


































Frontiers of Contemporary Education 
ISSN 2690-3520 (Print) ISSN 2690-3539 (Online) 

Vol. 2, No. 2, 2021 

www.scholink.org/ojs/index.php/fce 

42 
 

Original Paper 

Linking Budgeting with Computational Thinking Pedagogy: 

Program Theory, Performance, and Budgeting 

Michael Lindsay Hall
1
 

1
 Roger Williams University, Bristol and Providence, Rhode Island, United States 

 

Received: August 10, 2021    Accepted: August 19, 2021    Online Published: September 25, 2021 

doi:10.22158/fce.v2n2p42                  URL: http://dx.doi.org/10.22158/fce.v2n2p42 

 

Abstract 

The pedagogy involved with preparing and delivering an analytically based course must contend with a 

number of important limitations or challenges. The challenges/limitations include needing a context for 

the use of the analytics being taught; others include where best to embed analytic courses in degree 

curriculum, determining content and delivery along with a number of additional limitations. A context 

can be created for these courses by establishing a base of usefulness of the course content and how it 

relates to other courses and to professional applications. However, one useful approach for a 

budgeting course is to put the analytics in a context of production and performance. These two 

significant elements of any problem-solving organization finance and budgeting process are significant 

features of teaching a course in budgeting. The article presented here is an illustration of a 

context-based approach along with features of pedagogy based in computational thinking which can be 

used to operationalize course elements while overcoming other salient limitations for analytic courses. 

The exemplar of a budgeting course is posed as an example.   

Keywords 

computational thinking, budgeting, program theory, program design  

 

1. Introduction  

Analytically based courses are, by their very nature, challenging to develop and deliver. Courses 

requiring arithmetic and/or mathematical skills are the ones that offer particularly unique kinds of 

challenges. Those challenges begin with contextual limitations. Some of the limitations are presented 

by class participants. Further limitations would be content for linking with other courses to provide a 

scaffolding for learning and for deploying analytics professionally. A pedagogical approach to deal 

with this kind of problem can begin by establishing a context of usefulness of the arithmetic or math 

applications being taught. Courses such as production design, operations research, finance, accounting, 



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statistics, program evaluation, or budgeting which rely heavily on math would be the kind of courses to 

have the kinds of contextual limitations which potentially reduce the likelihood of maximum learning 

and intention to transfer to professional use. Courses intended to build problem solving skills are found 

in MBA, MPA, and Health Administration professional degree programs, among others. An example 

of the approach to create a context for mitigating context limitations follows using an MPA budgeting 

course as a point of reference for a way to overcome the challenges in analysis-based courses. 

 

2. Literature Review 

The budgeting course in Public Administration Degree programs is probably best understood as a 

pivotal course. It is pivotal because budgets exist for programs to function. Programs are what MPAs 

are being educated to manage. Courses in such Public Administration curricula are also intended for 

nonprofit applications as well as health care organizations. There are such courses in MBA degree 

programs for people interested in nonprofit management.  

So important is the program and budget linkage that, according to Howard Frank (2002, p. 147) a key 

component of the budget course he discusses is performance or simply put, how a government program 

works and needs to work. An apt analogy here might be one used by B. Guy Peters in his book 

American Public Policy, in which he refers to programs as engines of public policy. His book title, 

coincidentally, contains the subtitle “Promise and Performance” (Peters, 2019). Program performance 

is directly linked to budgets for those programs. Operations research for manufacturing or even 

services would offer a similar academic footing. Operations research is about production and 

performance which are at the heart of any service, program, or other organized, purposeful set of tasks 

intended to create an organizational result. 

However, traditional budgeting with its line items does not clearly offer a complete picture of 

production. Thus, a budgeting course must navigate the limited information base of line items to get to 

performance as a topic. It is also not uncommon for the production process to be unconnected to the 

budgeting process, with little input to decisions other than “budget requests”. The result is students take 

course a course for which they have only limited individual context and, if they are “in service” 

learners, they ordinarily do not encounter performance and budgets actually linked in a meaningful way. 

The course structure and discussion which follows is one way to take on this teaching task of creating 

linkages in thinking to programs/production from the important context of the precursors to budgeting. 

