How to Rediscover Positive Economics for Students? Active Learning Experience (Teaching Case)*

Antonio Sánchez-Bayón¹, Miguel Ángel Alonso Neira² and Francisco Javier Sastre Segovia³
1. Associate Professor in Applied Economics and PhDc Education at Universidad Rey Juan Carlos (Spain)
2. Full Professor in Applied Economics at Universidad Rey Juan Carlos (Spain)
3. Full Professor in Business at ESIC University & Business School (Spain) 
Correspondence to: Antonio Sánchez-Bayón, antonio.sbayon@urjc.es

DOI: https://doi.org/10.70389/PJS.100200

Additional information

• Ethical approval: N/a
• Consent: N/a
• Funding: No industry funding
• Conflicts of interest: N/a
• Author contribution: Antonio Sánchez-Bayón, Miguel Ángel Alonso Neira and Francisco Javier Sastre Segovia – Conceptualization, Writing – original draf, review and editing.
• Guarantor: Antonio Sánchez-Bayón
• Provenance and peer-review: Unsolicited and externally peer-reviewed
• Data availability statement: N/a

Keywords: Austrian business cycle theory, Digital macroeconomics pedagogy, Fred-based data literacy, phillips curve flattening, heterodox mainline economics.

Peer Review
Received: 12 July 2025
Last revised: 10 December 2025
Accepted: 11 December 2025
Version accepted: 9
Published: 3 January 2026

*This is part of Sánchez-Bayón´s PhD dissertation in Education at Universidad Rey Juan Carlos (URJC, Spain). Supported by Grupo de investigación consolidado para el Estudio y seguimiento del ciclo económico de la Universidad Rey Juan Carlos (GESCE-URJC), Grupo de Innovación Docente consolidado en Tecnologías de la información y comunicación y tecnologías del aprendizaje y conocimiento para la mejora de los estudios de ciencias de la economía y de la empresa (GID-TICTAC CEE-URJC), CIELO ESIC Business & Marketing School and Fundación Jesús Huerta de Soto Ballester (Madrid, Spain).

Plain Language Summary Infographic

Introduction

This research, grounded in a teaching case methodology, addresses two interconnected pedagogical questions:

1. Why have Economics students become disaffected and how to deal with this disconnection? This paper is focused particularly on mainstream Economics education under the Neoclassical Synthesis (NS), with its emphasis on designed models and what some have termed ‘mathiness’.^1,2
2. How can it be possible to re-engage Economics students with the social reality changes, and at the same time, how can it be improve their professional competencies in a digital way? This paper offers an active learning framework built on real-world data platforms such as FRED, which is maintained by the Federal Reserve Bank of St. Louis.^3,4 Through this lens, we can develop an alternative to NS-centered Economics education—which faces both practical and academic challenges intensified by the Great Recession—by drawing on heterodox perspectives (Austrian Economics and New Institutional Economics) combined with active learning strategies that bring authenticity and digital relevance into the classroom.

This heterodox approach and experience remains replicable across different universities and countries because it builds on theoretical review paired with empirical demonstration rather than relying on controlled experiments or inductive surveys. It teaches students to work with universal theoretical and methodological frameworks.^5,6

Context

Since World War II, Macroeconomics has dominated the Economics discipline through the NS framework. However, this dominance has been challenged by both globalization and digitalization, creating crises in real economic systems and in classrooms alike.^5,7 The situation worsened with mainstream proposals from New Keynesians starting in the 1990s, who believed economic cycles could be controlled through econometric techniques borrowed from engineering.⁸ This period was optimistically labeled the Great Moderation.⁹ Yet ten years after the dot-com bubble—papered over with debt that created an artificial financial cycle—reality reasserted itself over the carefully designed models of mainstream macroeconomics. The result was renewed crises marked by unemployment, inflation, and ultimately a loss of credibility for Macroeconomics as both taught and practiced.^1,2,10 along with deeper concerns about normative Economics.^11–13 In response, we present a heterodox alternative rooted in active learning that combines Austrian Economics (AE) and New Institutional Economics (NIE), drawing on logical theories such as Austrian Business Cycle Theory (ABCT) and Austrian Capital Theory (ACT).^14,15

