Developing Curriculum for Deep Thinking: The Knowledge Revival (PDF)

SpringerBriefs in Education Tim Surma · Claudio Vanhees · Michiel Wils · Jasper Nijlunsing · Nuno Crato · John Hattie · Daniel Muijs · Elizabeth Rata · Dylan Wiliam · Paul A. Kirschner Developing Curriculum for Deep Thinking The Knowledge Revival SpringerBriefs in Education We are delighted to announce SpringerBriefs in Education, an innovative product type that combines elements of both journals and books. Briefs present concise summaries of cutting-edge research and practical applications in education. Featuring compact volumes of 50 to 125 pages, the SpringerBriefs in Education allow authors to present their ideas and readers to absorb them with a minimal time investment. Briefs are published as part of Springer’s eBook Collection. In addition, Briefs are available for individual print and electronic purchase. 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Tim Surma Claudio Vanhees Thomas More University of Applied Thomas More University of Applied Sciences Sciences Antwerp, Belgium Antwerp, Belgium Michiel Wils Jasper Nijlunsing Thomas More University of Applied Thomas More University of Applied Sciences Sciences Antwerp, Belgium Antwerp, Belgium Nuno Crato John Hattie University of Lisboa The University of Melbourne Lisbon, Portugal Parkville, VIC, Australia Daniel Muijs Elizabeth Rata Queen’s University Belfast The University of Auckland Belfast, UK Grafton, New Zealand Dylan Wiliam Paul A. Kirschner UCL Institute of Education Open University of the Netherlands University College London Heerlen, The Netherlands London, UK Thomas More University of Applied Sciences Antwerp, Belgium Foreword This book, written by experts from eight nations, explains why imparting specific shared knowledge in early grades is important for achieving high levels of citizen competence and high levels of equality and equity. As the result of a worldwide collaboration, it is an open-access book. It is free to everyone everywhere. Special thanks is owed to Prof. Paul Kirschner of the Netherlands for herding into a unity these far-flung distinguished scholars, scientists, and public servants. Thank you, Paul! This international effort may come to be seen in the future as the sign of a new beginning for teaching young pupils—a farewell to individualistic “child-centered” doctrines, and a ringing in of a new, more effective early education in modern democracies. The science presented in the book is up to date, some of it very recent. Its general principles go back several years. These had their most memo- rable expression in a 1994 resolution approved unanimously by the Parliament of Norway—translated into English as follows by Prof. Gudmund Hernes: It is a tenet of popular enlightenment [i.e., the enlightenment of a whole people] that shared frames of reference must be the common property of all the people—indeed must be an integral part of general education—to escape avoidable differences in competence that can result in social inequality and be abused by undemocratic forces. Those who do not share the background information taken for granted in public discourse will often overlook the point or miss the meaning. Newcomers to a country who are not immersed in its frames of reference often remain outsiders because others cannot take for granted what they know and can do; they are in constant need of extra explanations. Common background knowledge is thus at the core of a national network of communication between members of a democratic community. It makes it possible to fathom complex messages, and to interpret new ideas, situations, and challenges. Education plays a leading role in passing on this common background information—the culture everybody must be familiar with if society is to remain democratic and its citizens sovereign. I have not found a better summary of what this present collaborative effort has documented with the latest experimental results. A foreword is not the place to develop a full-throated attack on the individualistic “child-centered” approach of recent decades and its incorrect empirical assumptions. vii viii Foreword Such a polemical tone is far from the straightforward expositions of the distinguished writers from diverse lands who have produced this welcome gift. These scholars and scientists are neither accusatory nor polemical. They show from diverse angles and fields of research that the shared knowledge revival in early grades is wrongly conceived as politically conservative. Rather, the shared knowledge approach is an essential path to equity in a democracy. These scholars from multiple nations show in detail with the latest scientific findings why a carefully sequenced shared-knowledge curriculum in early grades is essential to fairness and essential also to the complex cognitive skills demanded by modernity. I’m grateful for the kind mentions