2.1 Programs/Production Come First 

Since the engine metaphor is so workable for the understanding of what drives government policy or 

nonprofit or healthcare programs, it is only appropriate to recognize that engine design is critical to 

performance and fuel usage. An engine can only perform the way it was designed to operate and its 

fuel usage is also dependent on the design; equally the case with programs. Programs cannot create 

effects or results beyond their design. Consequently, program design is no small consideration when 

developing budgets. Attention to the “best that we know” for program design is essential for 



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performance and the budgets that fuel it. The same would be true for healthcare processes, nonprofits, 

as well as, for business and manufacturing.  

Manufacturing would begin with product design followed by production design to make it. Production 

design would not be guesswork. Production would be based on sound engineering principles to 

optimize production.  

Social services, human services, government operations, and healthcare programs should proceed on no 

less a foundation. Program theory represents the best that we know about these kinds of programs or 

production processes and is, therefore, instrumental in the design of a program; or at least could or 

should be. Nonetheless, the program must still be implemented based on some kind of thinking to 

achieve performance. Fidelity is the term applied to how closely program design for the program 

conforms to program theory’s determination of what has been shown to work best. Program theory, 

therefore, would be a logical starting point for finance and spending topics covered in a budget course. 

2.2 Program Theory 

Program theory is one of the younger theories. Unlike astronomy, physics, chemistry or engineering 

which have long histories, program theory in building and evaluating public and nonprofit programs is 

relatively new dating from the late 1960s. The range of program theories has grown since then and can 

now be found in the literature of social science, public administration, psychology, criminal justice, 

education, and public health to name only a few.  

These disciplines have their individual disciplinary theories which their respective professionally 

degreed practitioners are expected to master. The practitioners are expected to apply those theories in 

therapy, social work outreach programs, policing, and teaching.  

Schwandt (2014) says program theory and program management can and should be mutually 

reinforcing and be used to solve problems in program operations. Debra Rog, who references Schwandt 

in her article “Infusing Theory into Practice, Practice into Theory: Small Wins and Big Gains for 

Evaluation”, supports Schwandt but goes on to point out “there remains a lack of clarity, specification, 

and systemization for much of what we do” (Rog, 2015, p. 224). Even though she writes in an 

evaluation context, programs are the primary element in that context. Both Schwandt and Rog are part 

of the teaching community of higher education.  

Programs are what budgets are created to operate. What is most often used in budgeting pertaining to 

program design and implementation is “common knowledge” or as Rog puts it, program design is 

approached with some kind of “technical” expertise built over time (Rog, 2015). Importantly, Rog goes 

on to draw on Bickman (2000) plus Bickman, Peterson (1990) and Lipsey (1993), defining program 

theory as “an underlying logic or theory of change for (program) evaluation, typically a program or an 

intervention” (Rog, 2015, p. 225). More simply put, program theory is the reasoning based on research 

of why a program can work. As Rog shows, program theory is most often not considered when 

evaluating a program; it certainly is not considered when designing a program. Many practitioners 

think program theory is for academics only. They, as a group, see no value in applying or even 



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searching out program theory. In service learners are likely enough to bring such a mindset to a 

budgeting class. 

As a result of the contextual limitations described above, it is even more unlikely program theory is 

linked to budgeting for the program. A traditional budget built on line items in no way represents 

program work; only what is allotted for spending on its operation. Program design, let alone program 

theory, were not factors in creating budgetary assignment to a program’s appropriation; nor managing 

it nor evaluating it. 

Accordingly, it is imperative that program theory be introduced in a public administration curriculum 

as soon as the first course. Program theory should also be introduced early in other curricula for 

degrees in disciplines mentioned above. If not in an introductory course, then the budgeting course 

becomes the next best place since such a course would be open to the issue of performance. If program 

theory is reaffirmed as important in any program evaluation course, the foundation of content 

knowledge concerning program theory can become stronger. Nonetheless, program theory should be 

the underlying source for understanding program performance since simply examining line items, the 

most common budget format, does not allow for any performance assessment. Staying within spending 

limits of line items does not performance make. 

Failure to introduce and use program theory in a budgeting course creates an imbalance in what 

students learn in order to apply “the best that we know”. Students and teachers who guide them are in 

essence, victims of myopia about one of the key learning outcomes from an MPA or similar programs 

granting professional degrees. 

2.3 Performance and Budgeting  

Alfred Tat-Kei Ho discusses Performance Budgeting Theory in his article, “From Performance 

Budgeting to Performance Management” noting the importance of linking performance and budgeting. 

