertel, & 



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Walberg, 1990). Similarly, metacognition has been linked to scientific processes like hypothesis 
generation and problem solving (Blackburn & Robinson, 2016; Pate & Miller, 2011).  
 
Systems Thinking 
Systems thinking refers to the “awareness of and consideration for the interaction, 
synchronization, and integration of people, processes, and technology among dynamic and 
fluctuating systems” (Stalter et al., 2016, p. 326). Systems thinkers view problems and/or 
phenomena from a complex, nonlinear perspective and seek to identify patterns and 
relationships among interdependent components of a system (Leischow & Milstein, 2006). For 
example, when presented with the problem of soil erosion, a systems thinker might consider 
the historical, political, cultural, environmental, social, and economic elements of the problem 
and potential solutions. Systems thinking has been linked to metacognition through the self-
society framework. In essence, this framework argues awareness of thinking is required for 
individuals to position themselves to think about complex systems (Cabrera, Cabrera, & Powers, 
2015; Gregory, 2000).  
 
Problem Solving Ability 
Problem solving is defined as “efforts toward achieving a situational goal for which there is no 
direct solution” (Pate & Miller, 2011, p. 73). Both metacognition and systems thinking have 
been linked to problem solving ability. Due to overlap in executive control processes, higher 
metacognitive skill usage has been linked to improved problem solving (Magno, 2010). 
Specifically, problem solving requires the ability to sift through information, distinguishing 
between accurate and inaccurate information, and identify relevant and irrelevant facts, 
actions enhanced by metacognitive processing. In addition, research suggests systems thinking 
is a critical skill for problem solving. Highlighting individuals able to understand the 
interdependency of subsystems as well as unforeseen and long-term consequences of 
solutions, both Checkland (1999) and Senge (2014) laud systems thinking as a critical skill in 
complex problem solving.  
 
The emergent relationships found between metacognition, systems thinking, and problem-
solving ability comprise the conceptual model utilized within the study (see Figure 1). In this 
model, problem-solving ability is predicted by the combination of metacognition and systems 
thinking. Additionally, the model posits metacognition as a determinant of systems thinking. 
 
 
 
 

 
 
 
 
 
 

Problem-Solving 
Ability 

Systems  
Thinking 

Metacognition 



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Figure 1. Conceptual model of relationships between metacognition, systems thinking, and 
problem-solving ability.   

Purpose 

The purpose of this exploratory research was to analyze a conceptual model of the 
relationships between metacognition, systems thinking, and problem-solving ability among 
school-based agricultural education students. To accomplish this purpose, the following 
research objectives were identified:  

1. Determine the metacognition, systems thinking, and problem-solving abilities perceived 
by secondary school AFNR education students.  

2. Analyze a conceptual model of the relationships between metacognition, systems 
thinking, and problem-solving ability.  

 

Methods 

A descriptive correlational study design was employed to achieve the purpose and accomplish 
the stated research objectives. Data presented are part of a larger study exploring levels of 
student engagement in school-based AFNR education, metacognition, systems thinking, and 
problem-solving abilities.  
 
Target Population & Sampling 
The target population for this study included all secondary school students taking courses from 
school-based AFNR teachers in Michigan during the fall semester of the 2019-2020 school year. 
A stratified sample of students taught by twelve school-based agriculture teachers was 
purposively selected. Selected teachers were identified by engagement in a graduate level 
course offered by Michigan State University as well as teachers hosting micro-teaching 
experiences for preservice teachers at Michigan State University. Students from four of the 
recruited schools provided responses to the survey, yielding a 33.00% response rate among 
recruited teachers. In total, 56 secondary school student responses were received (n = 56). All 
respondents had a consent form signed by parent or guardian on file with the research team 
prior to completing the survey. Due to the sampling procedures, the data presented are not 
representative of the population.  
 
Instrumentation & Data Collection 
Data were collected via an online, Qualtrics survey distributed in September and October of 
2019. The survey included three constructs of interest, metacognition, systems thinking, and 
problem-solving ability. An existing construct, the Junior Metacognitive Awareness Inventory 
(JrMAI), was used to measure metacognition (Sperling, Howard, Miller, & Murphy, 2001), 
including both metacognitive knowledge and metacognitive regulation. The JrMAI is 18 
questions long (e.g., “I use different learning strategies depending on the task.”). An existing 
construct, the Systems Thinking Scale (STS), was used to measure systems thinking (Moore, 



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Dolansky, Singh, Palmieri, & Alemi, 2010). The STS includes 20 questions (e.g., “I consider how 
multiple changes affect each other.”). Similarly, an existing construct, Problem-Solving Ability 
Scale (PSAS) from the National Longitudinal Study of Adolescent to Adult Health, was used to 
measure problem-solving ability (Harris & Udry, 2018). The PSAS included four questions (e.g., 
“When I am attempting to find a solution to a problem, I usually try to think of as many 
different ways to approach the problem as possible.”). For consistency, each item within the 
three constructs was measured from 1 (never) to 5 (always).  
 