of my own work in this book and for the invita- tion to write this foreword. My years in the vineyard prompt me to add this observation to the reader of this book: when the term “knowledge” is used in describing the deci- sive experimental results cited in this book, it will be helpful for readers to think “shared knowledge,” to grasp that knowledge possession and knowledge use often involves a language transaction demanding silently shared background knowledge between writer and reader, teacher and pupil. Language is the means by which we humans co-ordinate our shared knowledge to create nations and achieve common goals. That’s why the word “shared” is important to connect with the word “knowledge.” Knowledge gets fixed in our minds and communicated to others across generations through shared language, which itself depends upon shared knowledge—even when that knowledge is unstated. Human tribes flourished over other creatures in evolu- tionary time (as evolutionary psychologists have explained) because shared knowl- edge enables shared language which enables a human tribe to transform itself into a massive creature that can defeat and eat large creatures. Here’s a memorable passage from the evolutionary psychologist Joseph Henrich: The disappearance of many megafaunal species eerily coincides with the arrival of humans on different continents and large islands. For example, before we showed up in Australia around 60,000 years ago, the continent was home to a menagerie of large animals, including two-ton wombats, immense meat-eating lizards, and leopard-sized marsupial lions. These, along with 55 other megafaunal species, went extinct in the wake of our arrival, resulting in the loss of 88% of Australia’s big vertebrates. It seems heartless to recount such a story until we consider that these lizards and marsupial lions did not achieve their immensity by picking berries. Our tribal festivals would have saved countless smaller creatures from the fearsome predators. From the viewpoint of the human tribe, the cooperative principle based upon language and shared knowledge was achieved by schooling the tribe’s children. The tribal human school was fostered by shared language built up not only for wombat-hunting techniques but also for other elements of cumulative tribal knowl- edge through the agency of language. A distinguished evolutionary psychologist, Michael Tomasello puts the case this way: Cumulative cultural evolution takes place when the inventions in a cultural group are passed on to the young with such fidelity that they remain stable in the group until a new and improved invention comes along (the so-called ratchet effect). Modern humans had a stronger ratchet Foreword ix than early humans and apes because they had—in addition to powerful skills of imitation— proclivities both to teach things to others and also to conform to others when they themselves were being taught. And so it is, with this wave of group-mindedness and conformity, that we get the possibility of cultural groups creating and constantly improving their own cognitive artifacts—from procedures for whale hunting to procedures for solving differential equations. (M. Tomasello. A Natural History of Human Thinking, 2014) That shared knowledge principle holds true in human schooling from the primor- dial cave to the early American “Common School” to the jazziest use of AI in the current classroom. Shared knowledge remains the foundation of human education. So, heartfelt thanks to the distinguished authors of this open educational resource, and especially to Paul Kirschner for selflessly bringing this book into being. Charlottesville, USA Prof. Dr. E. D. Hirsch Acknowledgements We thank Daniel Willingham, Dirk Van Damme, and Henk Byls for their feedback on earlier versions of the manuscript. xi Contents 1 Introduction.................................................... 1 References...................................................... 2 2 How Knowledge Matters......................................... 5 2.1 Knowledge Matters: A Learning Perspective.................... 6 2.1.1 A Basic Understanding of Human Cognitive Architecture... 6 2.1.2 How Can Prior Knowledge Facilitate Better Learning?..... 9 2.1.3 Why Complex Cognitive Skills Require Knowledge........ 11 2.1.4 Reading Comprehension............................... 14 2.2 Knowledge Matters: A Sociological Perspective................. 21 2.2.1 From Objectivist to Constructivist Thinking Perspectives... 22 2.2.2 Skills for the Twenty-first Century and Neoliberal Influences............................................ 23 2.2.3 Bringing Knowledge Back in........................... 25 2.3 Knowledge Matters: A Democratic Perspective.................. 27 2.4 How Knowledge Matters Summarised.......................... 30 References...................................................... 30 3 Knowledge and the Curriculum.................................. 