The perspective he offers shows that over many years of effort urging use of performance budgeting 

there is little to show for it (Tat-Kei, 2018). Tat-Kei traces over 100 years of history to survey efforts to 

add performance to budgeting. He points to Schick’s “classic” theory of categorizing the control, 

management, and planning orientations as the first marker laid down toward a more systematic 

understanding of budgeting and performance. Tat-Kei also discusses a “more relaxed” form of 

Performance Budgeting Theory which he terms “performance informed budgeting”. This approach to 

adding performance to budgeting requires, he says, some form of “dialog theory”. Then there is 

“presentational performance budgeting” which shows decision makers performance information but 

Tat-Kei writes it is “rarely used” and is not even intended for decision makers during budget decision 

making. 

All of Tat-Kei’s points are valid and important. However, these approaches or theories are at least one 

step beyond the origination point of budgets (whether stated or not): the programs.  

 

 



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Programs are the first step to improved budgeting and budget decision makers. While it is 

unquestionably true, as Tat-Kei states, important precursors must exist for performance. Those 

precursors include capability to think about performance; leadership for building consensus on its use; 

interest in performance in applying performance to budgeting; and a number of “layers” of challenges 

surrounding the budgeting task including staff competency about measurement information plus 

technology to use it. He does not discuss program theory as one of the precursors, but it must be 

considered in the list, perhaps even the primary precursor without which performance data may be off 

target more than it otherwise might be.  

Certainly, the challenges to performance criteria and program theory’s use are significant. Nonetheless, 

programs are at the base of budgeting. There would be no need for budgeting if programs did not exist. 

Given this point of view, the work of Campbell and Lambright is an illustration of program theory 

informing program performance (Campbell & Lambright, 2016).  

They set out to examine nonprofit program performance and its origination point. They apply program 

theory called Multiple Constituency Theory. Before doing so, they review program theories which 

frame performance as accountability writing. They make it clear that such approaches are imposed by 

funders rather than being applied by decision makers in budgeting. While certainly valuable for 

funding originators and are not to be abandoned, Campbell and Lambright argue for an expanded 

consideration of program performance. Their research points to the need for an enlarged definition of 

performance. Which means, in turn, a change in program designs to match the expanded performance 

criteria. Such a change would make program design from this perspective stronger. Campbell and 

Lambright offer yet another reason to include program theory is a graduate budgeting course.  

Persuading budget decision makers about the importance, value, and improvements program theory can 

make, must be a “selling job” to decision makers. Professional degree holders having taken budgeting 

and finance courses should be taught about the value of using program theory to inform performance, 

however. Yet, trying to sell decision makers on performance becomes even more difficult if the 

performance of the program is not up to standards based on performance measurement. Here it is 

important to emphasize measurement is not counting.  

Measurement requires a scale; counting does not. This distinction must be made unequivocal to 

graduate students so that they do not fall into the trap of using counts as performance gauges. Equally 

important for understanding measurement for performance is the situation in which program design 

was inadequate to the task to begin with and becomes based on counting, as in traditional rating 

performance.   

It is difficult to imagine production and performance so distant from the thinking of those charged with 

managing government programs, nonprofit enterprises, or healthcare. Even more difficult to explain is 

having to be persuaded to apply theory or principles or even best practices. Using the terms production, 

performance, and theory yielded almost 27,000 results from the author’s university library search 

engines. The results come from agriculture, water treatment, computer chip manufacturing, education, 



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education policy, public speaking, and government enterprise analysis, among others. It should be 

pointed out here these results are just from the years 2017 to 2020, making them recent as well as 

relevant.  

The point here is not to criticize Tat-Kei but instead to agree with him and illustrate how the task of 

persuading budget decision makers on the value of performance budgeting could be accomplished. 

Program theory should precede performance budgeting not replace it. Nor is the intention here to 

ignore the barriers and challenges in moving performance budgeting ahead through the “layers” of 

challenges. The intention, instead, is to build a strong foundation of program design, development, 

implementation, and evaluation to allow for performance measurement information to be valuable and 

therefore “sellable”. Putting these elements of performance together in this sequence would build a 

learning pathway for students to take with them to employ on the job.  

 

3. Research Methods 

A critical review of relevant conceptualizations found in the literature was undertaken to develop an 

exemplar course applying computational thinking bolstered by program theory, program performance, 

budgeting, and course pedagogy. The material found most relevant is considered below.  