Validity and Reliability 
Each of the three constructs have been validated within existing literature; readers are 
encouraged to review the citations corresponding with each construct for more information 
about the validation process. To ensure the constructs were reliable among respondents, a 
post-hoc reliability analysis was completed. Each of the constructs fell within the acceptable 
range for reliability using Cronbach’s alpha (i.e., metacognition = .83; systems thinking = .93; 
problem-solving ability = .72; Fraenkel & Wallen, 2000; Nunnally & Bernstein, 1994).  
 
Limitations 
Two major limitations are considered in the current study. First, the data are limited by the 
honesty and truthfulness of the 56 respondents who provided data to the current study. 
Second, the data collection methods did not include more robust measures of metacognition 
(e.g., participant observations), systems thinking (e.g., mental modeling), or problem-solving 
ability (e.g., case problem analysis) due to feasibility, costs, and time constraints. 
 
Data Analysis  
Data collected for research objective one were analyzed by condensing items within the three 
constructs into three average construct scores, identifying the minimum and maximum 
response for each construct across participants, averaging each construct score across 
responses, and reporting the standard deviation among respondents. For objective two, a path 
analysis was completed in concert with the conceptual model. To complete the path analysis, 
two linear regressions were completed. The first regression included metacognition as the 
exogenous variable with systems thinking as the endogenous. The second regression included 
both metacognition and systems thinking as exogenous variables and problem-solving ability as 
the endogenous variable. Path analysis was selected as it provided a holistic evaluation of the 
conceptual model without requiring the sample size of more robust statistical analyses (e.g., 
structural equation modeling).  

 
Findings 

A brief description of respondents is provided in an effort to contextualize the data presented. 
Respondents included a larger number of females (62.75%) than males (37.25%). The largest 
proportion of respondents were sophomores (44.20%) followed by seniors (30.80%) and juniors 
(25.00%). With regard to FFA participation, the majority were non-officer members (71.20%) 
followed by FFA officers (19.20%) and students who were not involved in FFA at the time of 



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data collection (9.60%). In addition, the majority of respondents reported having a Supervised 
Agricultural Experience (76.90%) and being interested in a career in agriculture, food, and 
natural resources (70.60%).   
 
In research objective one, the metacognition, systems thinking, and problem-solving abilities 
perceived by students were examined (see Table 1). Students rated their problem-solving 
abilities (M = 3.57; SD = 0.80) slightly higher than metacognition (M = 3.55; SD = 0.64) or 
systems thinking (M = 3.46; SD = 0.65). A large range of responses, however, was identified for 
each of the three constructs.  
 
Table 1 
 
Metacognition, Systems Thinking, and Problem-solving Ability  
Variables Minimum Maximum M SD 
Metacognition 
 

1.06 4.78 3.55 0.64 

Systems Thinking 
 

1.85 5.00 3.46 0.65 

Problem-Solving Ability 1.00 5.00 3.57 0.80 
Note. Items measured from 1 (never) to 5 (always).  
 
In research objective two, the relationships between metacognition, systems thinking, and 
problem-solving ability were analyzed in accordance with the conceptual model (see Figure 2). 
The first path analyzed was the relationship between metacognition, the exogenous variable, 
and systems thinking, the endogenous variable. The model was statistically significant (F-value 
= 94.04; p-value < .001). Further, metacognition was a statistically significant predictor of 
systems thinking (B = 0.81, SEB = 0.08; β = .80; p-value < .001) and predicted 64% of the 
variance in systems thinking (r = .80, r2 = .64).  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Problem-Solving 
Ability 

Systems  
Thinking 

Metacognition 

R2 = .64 

R2 = .64 
.65 

.17 

.80 



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Figure 2. Output diagram of relationships between metacognition, systems thinking, and 
problem-solving ability.  
 