37 3.1 Everything Starts with the Curriculum.......................... 37 3.2 Curriculum as a Pendulum.................................... 41 3.3 Towards the Best of Both Worlds: A Knowledge-Rich Curriculum................................................. 46 3.4 On Content-Richness........................................ 47 3.4.1 The Selection of Content............................... 47 3.4.2 The Basis of the Selection Process....................... 48 3.4.3 The Impact of Hierarchy and Structure in Knowledge and Sequence......................................... 50 3.4.4 The Relation Between Knowledge and Skills.............. 51 3.5 On Coherence............................................... 52 3.5.1 Horizontal Coherence................................. 53 3.5.2 Vertical Coherence.................................... 53 xiii xiv Contents 3.5.3 Coherence and Disciplinary Knowledge.................. 55 3.6 On Clarity.................................................. 59 3.6.1 The Importance of Clear Learning Goals................. 59 3.6.2 The Interpretation of Learning Goals..................... 60 3.6.3 The Importance of Good Alignment..................... 62 3.7 A Knowledge-Rich Curriculum and Student Achievement......... 68 References...................................................... 70 4 Concluding Remarks............................................ 75 References...................................................... 77 5 Executive Summary............................................. 79 5.1 How Knowledge Matters..................................... 79 5.1.1 Knowledge Matters: A Learning Perspective.............. 79 5.1.2 Knowledge Matters: A Sociological Perspective........... 80 5.1.3 Knowledge Matters: A Democratic Perspective............ 81 5.2 Knowledge and the Curriculum................................ 82 5.2.1 Everything Starts with the Curriculum................... 82 5.2.2 Curriculum as a Pendulum............................. 83 5.2.3 Towards the Best of Both Worlds: A Knowledge-Rich Curriculum........................................... 83 5.2.4 A Knowledge-Rich Curriculum and Student Achievement......................................... 84 5.3 Concluding Remarks......................................... 85 References...................................................... 85 Appendix: How is Knowledge Remembered?.......................... 89 References......................................................... 91 Chapter 1 Introduction Nearly all teachers and other stakeholders in education pursue a common aim: We want the students whom we teach and guide during their formative years to think deeply about what we teach them. We want them to be able to go beyond their current experiences and have a deep understanding of the world. We want to enable them to thrive and find their path through life, long after their formal education ceases. We want them to be able to think critically, work together, solve problems, read for understanding, and perform many other complex tasks. If we want students to be able to do all this, we should just include it in the curriculum and teach them, right? In this book, we discuss why this apparently obvious strategy of simply teaching children how to “think deeply” does not work, and we offer an alternative way forward. In recent decades, many trends in the curriculum have been observed, some- times collectively described as a curriculum turn. One of the characteristics of this turn is the frantic push to encourage skill acquisition with a focus on generic skills and competencies such as problem-solving, reading comprehension, collaboration, communication with each other, and so forth (Priestley & Biesta, 2013). Critics have argued that these trends have downplayed the importance of knowledge in the new curriculum (Rata, 2012; Wheelahan, 2010; Young, 2007) to the point of seeing it as either irrelevant or something that can be learned through the act of practicing these generic skills and competencies. To an extent, these criticisms have been supported by empirical evidence that shows a reduction in the specificity of content in curricula (and thus the acquisition of domain-specific knowledge) and a diminishing emphasis on the importance of knowledge in relation to general skills and competencies (Priestley & Sinnema, 2014). The continuing decline in reading comprehension scores, as well as in science and, more recently, mathematics across several OECD countries (OECD, 2023), has highlighted the need for a renewed focus on knowledge as a necessary foundation for teaching and acquiring complex cognitive skills. Additionally, a notable shift has been observed in the OECD discourse, in which the importance of disciplinary or subject-specific knowledge is now seen as a crucial fundamental basis for equitable © The Author(s) 2025 1 T. Surma et al., Developing Curriculum