3.1 Data Sources and Analysis 

Program theory is not likely to be the first thing on the minds of decision makers, particularly the 

elected ones or funders in the nonprofit context. Program theory, however, is in the purview of those 

trained as MPAs or MBAs. MPAs are more apt to be at least familiar with the concept from their 

courses and seminars. O’Brien (1987), as cited in Clement and Bigby (2011), emphasizes program 

theory as the basis of performance. Without program theory they write “an organization is not able to 

assess how well they (sic) are implementing processes that are linked to expected outcomes”. 

Measuring workload, time on task, number of clients served, and the like which are counts, makes it all 

but impossible to understand or to determine why or, even if, outcomes did occur (assuming such 

outcomes are the appropriate ones for the processes used). Furthermore, if the appropriate outcomes did 

not occur, the counting numbers will not be the method of discovering why not. Since budgeting should 

be based on performance, repeating, even a supposedly successful program, would be guesswork 

without knowing about the link between work and results. Furthermore, not knowing what was to 

intended to work and did not, converts into no better than puzzlement. Budget course learners must be 

aware of these concepts. 

3.2 Logic Models 

Another understanding of program theory and one which may be clearer to decision makers is logic 

modeling. A logic model is an illustration of work processes which, with careful attention, can be used 

as a diagram for viewing a program and its processes. Logic models, in other words, allow 

visualization of work process for the program. Visual representations offer the benefit of a way to see 

how the program processes are aligned and how those processes are supposed to link to results.  



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Logic model visuals are also important for seeing how a program’s work process is supposed to align. 

Visuals provided an added benefit of becoming a “selling” mechanism to decision makers. An added 

value from such visuals would be the ability to determine the implementation fidelity with logic models 

and/or program theory. Munter et al. (2014), show the way on this issue in their article “Assessing 

Fidelity of Implementation Unprescribed, Diagnostic Mathematics Intervention”. Following Dusenbury 

et al. (2003), fidelity of implementation is implementing programs as intended. They also show 

examples of fidelity assessment criteria such as quality of program delivery, adherence, program 

participants’ responsiveness to the program, and program duration. Munter, et al., also indicate the 

benefit of understanding the programs “mechanisms for change” can be better assessed for refining 

program theory and improving understanding why a program works or does not. In so doing, a form of 

program evaluation is being created. Program evaluation can then become linked with budgeting for 

the program.  

The literature in public administration is replete with logic models. Program evaluation literature is one 

of the key areas where logic models can be found. An MPA must become aware of such literature and 

the value of logic models.  

3.3 Program Theory, Logic Models, and Lean Management 

Douglas Martin (2017) points to using lean management connected to decision making based on 

program processes illustrations. He points to the linkages of these elements for addressing the “layers” 

of decision making around budgets in his examination of the civil service in the British context. He 

concludes that lean management applied to Social Security reduced the complexity of what had become 

unnecessarily bureaucratic generating layers of rules and decision making for the eligibility of 

claimants. Furthermore, lean generated “greater emphasis” on performance targets. It led to 

consolidation of what had been separate decision-making processes.  

Martin reports further “Lean was central to addressing more efficient and quicker ways of dealing with 

these areas of work” (Martin, 2017, p. 155). Lean Thinking and Management courses appear in MPA 

and MBA curricula. The lessons from lean coursework can be transferred directly to budget course 

material to train MPAs or MBAs in the decision-making portions of a budget course project.  

Value Stream Maps used in lean management represent both the present state of the process of program 

operations and its future state forming, in a real sense, a logic model also. The value stream may 

provide more detail in many cases, but the starting point and end point are the same as a logic model. 

An illustration of the use of value stream maps in this sense was research performed by Hurley et al. 

(2017). In the study they designed, the research was intended to understand the best patient pathways in 

national patient care used in Ireland. Using value stream mapping, the researchers examined protocols 

or standard operating procedures to find patient care operations that “promote effective patient flow 

through the continuum of care from presentation at the emergency department through safe discharge 

home” (Hurley et al., p. 2). The data from the study was to show the national Irish agency in charge of 

quality of care how the lean processes can provide what is needed to make care better. In other words, 



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the value stream maps were to be used for decision making. The researchers even applied program 

theory to the approach they described. Lean thinking and management are capacity building elements 

for budgeting purposes also. 