Theoretical and Methodological Review

This active learning experience, structured as a teaching case, employs heterodox theoretical and methodological frameworks to foster critical and creative thinking in the study of positive Economics. The starting point involves reviewing with students the economic landscape before and after the Great Recession. Monetary and credit expansion significantly impacted production processes and domestic economic structures in the years leading to 2008, generating pronounced cycles that have sparked renewed academic interest. ABCT has gained considerable traction among new generations of researchers eager to revisit the foundational contributions of thinkers like Mises,^16,17 Hayek,^18–20 and Rothbard.²¹ According to ABCT, when monetary expansions are channeled through loanable funds markets with the intention of suppressing interest rates, they induce artificial credit growth within fractional-reserve banking systems.

This credit expansion—lacking adequate prior savings—introduces major distortions in the economy’s production (or capital) structure. These distortions don’t reflect genuine resource availability or household consumption-saving preferences, meaning they must eventually correct through deep recession.^{14–15,22–25} Initially, an environment of abundant, cheap credit fostered by central bank monetary laxity creates conditions were insufficient savings cannot complete new production structures, while growing household debt finances immediate consumption. These pressures push interest rates upward and trigger what’s known as the ‘Ricardo effect’ or ‘readjustment effect’ (analyzed through the geek’n’talent methodology,^12,26–27 causing the economy to shift from artificial boom to adjustment and crisis.²⁸⁻²⁹

ABCT demonstrates that business cycles characterized by unsustainable growth emerge as consequences of artificial manipulation by monetary authorities. More specifically, bank credit expansion processes orchestrated by central banks within fractional reserve systems cause monetary interest rates to diverge from their natural levels. When this happens, interest rates fail to coordinate producers and consumers effectively, leading to the accumulation of investment errors in stages furthest from final consumption. The Austrian expansionary-recessionary cycle unfolds through several phases. First, the crisis begins with bank credit expansion and corresponding interest rate declines, all occurring within an environment of extremely loose monetary policy driven by central banks. Through this mechanism, monetary authorities disrupt the equilibrium between savings and investment by forcing monetary interest rates below their natural market level. Second, declining interest rates, the capital structure in economic system is shifted toward earlier, more time & capital-intensive production stages, while later stages oriented toward final consumption are neglected.

Unlike sustainable growth scenarios, however, this increased investment lacks support from accumulated voluntary savings. Third, artificial credit expansion distorts the production structure by creating a mismatch between the intertemporal plans of producers and consumers. This results in accumulation of long-term ‘malinvestments’ that markets cannot absorb. Additionally, rising inflation driven by abundant cheap money forces monetary authorities to raise official interest rates and restrict credit. Fourth, since the capital structure doesn’t correspond to actual resource availability (savings) or to individual consumption-saving preferences, it must ultimately adjust through severe recession. This adjustment is compounded by the impact of credit tightening and rising interest rates on the viability of investment projects. This sequence of stages suggests that changes in official intervention rates generally, and the slope of the yield curve specifically, can serve as relevant leading indicators of cycles induced by monetary and credit expansion processes.

The ABCT framework reveals that business cycles exhibiting unsustainable growth patterns arise because monetary authorities actively manipulate these patterns. Bank credit expansion through central bank actions within fractional reserve systems causes interest rates to diverge from their natural levels. The interest rate mechanism loses its function of linking producers with both other producers and consumers, while investment errors accumulate in production stages distant from final consumption. The Austrian expansionary-recessionary cycle consists of identifiable stages that follow this progression: First, bank credit expansion triggers declining interest rates while central banks maintain extremely accommodative monetary policies. The monetary authority disrupts the savings-investment equilibrium by manipulating interest rates below their natural market value. Second, reduced interest rates lead producers to allocate capital resources toward initial production stages that are time-consuming and capital-intensive, while abandoning stages oriented toward final consumption.