The second path analyzed included both metacognition and systems thinking as exogenous 
variables with problem solving ability serving as the endogenous variable. The second model 
was statistically significant (F-value = 43.53; p-value < .001). In total, the combination of 
metacognition and systems thinking predicted 64% of the variance in problem-solving ability (r 
= .80, r2 = .64). Systems thinking was identified as a statistically significant predictor of problem-
solving ability (B = 0.79, SEB = 0.17; β = .65; p-value < .001); however, metacognition was not a 
statistically significant predictor of problem-solving ability within the model (B = 0.21, SEB = 
0.18; β = .17; p-value = .237). 
 

Conclusions, Discussion, and Recommendations 

The need for educational systems to develop learners able to solve problems compelled our 
analysis of problem-solving ability, metacognition, and systems thinking among school-based 
AFNR Education students. Findings from the study help to refine the conceptual model 
presented in Figure 1. The lack of a statistically significant relationship between metacognition 
and problem-solving ability, when accounting for systems thinking, suggests a slightly modified 
model (see Figure 3) in which metacognition directly influences systems thinking with systems 
thinking directly relating to problem solving ability. Each of the relationships proposed in the 
reconceptualized model were supported by the findings.  
 
 
 
 
 
Figure 3. Reconceptualized model of metacognition, systems thinking, and problem-solving 
ability.  
 
In addition to a new perspective of the relationships between metacognition, systems thinking, 
and problem-solving ability, the current study illuminated additional points of discussion. First, 
the average scores for metacognition (i.e., 3.55 out of a possible 5.00), systems thinking (i.e., 
3.46 out of a possible 5.00), and problem-solving ability (i.e., 3.57 out of a possible 5.00) 
indicate opportunities for growth among responding students. Additional support for 
interventions to increase metacognition and systems thinking emerged from the path analysis 
of the relationships between constructs. Starting from the right side of the model, evidence 
suggests systems thinking is an important predictor of problem-solving ability. This finding is 
supported by existing research (Checkland, 1999; Senge, 2014). The identified importance of 
systems thinking suggests a greater emphasis on systems thinking within school-based AFNR 
classrooms could benefit the problem-solving abilities of learners. Methods for increasing 
systems thinking within classrooms include the use of case study analyses highlighting 
interdependence among systems, interdisciplinary connections being made explicit during 

Problem-Solving 
Ability 

Systems  
Thinking Metacognition 



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learning experiences, and encouraging student use of mental modeling to illustrate their 
thinking.  
 
Continuing to work left on the model, findings support a relationship between metacognition 
and systems thinking. The positive relationship between metacognition and systems thinking is 
supported by the self-society framework (Cabrera, Cabrera, & Powers, 2015; Gregory, 2000); 
specifically, that an individual more apt to internally consider their own thinking is positioned to 
think more broadly and systematically about external factors. Given the identified importance 
of systems thinking to problem-solving ability alongside the self-society framework, it is 
recommended that school-based AFNR Educators explore strategies for enhancing the 
metacognition of learners. Strategies for increasing metacognition include having students 
evaluate multiple learning strategies for their efficacy and asking metacognitive reflection 
questions to learners (e.g., “what about this problem is challenging,” “how are you learning 
this”) to build metacognitive knowledge. In addition, using a metacognitive regulatory checklist 
and asking metacognitive action questions of leaners (e.g., “in what other contexts could you 
use this learning strategy,” “what have you used successfully in the past to solve a problem like 
this”) are recommended strategies for building metacognitive regulation.  
 
Importantly, a simple recommendation which attends to both the need to develop systems 
thinking and metacognition among learners is for educators to make their thinking explicit 
during classroom instruction. For example, an educator who highlights why a particular answer 
may be more favorable when considering the interdependent systems while also articulating 
their thinking as they process to that conclusion has the potential to foster both systems 
thinking and metacognition among learners.  
 
In addition to practical recommendations for increasing metacognition, systems thinking, and 
problem-solving ability among secondary school learners, authors recommend more rigorous 
analyses of the reconceptualized model. As a roadmap, evaluating metacognition using 
regulatory checklists and participant observations, systems thinking using mental models, and 
problem-solving abilities using case studies will provide a more in-depth understanding of the 
relationships supported by this exploratory research. Further, obtaining a large enough sample 
to evaluate the model using structure equation modeling, in which systems thinking 
simultaneously acts as an endogenous and exogenous variable, will address the error variance 
unaccounted for within the path analysis approach. Future research can build upon the 
foundation provided by the exploratory study to uncover additional insights into the 
relationships between these important educational outcomes.  
 
With the opportunity to more effectively prepare the learners of today to be problem-solving 
leaders of tomorrow, analyses and interventions to understand and increase metacognition, 
systems thinking, and problem-solving skills within AFNR Education are essential to addressing 
the increasingly complex challenges facing the world.  



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