for Deep Thinking, SpringerBriefs in Education, https://doi.org/10.1007/978-3-031-74661-1_1 2 1 Introduction opportunities (OECD, 2019). This deviates greatly from previous OECD reports that prioritised generic skills and competencies (Hughson & Wood, 2022). Social realists (Barrett, 2024), sociological theorists who have emerged as successors of construc- tivist thinkers (Rata, 2024a), and several cognitive psychologists now agree that a curriculum rich in domain-specific knowledge is crucial if we hope to achieve equi- table opportunities for all. In line with the ideas of E. D. Hirsch (2016), they believe that focusing on rich and broad content knowledge ensures that all students, regard- less of background, have equal access to a foundational body of knowledge, which helps mitigating disparities and promotes a more inclusive educational experience. This book discusses the prominent role of knowledge in how we learn, think, read, understand, and solve problems. We draw ideas from cognitive psychology, educational psychology, sociology, and curriculum studies, and combine these ideas with case studies describing real-life classroom experiences. The publication seam- lessly aligns with a clearly observable global knowledge revival: various educational systems are re-evaluating the role of knowledge in their curricula, and in a growing number of academic and non-academic publications the role of knowledge to promote equity, unity and progress in a modern society is emphasised. Our goal is therefore to explain why a knowledge-rich curriculum is the soundest way forward to both effectively teach knowledge and complex skills in school. References Barrett, B. (2024). Rob Moore, social realism, and the sociology of education and knowledge. In E. Rata (Ed.), Research handbook in curriculum and education, Chap. 5 (pp. 79–87) Edward Elgar Publishing. Hirsch, E. D. (2016). Why knowledge matters: Rescuing our children from failed educational theories. Harvard Education Press. Hughson, T. A., & Wood, B. E. (2022). The OECD Learning Compass 2030 and the future of disciplinary learning: A Bernsteinian critique. Journal of Education Policy, 37(4), 634–654. OECD. (2019). Conceptual learning framework: Knowledge for 2030 concept note. https://www. oecd.org/education/2030-project/teaching-and-learning/learning/knowledge/in_brief_Knowle dge.pdf OECD. (2023). PISA 2022 Results (Volume I): The state of learning and equity in education, PISA, OECD Publishing. Priestley, M., & Biesta, G. (Eds.). (2013). Reinventing the curriculum: New trends in curriculum policy and practice. A&C Black. Priestley, M., & Sinnema, C. (2014). Downgraded curriculum? An analysis of knowledge in new curricula in Scotland and New Zealand. In Creating curricula: Aims, knowledge and control (pp. 61–86). Routledge. Rata, E. (2012). The politics of knowledge in education. British Educational Research Journal, 38, 103–124. Rata, E. (2024). Introduction: Social realism, didaktik, and cognitive science in curriculum and education. In E. Rata (Ed.), Research handbook on curriculum and education (pp. 1–18). Edward Elgar Publishing. Wheelahan, L. (2010). Why knowledge matters in curriculum: A social realist argument. Routledge. Young, M. (2007). Bringing knowledge back in: From social constructivism to social realism in the sociology of education. Routledge. References 3 Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. Chapter 2 How Knowledge Matters Abstract We, in education, all have a common aim: We want students to be able to think deeply about what we teach them, go beyond their current experiences, and have a deep understanding of the world. We want to enable them to think critically, work together, solve problems, read for understanding, and carry out complex cognitive tasks. If we want students to be able to do all this, we should just include itt in the curriculum and teach them, right? In this chapter, we discuss why this apparently obvious strategy of simply teaching children how to think deeply does not work, and offer an alternative way forward. This chapter discusses the prominent role of knowledge in how we learn, think, read, understand, and solve problems. Insights from cognitive and educational psychology, sociology, and curriculum studies are used to underpin the current knowledge revival that is widely being observed in education. Keywords Knowledge-rich · Education · Knowledge acquisition · Complex cognitive skills · Democratic citizenship “What we know is a drop what we don’t know is an ocean” (Isaac Newton). What you know determines what you see (Kirschner, 1991). Another and possibly more prosaic way of saying this is: Knowledge begets knowing. The more extensive one’s knowledge-base is in terms of both its breadth and its depth the more easily new knowledge is acquired and remembered (Alexander et al., 1994; Ausubel, 1968; Shapiro, 2004). Knowledge is