This investment growth differs from typical sustainable growth patterns because it lacks foundation in voluntary savings accumulation. Third, artificial credit expansion creates production distortions by generating mismatches between producer and consumer time-based planning. This results in unmarketable long-term investments. Meanwhile, excessive money supply generates inflation, which compels monetary authorities to increase interest rates and restrict credit availability. Fourth, the capital structure experiences deep recession because it fails to align with genuine resource availability and individual consumption-saving choices. Credit reduction combined with increasing interest rates threatens the sustainability of investment projects. This sequence of events demonstrates that official intervention rates, together with yield curve slopes, function as important predictive indicators of cycles induced by monetary and credit expansion.

Empirical Illustration & Digital Literacy: Fred Tools & Students’ Re-Engagement

Digitization has dramatically increased access to open databases that could modernize and transform Economics teaching, creating enriched connections between theory, data interpretation, and practical application. Yet many introductory and intermediate economics textbooks and teaching methodologies still emphasize abstract mathematical models and synthetic diagrams, neglecting skills like managing, analyzing, and interpreting economic and financial data.^3–4,31 The absence of realistic Economics teaching environments can influence labor market outcomes. Torbet³² identifies ten qualities that define a good economist, with the first being the ability to handle numerical databases and interpret visual data and graphs. Given that neuroscience research shows our brains process 90% of information visually and process images 60,000 times faster than written documents, we propose studying the macroeconomics curriculum using real economy data and graphic representations supported by digital transformation and paradigm shifts.^7,33–35 This initiative aligns with contributions from authors like Hyerle,³⁶ who advocates for visual methods as essential learning modalities in education more broadly.

FRED^® consists of freely accessible online tools enabling students to search, visualize, transform, analyze, and download economic and financial data.³⁷ It offers extensive alternatives as a pedagogical tool for learning both economic theory and applied economics. FRED^® minimizes the time teachers and students spend searching for and mining web data for representation and subsequent analysis. Additionally, materials facilitate planning pedagogical strategies such as flipped classrooms,³⁸ collaborative work,^39–40 or interactive demonstrations,⁴¹ all creating active learning environments. This teaching approach doesn’t aim to replace mainstream Economics study with its designed models. Rather, it seeks to re-engage students in two ways: first, by explaining economic theory through empirical illustrations based on real data; second, by training students for professional futures (for example, learning to read graphics or figures and building graphics using real data). In this sense, this active learning experience aims to provide greater meaning and purpose to Economics study (see Table 1).

Table 1: Educational objectives.

General Objectives

1. Access and organize existing knowledge: economic research data and its sources. The knowledge and handling of the data of the real economy allow the student to have additional information that is very relevant, which is usually ignored in most economic theory courses.

2. Interpret existing knowledge. The development of tasks with accurate data not only facilitates the student’s ability to obtain a better understanding of real-world problems. It also helps him to interpret the economic variables and abstract relationships that underlie the usual theoretical models.

3. Interpret and manipulate quantitative data. The objective is achieved naturally by working with historical data series and observing their evolution over time. In this case, the detection of regularities in the individual behavior of the variables, or correlations between them, does not require resorting to the use of econometric techniques, so this method is recommended at the initial and intermediate levels of university degrees in Economy and ADE.

4. Apply existing knowledge. Using economic data from the real world allows the student to apply the knowledge acquired in the classroom (generally in the form of theoretical models) to the analysis of the economic problems of each country. The development of this competence (learning objective) is essential for the future exercise of professional activity as an economic analyst.

Special Objectives

1. Teach the student to create, manipulate, and interpret graphs of economic data. These data correspond to the economic variables that are discussed in class.

