How Marzano and Kendall’s Taxonomy Rethinks Learning

In education, it is very common to use taxonomies to organize types of knowledge and thus define clear objectives, structure classes and activities, and design evaluations. The most popular of these is Bloom’s taxonomy, a classic model developed in the 1950s that organizes cognitive development into six levels of complexity. This is based on the idea that learning is not only about memorizing information, but also about understanding it, applying it, analyzing it, creating new ideas, and evaluating arguments. 

Thanks to this model, teachers worldwide began designing educational activities and objectives that promote deeper, more meaningful learning. However, over time, research in education and cognitive sciences has shown that acquiring knowledge involves much more than developing cognitive skills. Aspects such as motivation, emotions, self-regulation, and reflection must be considered, as they affect how we process and use information. 

In this context, the proposal by Robert Marzano and John Kendall can be understood as an evolution or extension of Bloom’s taxonomy, since it incorporates dimensions related to metacognition and awareness of students’ own educational processes, as well as the emotional factors that influence them. Their taxonomy expands the traditional view of learning and offers a more comprehensive view of how people acquire, process, and use knowledge.

Learning is not the same as it was in the 50s

Bloom’s taxonomy emerged at a time when much of the knowledge acquired came from classrooms, books, and traditional media. Memorizing information and developing cognitive skills that would allow students to understand and apply what they learned were considered most fundamental, since information was not as accessible or as immediate as it is today. The classroom served as a space for the transmission of content, with teachers as one of the main sources of knowledge.

However, our relationship with information has changed radically in recent decades. The internet has made it possible to access virtually unlimited content, especially now with tools such as AI that produce answers, summaries, and explanations in a matter of seconds. Now, memorizing data is not enough; the challenge is to know how to interpret, question, and ethically use those results. 

This is where Marzano and Kendall’s taxonomy becomes relevant, as it is not limited to organizing cognitive skills, but also attempts to explain how learning works from a more human context. For these authors, acquiring knowledge goes beyond memorizing or analyzing information. It includes mental processes related to awareness of one’s own thinking and the student’s emotional disposition toward engaging in their education.

Marzano and Kendall’s Taxonomy: Emotions Also Teach

Unlike Bloom, who presents a progressive hierarchy of cognitive skills, these authors propose a model in which different processes interact. For them, learning is organized around three major mental dimensions: the cognitive, the metacognitive, and the self-system (or internal system). Each one serves a different function, and together they help us understand why education involves so much more than memorizing.

The cognitive system refers to the mental processes involved in processing data, such as remembering information, understanding concepts, analyzing ideas, and using what we know to solve problems. It is the part most closely related to the levels of Bloom’s taxonomy.

Then there is the metacognitive system, which is responsible for supervising and regulating learning. This is where processes such as setting goals, monitoring understanding, identifying errors, and deciding on strategies for appropriating knowledge occur. In other words, it is the system responsible for developing awareness of one’s own thinking.

Finally, the internal system integrates emotions and motivation into this process. Here, the brain assesses whether a task is worth the attention and effort, considering factors such as how interesting the content seems, the student’s perception of their abilities, and how important the activity is to them.

In addition, within the cognitive system, Marzano and Kendall describe different levels of knowledge processing that explain the student’s interaction with information and the complexity of the mental tasks involved. These levels help clarify that not all cognitive activities require the same effort or depth. For example, remembering the periodic table requires a different kind of processing than analyzing the causes of social conflict or using that knowledge to solve an everyday problem.

The first level is retrieval, in which learning involves memorizing information, recognizing concepts, or identifying specific facts, such as historical dates, names, or events. Although this is often considered a basic skill, it is of utmost importance as a starting point for more complex processes.

Second is comprehension, in which information is no longer merely remembered and begins to acquire meaning. At this level, a student can explain a concept in their own words, summarize a reading, or relate new data to previous material. For example, memorizing a mathematical formula does not necessarily imply understanding what it represents or why it works. Comprehension is evident when the student can interpret the procedure and explain its logic, rather than simply repeating it from memory.