also essential for carrying out the complex cogni- tive skills such as critical thinking (you think critically about something) problem- solving (you solve problems in something) and reading comprehension (you compre- hend something written about something). The more robust one’s knowledge-base the more seamlessly and efficiently these complex cognitive skills—which require students to “think deeply” and are precisely those that teachers aim to develop in their students—are acquired and can be carried out. The subsequent chapter describes © The Author(s) 2025 5 T. Surma et al., Developing Curriculum for Deep Thinking, SpringerBriefs in Education, https://doi.org/10.1007/978-3-031-74661-1_2 6 2 How Knowledge Matters the significance of knowledge from cognitive sociological and democratic perspec- tives. These different perspectives draw on various research traditions each with its own procedures and standards for what constitutes a convincing argument. What is presented is therefore a mixture of scientific and humanities approaches all serving the objective of illustrating the critical role of knowledge in promoting deep thinking. 2.1 Knowledge Matters: A Learning Perspective 2.1.1 A Basic Understanding of Human Cognitive Architecture We need to examine our cognitive architecture to understand where knowledge fits within human cognition. Some forms of knowledge appear to be acquired effortlessly due to our evolutionary development over numerous millennia. Such knowledge, resulting in skills like communicating with those around us, speaking our mother tongue, recognising facial expressions and physical signals, recognising others, understanding the relation between an incline and things rolling down from it … is recognised by evolutionary psychologists as being biologically (or evolutionarily) primary knowledge (Geary & Berch, 2016). In contrast, a second category consists of knowledge that is more recent in nature. It was not until a couple of hundred or even thousand years ago that most people in a few societies learned to read and write, solve algebraic problems, and engage in discussions about geographical, scientific, political, cultural, and historical phenomena. This category of knowledge is known as biologically (or evolutionarily) secondary knowledge and, unlike our innate ability to seemingly effortlessly acquire the first category (without many/most of them a person could not live long enough to procreate), we lack a natural mechanism to just as effortlessly assimilate the second—more cultural—category through mere exposure. It must be consciously taught and effortfully learned, building upon, yet distinct from biologically primary knowledge. Schools were established to impart this biologically secondary knowledge that is rarely acquired spontaneously, as it is vital for functioning in contemporary societies. When we speak of knowledge in educational systems, we mainly refer to biologically secondary knowledge, and cognitive psychologists have been investigating how human cognitive architecture processes this information for many decades. Broadbent (1958) is considered one of the first scientists to use an information processing metaphor to portray the human attentional processing system. He postu- lated, around the same time as Miller (1957; The Magical Number Seven, Plus or Minus Two), that humans have a limited capacity to process information and, due to this limited capacity, a selective filter—acting like a bottleneck—is needed for information processing. He compared human information processing capacity to a limited amount of information that can be conveyed through a given channel at a 2.1 Knowledge Matters: A Learning Perspective 7 given time: “if we send a Morse code with a buzzer, we cannot send a dot and a dash at the same time, but must send them successively” (Broadbent, 1958, p. 5). Shortly thereafter, Atkinson and Shiffrin (1968) put forward a multi-store model of memory composed of a sensory memory where information from all of our senses enters memory; a short-term memory (STM) which receives and retains input from both the sensory memory and the long-term store; and finally this long-term store, where information that has been repeatedly rehearsed in the short-term store is permanently stored. Subsequently, Baddeley and Hitch (1974) proposed a new memory model that challenged the prevailing view of short-term memory. They suggested that short-term memory is composed of multiple, distinct components that work together, allowing us to hold information in our minds and manipulate it. This is what became known as working memory (WM). In their original work, they spoke of a verbal and visual store, but more recent research (e.g., Baddeley & Andrade, 2000) has expanded this to include other memory stores such as olfactory, gustatory, and tactile. Baddeley and Hitch also discussed the role of long-term memory in