2. It makes it easier for the student to discover trends and regularities in the cyclical behavior of the variables under study and the possible existence of correlation and causality relationships between them. In addition, the representation of the temporal profile of the economic variables allows us to verify that some act as accurate «advance indicators of the cycle».

3. Lastly, the students discover the usefulness of the skills achieved due to their effort and involvement. The knowledge obtained through the development of active and experimental learning activities places students in an advantageous position to formulate a professional diagnosis of the different scenarios and design the appropriate economic policy strategies required by each situation.

Source: Own elaboration.

Active Learning Experience for Digital Literacy on Real Macroeconomics: Literature Review

Hansen^42–44 argues that pedagogical use of real-world economic data constitutes a first-order active learning tool, highlighted by numerous specialists in economic education. The Emeritus Professor of Economics at the University of Wisconsin-Madison identifies skills that university students should acquire by graduation, including: first, accessing existing knowledge and organizing it effectively; second, analyzing and using quantitative data. The first skill requires scrutinizing economic data and data sources while discovering information about their creation, construction, and meaning. The second involves explaining how to understand and interpret numerical data found in published tables, identifying patterns and trends in existing data to illustrate economic problems, and describing connections between different quantitative variables such as unemployment, prices, and GDP.

In a publication from the same year, Simkins & Maier⁴⁵ observe that most economic theory courses are encyclopedic and barely manage to deepen understanding of economic thought—an objective that nevertheless must be achieved as a priority in Economics students’ learning processes. Learning to ‘think like an economist’ requires going beyond superficial study of subject contents, solving numerical problems, or answering multiple choice questions. Students need to examine trends and correlations in economic variables’ behavior and apply economic theory insights to real-world problems. They require expert guidance to help them organize ideas, structure knowledge, and build confidence in skills developed and connected with real-world problems and issues. Mendez-Carbajo^3,4 emphasizes that using real-world economic data for pedagogical purposes favors active learning techniques defended by renowned academics in economic and financial education.

Active learning processes based on autonomous resolution (guided by teachers) of practical cases analyzing economic variables’ behavior in sustainable growth scenarios or across the four phases defining an economic cycle help undergraduate students better understand the world around them. Through management and direct observation of accurate economic data, the teaching-learning method proposed in this work achieves four of seven educational objectives proposed by Hansen^42–44 in the spirit of taxonomy developed by Bloom.⁴⁶ The method presented in this paper has been used for two academic years in teaching subjects including Introduction to Economics, Political Economy, and Macroeconomics (across degrees in Economics, Business Administration and Management, Law, and Political Science), delivered through parallel workshops and seminars (2021, 2022–23, and 2023–24).

This initiative has been supported by a research group (Grupo de investigación consolidado para el Estudio y seguimiento del ciclo económico de la Universidad Rey Juan Carlos) and a teaching innovation group (Grupo de Innovación Docente Consolidado en Tecnologías de la información y comunicación y tecnologías del aprendizaje y conocimiento para la mejora de los estudios de ciencias de la economía y de la empresa)  with three grants from Educational Innovation Projects (Calls for Educational Innovation Projects from URJC, 2021–22, 2022–23 & 2023–24, see annexes). It was launched during the 2021-22 academic year within the Department of Applied Economics at King Juan Carlos University (Spain), with support from Dr. Mendez-Carbajo (senior specialist in economic education at the Federal Reserve Bank of St. Louis, USA) and Dr. Dierks (Professor of Finance & International Capital Markets at Lübeck University of Applied Sciences, Germany).