As can be seen from this discussion, program theory, lean thinking and management, logic models, and 

dollars and cents can be construed as intertwined. The nexus of these concepts is therefore directly 

linked to budgeting and as such can be a part of a budgeting course. 

The program theory-based budget course can be delivered in a measured fashion to build toward the 

final element of an actual budget creation exercise. The concept of computational thinking becomes 

extremely germane here as part of the program theory section. It is the foundational element for 

building a course using the concepts needing linkage with budgeting discussed above. Uzumcu and 

Bay (2021) discuss conceptual thinking as a concept in teaching students 21
st
 century problem solving; 

in other words, courses like budgeting combining program theory, performance, and lean management 

principles. They write that computational thinking is being applied to a “wide range” of curricula 

internationally (p. 567). 

Citing Wing from 2010, Lee and Malyn-Smith, report that computational thinking is, in the first 

instance, reasoning in a way that formulates a problem and then devising its solution. These two steps 

allow “an information processing agent” to apply the solution to the problem (Wing, 2010, p. 1; Lee & 

Malyn-Smith, n.d., p. 9). Such a description clearly fits anyone involved in program management and 

budgeting for that program.  

Hadad et al. (2021), build on this premise and its consequences by drawing on Zviel-Girshin et al., 

writing “Professionals in the knowledge society need digital skills suitable for coping with constantly 

evolving Information and Communication Technologies (ICT), digital tools, and applications (Hadad et 

al., p. 764; Zviel-Girshin et al., 2020). The budgeting process would be well served by having educated 

personnel who have the first order element of these skills: computational thinking. 

3.4 Computational Thinking 

Computational thinking is also termed procedural thinking (Papert, 1996; in Cansu & Cansu, 2019). 

For teaching budgeting procedural thinking might be less intimidating daunting for learners who think 

themselves less than proficient in mathematics or even arithmetic. Procedural thinking is a stepwise 

construction which can create a logic model and, therefore, a visual toward algorithmic thinking. 

Computational thinking overlaps with procedural thinking since both seek a logic toward finding a 

problem solution.  

Equally important, logic modelling for computational/procedural thinking is a direct pedagogically, 

sensible way to introduce program theory as logic modelling. The pedagogical approach using logic 

models would be a form of creating a learning context as described by Allen and Heredia (2021). 

Contexts can be limiting factors, as they explain them. Naturally, then reducing the influence of 

learning limitations would be important to integrating program theory into a budget course or, any 

analytic course for that matter. One way to do so, would be what Heredia and Tan (2021) did in 



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working with a graduate course: create a context for learning using active learning or learning by doing. 

Learning by doing is another way of saying learning by making as discussed below.  

Since computational thinking or procedural thinking both involve seeking problem solutions, it is valid 

to apply them to budgeting and a budgeting course. Existing budget numbers or expense numbers are 

most often defined as the problem rather than program theory or program design. Dollars for line items 

representing a program forms the problem because of limitations of revenue, time, and the imperative 

to show spending as the important issue. Papert writes, as cited in Cansu and Cansu, that procedural 

thinking can be interpreted as individual thinking about problem solving and involves “learning by 

making” (Papert, 1991, p. 1; in Cansu & Cansu, p. 1).  

A budget course would provide just such learning by making with a budget creation assignment, but 

one that also included program theory and program design resulting from that theory.   

A number of elements of computational thinking or procedural thinking that are logically associated 

with learning by making and would be useful in a budget course are  

 Formulating  

 Analyzing and dividing problems into manageable constituent parts 

 Organizing and modelling 

 Efficiency 

 Critical Thinking and creativity 

 Data Analysis 

All these features of computational thinking would be needed in making a course budget project. 

The very important element tying all of these facets of computational thinking together is a set of 

systematic logical steps making a replicable solution, i.e., an algorithmic way of thinking which is a 

form of synthesis. In this sense it “is the process of constructing a scheme of ordered steps which may 

be followed to provide solutions to all constituent problems necessary to solve the original problem” 

(Cansu & Cansu, p. 4). The synthesis step is found in the program and performance-based budget 

activity. Others of the list above are usually part of a budgeting course in one way or another. The 

pedagogical aim of using procedural learning in a budget course is putting these analytic processes in a 

logical order and using them to create a program and performance-based budget assignment. 