In third place is analysis, which involves comparing ideas, identifying patterns, classifying information, and detecting relationships between concepts. Analysis requires critical thinking because it involves questioning and examining information beyond its superficial meaning, for example, detecting biases in news or comparing different sources. 

The fourth level is applying knowledge, where the student uses their knowledge to solve problems, make decisions, or face real situations. Here, the information ceases to remain only on the theoretical plane. A student can understand how percentages work, but applying this understanding in everyday contexts, such as calculating discounts or tips, is more challenging.

Although these levels are similar to Bloom’s, Marzano and Kendall argue that learning does not occur solely in a linear sequence of increasing difficulty; rather, different mental systems interact constantly during knowledge acquisition.  One of the most innovative aspects of the taxonomy of these two authors is the incorporation of emotional factors and motivation within learning. For a long time, education separated cognitive processes and emotions, as if education depended solely on rational abilities. However, factors such as anxiety, frustration, interest, and confidence directly affect how a person understands new concepts. 

Marzano and Kendall integrate these factors within the internal system, a dimension in which a student’s emotions, motivation, and self-perception are considered. From this perspective, before engaging in an activity, people evaluate whether the task is interesting, relevant, or too complicated. 

The clearest example is when a student repeatedly hears that “he is not good at math,” begins to lose confidence in himself, and develops rejection or anxiety towards that subject, even if he has the necessary skills to understand it. In the same way, a person who is confident and emotionally motivated or inspired usually shows greater willingness to persist with complex tasks and to face academic challenges. 

The incorporation of these elements is especially relevant in a context in which digital stimuli constantly fragment attention. Social networks, electronic notifications, and digital platforms continually compete for students’ attention, making it increasingly important to develop self-regulation and concentration skills.

Another central concept in this taxonomy is metacognition, the ability to reflect on one’s own thinking and to supervise the process of acquiring knowledge. This skill is extremely relevant today due to the growth of autonomous learning and digital tools, such as acquiring knowledge through online courses, videos, or social networks without a teacher present. In these contexts, knowing how to manage one’s own educational process is essential. 

In addition, generative artificial intelligence has generated new discussions about what it really means to learn. Tools capable of generating immediate answers can facilitate academic tasks, but they can also generate an illusion of acquired concepts. Reading an explanation or copying an answer does not necessarily imply understanding it. The difference is no longer just in accessing information, but in knowing what to do with it.

Although Marzano and Kendall’s taxonomy is often used to design activities, learning objectives, and assessments, its importance goes beyond educational planning. This model also helps us understand how students relate to their educational process in increasingly complex, information-saturated contexts.

For example, the taxonomy can help teachers design activities that go beyond memorization. A retrieval-focused goal might require students to identify basic concepts, while a knowledge application activity would involve solving real problems or making decisions based on the information learned.

In addition, the model allows the incorporation of strategies related to reflection and self-regulation, such as asking the student to evaluate their own learning processes or to identify which strategies were most useful for them in understanding a topic.

Although Bloom’s taxonomy remains one of the most influential educational tools of the last century and remains fundamental for structuring learning objectives and activities, proposals such as those of Marzano and Kendall allow us to broaden this vision and incorporate new aspects that were left out of traditional educational discussions.

Beyond memorizing information or developing cognitive skills, the latter taxonomy reminds us that education also involves emotions, motivation, mental habits, and self-regulation processes. In other words, learning does not only mean memorization. It also entails how people perceive their abilities, manage their attention, and construct meaning from the knowledge they process.

In an age marked by artificial intelligence, information overload, and the constant need to learn new things, perhaps the greatest educational challenge is no longer about teaching more content, but also about helping people understand how they learn, develop critical thinking, and build a more conscious relationship with knowledge.

Translation by Daniel Wetta

Paulette Delgado

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This article from Observatory of the Institute for the Future of Education may be shared under the terms of the license CC BY-NC-SA 4.0