Each proposed activity responds to an active and experimental learning strategy where students must complete four stages (Table 2). First, students answer an online test-type questionnaire for control purposes. This stage aims to determine students’ previous knowledge of the research topic. In the second stage, students must build and interpret different graphs (the number varies depending on the activity) using the FRED^® interface. Professors prepare documents that: first, provide brief introductions to problems; second, describe variables to be found and graphed; third, formulate short-answer questions intended to guide and facilitate interpretation and understanding of figure contents; and fourth, ask students to write reports collecting main results of their work plus proposals for possible economic policy solutions (when activities require this). After delivering this activity, students must answer the online test again. This control tool determines whether the teacher’s active learning process has objectively impacted student training. Finally, in the fourth stage, results and practical applications of the activity are discussed in the classroom. It’s advisable for teachers to suggest discussion topics that illustrate, reinforce, and consolidate knowledge acquired by students.

Table 2: Stages in the active learning experience.

Stage 1: answer the on-line test for initial control

Stage 2: activity development (work guide): a) search for variables and construction of figures b) interpretation and analysis of graphical data c) preparation of a report with results and proposals

Stage 3: repetition of the initial online test

Stage 4: discussion of results and practical applications in the classroom and in the future professional field

Source: own elaboration.

Active Learning Experience Applied: Phillips Curve Review with FRED (from normative to positive Economics)

Mainstream Macroeconomics employs the Phillips Curve for its normative Economics to explain the inverse relationship between inflation and unemployment. In a paper published in the journal Economica, William Phillips⁴⁷ documented a non-linear negative correlation between wage inflation rates and unemployment rates for the UK (1861–1957), which Samuelson & Solow⁴⁸ then transplanted to the USA (1959–1969). The Phillips Curve became a key tool in how Macroeconomics was conceived during the 1960s. This empirical relationship suggested that governments could lower unemployment rates if they were willing to accept higher inflation costs. Many economists treated this relationship as a universal law, even though it was a finding restricted to specific countries and historical periods.

Friedman^49–50 explained the formulation of the natural unemployment rate hypothesis and the expectations-augmented Phillips Curve model. Lucas⁵¹ developed a general equilibrium model with rational expectations and imperfect information (generating money illusion problems), along with the problematic stagflation scenario of the 1970s, which led to rejection of the Phillips Curve as a stable relationship⁵² that could be extrapolated to any historical period and place. However, despite these contributions, most modern Economic Theory textbooks maintain the Phillips Curve as a reliable empirical relationship and a key piece in formulating short-term macroeconomic models. Today, thanks to FRED, real data review with students in the classroom becomes possible.

The Phillips Curve contemplates two objectives pursued by the Fed’s dual monetary policy: achieving maximum sustainable employment levels and price stability at values close to 2%. However, these objectives are often incompatible, creating an economic policy dilemma. Based on Samuelson and Solow’s version of the Phillips Curve, prioritizing employment leads to executing inflationary policies during recessive periods. Conversely, if the objective is stabilizing inflation, required monetary restriction policies tend to raise unemployment rates. Academic research provides different political and economic explanations for instability in the relationship between inflation and unemployment rates since the early 1970s. First, there has been a change in FED monetary policy priorities. In October 2018, James Bullard, president and CEO of the Federal Reserve Bank of St. Louis, explained in an interview with US National Public Radio that the Fed had chosen to stabilize the inflation rate at 2% over the last two decades, which is why an inverse relationship between inflation and unemployment rates could no longer be discussed.

With a priority inflation target of 2% (as opposed to discretionary use of monetary policy as an instrument to stabilize economic cycles), the Fed stopped applying loose monetary policies during periods characterized by high unemployment rates. This would justify that inflation rates between 3% and 4% in 1988–2005 and 2% and 3% in 2006–2019 were compatible with unemployment rates between 3.9% and 7.6% and 3.6% and 9.9%, respectively. Accordingly, the Fed’s monetary policy and market credibility would have caused a downward shift and flattening of the Phillips Curve⁵³ around inflation rates between 1% and 3% in 2000–2019, as shown in Figure 1.