working memory, noting that it plays a crucial role in the ability to hold and manipulate information over longer periods of time. They suggested that working memory and long-term memory are separate but interdependent systems. This led to a widely used and functionally practical memory model, which is simplified in Fig. 2.1 (based on Willingham, 2021). Working memory is essentially the cognitive workspace in which information is temporarily stored and acted upon. That is, it is the system that supports our capacity to “keep things in mind” when carrying out complex tasks. For instance, when pondering the question: “What similarities exist between a raincoat and a notebook?” we would extract pertinent details about raincoats (e.g., water-resistant, worn outdoors, protective gear) and notebooks (e.g., bound pages, used for writing, portable) from long-term memory. The next step would involve assessing these characteristics for any commonalities (Willingham, 2019). We carry this out in our working memory. Working memory capacity is notably finite, having a capacity for only four to seven unconnected elements at any given time. We cannot hold a lot in Fig. 2.1 A simple representation of human memory (based on Willingham, 2021) 8 2 How Knowledge Matters our working memory, and even the small amount we can hold does not remain there for a very long time (i.e., 3–20 s) if nothing is done with it. Imagine being asked to identify a common attribute among a raincoat, a notebook, a teaspoon, a guitar, and a refrigerator. Our working memory could possibly retain the names of these five items, but supporting details for each item and the cognitive resources to evaluate them collectively would exceed our processing limitations. Let’s look at a second example that illustrates the constraints of working memory. Imagine you have to learn the chemical interactions below: 2 NaCN + H2 O + CO2 = Na2 CO3 + 2HCN 2H + CNO2 CNa2 O : NaH3 + 2CCN2 O You are exposed to these formulas for 30 s each. After one minute, they are hidden, and you are asked to write them down. How well did you do? Well, it probably depends. This experiment, a modern-day version of de Groot (1965) studies with chess masters, was conducted with experts and non-experts in chemistry (Zhilin & Tkachuk, 2013). After respondents in both groups were exposed to the equations for 30 s, experts immediately recognised that the first equation represented a reaction in which sodium cyanide (NaCN) reacts with water (H2 O) and carbon dioxide (CO2 ) to produce sodium carbonate (Na2 CO3 ) and hydrogen cyanide (HCN). The experts also recalled real chemical equations better than the novice participants, but experts and novices had no significant difference in recalling the fake equation. Novices tended to remember both real and fake equations symbol-by-symbol from left to right, with increasing mistakes in the same order. On the other hand, experts remembered the real equation as a whole, and could chunk (combine smaller, unitary bits of information into larger and more meaningful ones) some chains in the fake sequences, resulting in slightly better memory. These findings support the idea that experts can chunk information based on the knowledge in their long-term memory and can see patterns in new situations. They combine (or “chunk”) smaller bits of knowledge into a single knowledge unit. This chunking (combining smaller units of information into larger ones) is a function of our prior knowledge in our long-term memory. Long-term memory, thus, assists working memory while thinking. It is an expansive repository within our cognitive structure that possesses a seemingly limitless capacity for storing information. It facilitates the consolidation of discrete elements into coherent wholes (i.e., chunks), thereby economising scarce working memory resources. The first chemical equation probably only contains one chunk of information for the expert. This frees cognitive space from working memory—a cognitive shortcut—to consider other aspects of the task. It makes learning look easier as the information needed to carry out the task pops up seemingly effortless. What you already know does not require much mental effort, so experts with vast amounts of domain-specific knowledge can tackle new problems in their domain of expertise more efficiently (i.e., with more speed and accuracy) than novices do (Willingham, 2021). The key words here are ‘in their domain’. A good chess player is not a good checkers or go-player, just as the chemists in the 2.1 Knowledge Matters: A Learning Perspective 9 experiment cannot do this when dealing with a geology problem let alone a problem dealing with a language they do not speak. Their skill is not generic, but rather specific to particular subject matter—what psychologists call ‘domain-specific’. Knowledge is stored in long-term memory in cognitive structures or schemas. They