In their article on using computational thinking in an education setting, Kang and Lee (2020) call the 

approach described above as “project learning” and is found “in every discipline in schools for many 

years” (Kang & Lee, p. 5358). They add the approach has been “very popular”. They cite a large 

number of examples of successfully using computational or procedural thinking in courses (Kang & 

Lee, pp. 5360-5361). Kang and Lee also add that the approach of procedural or computation thinking 

has appeared in courses found in liberal arts (Kang & Lee, p. 5361). 

Caldron provides analysis for a more traditional classroom exercise intended to have students work in 

role plays and problem-solving exercise aimed at computational thinking. This teaching approach is not 

project learning, but it is learning by doing. The pretest and posttest results reported by Caldron showed 



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“a positive correlation between teaching abstraction and an enhancement in learner’s ability to engage 

in abstract thinking; and a positive correlation teaching algorithmic thinking and an enhancement in 

sequentiality” (Caldron, pp. 1190-1191). The Caldron role play results show formation of the two 

elemental features of computational thinking: abstract thinking and sequentiality. Abstract thinking 

allows a person to recognize patterns, spot then eliminate irrelevant facets of a problem, followed by 

generating a framework for diagnosing similar problems (Calderon, 2020). Sequentiality involves using 

a step-by-step process in an unambiguous fashion.  

There is no pedagogical reason that project learning or role plays and problem solving could not be 

combined. In a budget course, for example, role plays of stakeholders in the budgeting process could be 

designed followed by a project such as preparing a budget using hypothetical budget numbers and 

programs. The assignment could be to find the least expensive supplier of a key expense such as an 

item of equipment. Problem solving exercises in which the assignment is to work through a budget 

problem such as determine the best combination of resources for a hypothetical nonprofit theater 

deciding on how to balance marketing/outreach with the need to add new stage lighting. Suitable 

variations could be made for a government operation or health care organization.  

Such exercises in the language of teaching computational thinking are “unplugged”. That is to say, they 

do not necessarily need computer applications for calculations. In fact, there may be no actual 

mathematical work needed.  

An Example of one such unplugged assignment was developed and evaluated for learning outcomes by 

Mitri et al. (Mitri et al., 2017). The authors describe their work using role plays as active learning 

devising role plays which encourage, they say, participation and engagement. This description sounds 

remarkably similar to that described by Calderon et al. The similarities begin with the assignments 

being unplugged. Another similarity is learning by doing. Yet another similarity is following a 

sequence of steps. The desired learning outcomes involved “understanding” and “relating” information 

systems to strategy. While the term computational thinking is never used in the study, clearly the same 

kinds of cognitive skills would need to be applied as the Calderon, et.al, research highlighted. 

Furthermore, the role play exercises and learning outcomes included the subject of budgeting in the 

course they studied. 

 

4. Discussion 

While cognitive thinking emerged from Computer Science it need not be used exclusively for computer 

programming, as was its original purpose. A major use of computational thinking is problem solving 

based on mental steps to discern patterns, disassemble them, and then organize them for a solution to a 

problem. 

The need for analytic problem solvers is critical in many fields and disciplines. While computational 

thinking can yield an organized sequence of elements to solve a problem, not all problem solvers need 

to be able to algorithmic thinking. Wing noted in her definition of the concept the end point would be 



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the information processing agent to make the solution operational. The information processing agent 

can be other than the problem solver. In any case, the solution must be applied to the problem by 

someone competent in the analytics found in computational thinking. MPAs and MBAs would be the 

target audience for such thinking.  

Those information processing agents form the drivers of such fields as informatics and its full array of 

disciplines. Informationalists in informatics are trained to use databases to extract information for 

examination and use by decision and/or policy makers. The education of these groups includes 

graduate degrees such as MPA, MBA, Health Administration, and nonprofits.   

Analytically oriented courses, such as budgeting or finance or program evaluation, in these degrees 

present the opportunities illuminate the importance of computational thinking drawing on program 

theory, logic modelling, value steam mapping, and performance. As a result, the future decision makers 

or policy makers would be up to date on what their professional future is likely to present as problems 

and the way to solve them. 

Based on the research literature presented and reviewed here, the task of teaching the subject of 

computational thinking can be done for efficacy. While the focus here was a budgeting course, as was 

seen in the early sections above, the courses to which it can be applied are wide and diverse.  

 

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