Economic explanations come from expectation formation processes.^49–51 Before the stagflationary crisis of 1972–1981, Friedman rejected the idea that the Phillips Curve presented a negative and stable long-term non-linear relationship between inflation and unemployment rates. They warned that this relationship was merely statistical and didn’t imply causality, so its use to support executing inflationary policies aimed at reducing unemployment rates in recessive cycle phases wasn’t justifiable. Furthermore, they argued that the main problem with Phillips’s 1958 finding was that salary variation rate determination didn’t depend on inflation, which was equivalent to accepting that agents acted irrationally and suffered from monetary illusion.⁵⁴ In other words, they made labor decisions based on nominal salaries without paying attention to price behavior. Indeed, the Phillips Curve didn’t consider inflation expectations, which could be important in wage negotiation processes. In contrast, Friedman⁴⁹ claimed that changes in inflation expectations could alter the supposedly stable relationship^47–48 between inflation and unemployment. This insight led him to formulate the expectations-augmented Phillips Curve model, which introduced adaptive expectations theory into the Phillips Curve.

Friedman’s model rejects the existence of a long-run trade-off between inflation and unemployment. He establishes that permanently reducing unemployment rates by managing inflationary policies is impossible. In the long term, once agents adjust their expectations to new inflation rates, the only impact of these measures will be increases in general price levels, while unemployment rates return to their natural or equilibrium levels. In this case, we could no longer speak of Phillips Curve flattening but rather verticalization at the natural unemployment rate height.

The third line of explanation lies in the rational expectations hypothesis and the so-called Lucas critique,⁵¹ which warns of the impossibility that central banks can permanently lower unemployment rates through systematic use of inflationary policies in a model of optimizing agents with rational expectations and complete information. It affirms that all information related to using monetary policy rules aimed at lowering unemployment rates will be integrated by agents into their expectation formation and therefore into their decision-making processes, which will end up neutralizing the real impact of these measures.^51,55 This result bears direct connection to the ‘Lucas critique’. Lucas⁵¹ criticizes the usual practice of evaluating the impact of different economic policy measures by estimating econometric models that assume parameters and constants remain stable when governments alter the course of their policies. He states that parameters cannot remain unchanged. Under rational expectations, individuals modify their behavior in response to any known political change affecting their decision-making processes.

Each economic policy regime has its own parameters, whose values depend mainly on individual reactions provoked by each alternative scenario. Lucas’s reasoning has important implications for economic policy design and execution. Since governments cannot predict the impact of changes in policy intensity and direction on model parameters they manage, econometric methods don’t allow anticipating repercussions of variations in economic policy decisions.⁵⁴ The impact of expectancy formation processes in decision-making and the ‘Lucas critique’ lead us to reject the possibility of any automatism that allows observing the Phillips Curve as a lasting and stable relationship. Finally, Lucas’s critique evokes, to some extent, ideas developed earlier by Austrian School economists. The Austrians argue that since empirical phenomena are continuously variable, in social facts there are no constancy relationships (all are variables), which hinders traditional econometric analysis purposes based on extrapolating past trends into the future.^5,56–57

Does the Phillips Curve show a negative and stable long-run relationship? Can curve flattening be observed in recent decades? To answer these questions, we propose examining the relationship between inflation and unemployment rates (in annual data) for a selection of countries (UK, Germany, France, Italy, Spain & Greece) from 1983–2021 (see Figures 2 and 3). These countries can be divided into two groups based on their central banks’ credibility and success in fighting inflation before the euro launch in January 1999. Thus, states with the lowest and most stable inflation rates are UK, Germany & France. In contrast, countries with the highest and most volatile inflation rates are Greece, Spain, and Italy.

The following figure reveals changes in the Phillips Curve between 1983–2021 for these countries. These graphs allow us to notice, as in the United States case, that all nations under study present downward displacement and flattening of their respective Phillips Curves. These movements are consequences of changes in monetary policy priorities. In recent decades, establishing a ‘credible’ 2% inflation target has caused downward Phillips Curve movement, which has tended to flatten around projected inflation. Under inflation-targeting monetary policy effects, inflation rates have been lower, less volatile, and closer to desired inflation.⁵³ These effects have been critical factors in flattening the Phillips Curves of European nations. Phillips Curve flattening has been especially evident in countries that have suffered higher and more unstable inflation rates: Greece, Italy, and Spain. There’s also an essential change in the UK case. Finally, it should be noted that Phillips Curve descents for Germany and France have been very similar.

According to these results, we can conclude that the Phillips Curve doesn’t indicate an inverse, permanent, and stable long-term relationship between inflation and unemployment (it shows a normative fallacy). In recent decades, the Phillips Curve has flattened due to an anti-inflationary turn in monetary policies.^58–60 This conclusion has a couple of implications. On one hand, effective and credible monetary policy can push the Phillips Curve slope to zero, which means nullifying the inverse relationship between inflation and unemployment rates underlying Keynesian business cycle models. On the other hand, as a result of the previous point, it’s doubtful that nowadays reliable signals from the Phillips Curve can be resorted to for guiding monetary policies.⁵³ According to heterodox approaches for positive Economics reading close to reality, it would be better to attend to Okun’s rule and its misery index.^61–63

Conclusion

This work has served to review mainstream Macroeconomics while offering a heterodox reading based on capital and cycle theory revised and adapted to the current context of poly and permacrisis, promoting digital literacy and the geek’n’talent method. For this goal, this work provides illustration with FRED. Considering the relevance of digital alphabetization skills for real Macroeconomics, which align with contributions of renowned academics in the field of education generally and economic and financial education particularly, this work proposes an autonomous, active, and experimental learning strategy based on managing interactive media (statistical and graphic) facilitated by the FRED^® platform of the Federal Reserve Bank of St. Louis.

These online resources can be extremely useful for teachers and students in implementing new active teaching-learning methods (as empirical illustration for heterodox review). Although FRED^® data can be used as input for pedagogical econometric analysis techniques, the didactic method presented in this work focuses on using economic time series strategies (with data from reality development). Integrating the FRED^® platform as a pedagogical resource in economic theory and applied economics allows for covering competencies for current university graduates. Specifically: accessing and organizing existing knowledge, interpreting and managing this knowledge, interpreting and manipulating quantitative data, and applying existing knowledge.

In this sense, this work describes a learning methodology on macroeconomic issues where students learn to track information from statistical data and create graphs (for empirical illustrations of economic principles.^64–66 Students read, understand, and interpret the content of those graphics, and apply obtained information to analyzing real economic problems and designing possible economic policy decisions. The proposed system allows us to recognize relevant economic relationships without resorting to econometric analysis. This makes it an ideal teaching-learning method for first courses (introductory and intermediate) of Economics degrees. The proposed tasks help develop intuition and analytical skills, allowing students to achieve greater understanding of abstract concepts characteristic of subjects like macroeconomics.

Indeed, using FRED^® makes it easier for teachers to place abstract theories and models in real and relevant environments. Using real-world examples inspires classroom discussions, which also boosts students’ communication skills. After adopting this methodology in recent courses, either in undergraduate courses or in specialized seminars (at King Juan Carlos University), we have observed that students become more involved and committed to their training processes upon verifying the practical use of their knowledge. Likewise, they expand their skills in reading, analyzing, and interpreting data and economic and financial representations, which allows them to achieve greater understanding of theoretical models studied. Finally, handling statistical data establishes bases of new skills for resolution of more sophisticated econometric analysis activities in more advanced courses of study plans. For future research, we would like to extend this active learning experience, based on heterodox approaches and managing FRED, to other topics and promote transition from normative to positive Economics.

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Cite this article as:
Sánchez-Bayón A, M Neira MAA and Segovia FJS. How to Rediscover Positive Economics for Students? Active Learning Experience (Teaching Case)*. Premier Journal of Science 2025;16:100200

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