can be seen as structures of organised and interconnected knowledge elements comprising concepts, words, and ideas. Schemas can also consist of other schemas, much like how the concept of ‘a tree’ can be explained in terms of its roots, trunk, branches, leaves, and fruits. Each of these terms can also be broken down further, such as the veins, chlorophyll, and cells of a leaf. Renn (2020) refers to these complex interdependencies as the ‘architecture of knowledge’. However, schemas are not permanently fixed. We can also start from ‘inside the box’ and expand it in a different direction. For instance, starting from the concept of chlorophyll, one may discover examples such as green pasta or spirits, in which chlorophyll is used as a colouring additive (E140). In other words, knowledge can be organised into various hierarchies, forming complex schemas of interconnected ideas, and serve as conceptual coat hangers or anchors for the organisation of knowledge and learning new ideas (Hattie, 2023). 2.1.2 How Can Prior Knowledge Facilitate Better Learning? Possessing knowledge and skills in long-term memory frees up valuable space in working memory to tackle more complex cognitive thinking tasks such as problem- solving, critical thinking, and reading comprehension, as discussed below. If you have ever wondered why we have elementary school students memorise the multiplica- tion tables, drill verb conjugations, perfect their spelling, expand their vocabulary, and acquire background knowledge, the following will help you to understand. It’s all about automaticity. Seemingly counterintuitively, the best ways to become profi- cient in a skill often do not resemble the skill itself (Wiliam, 2018). For instance, if someone wants to become proficient at playing a musical instrument like the piano, they might find that simply playing the piano for hours on end isn’t the most efficient way to improve. A novice piano player might first want to train and automate certain hand movements or learn about music theory. Outside education, this subtle under- standing of the importance of automaticity is well understood and is often seen as a precondition for true mastery. How many passionate young football (in America: soccer) players meticulously practice isolated dribbling techniques to succeed on the pitch? And how many actors or musicians memorise their lines or riffs to be able to improvise on stage? Beyond improving on the practiced task, this approach has the added benefit of conserving ‘mental bandwidth’ to do more. And it goes even further than that. Try to study the following three rows of twelve digits. Row 1 : 610894121158 Row 2 : 106614921815 10 2 How Knowledge Matters Row 3 : 198520192023 Which row do you remember best? Let’s take a guess. You found row 1 to be the hardest to learn. Row 3 was the easiest to learn because you possibly saw it as referring to recent years. Row 2 could also be easy to remember, yet only if you have sufficient background knowledge of history: 1066 was the year of the Battle of Hastings; in 1492 Columbus ‘discovered’ America, and in 1815 Napoleon met his Waterloo. However, if you do not possess this background knowledge, Row 2 would appear as difficult as Row 1. Knowledge in your long-term memory not only reduces the complexity and difficulty of acquiring new knowledge (i.e., turning added information into knowledge), it also seems easier to do so, while simultaneously enhancing retention. Whereas the numbers from Row 1 might already be fading from memory, those from Rows 2 and 3 are more likely to remain embedded in memory. That is because new information that can be connected to prior knowledge tends to stick around longer. The slower you forget, the longer you retain. Psychologists have spent decades studying the processes involved in slowing down forgetting and effectively storing knowledge in long-term memory. They have identified a range of learning strategies, the detailed exploration of which lies beyond the scope of this book. For a brief introduction, see Appendix A. If prior knowledge supports thinking, makes learning easier, and leads to more durable learning, it is tempting to conclude that our children simply need to learn a vast amount of knowledge. Right? The more prior knowledge, the better. Yet, this is the point at which we must proceed with caution. Whether and how prior knowledge influences learning depends on the nature of that prior knowledge itself (Brod, 2021). Knowledge alone does not lead to better learning. To be effective, prior knowledge must meet several important criteria. First, it must be activated. For example, imagine students learning about the formation of the Himalayas. Potentially relevant prior knowledge might include understanding what a mountain range is and how plate tectonics play a role in this, knowing that the Himalayas are located in Asia at the point where two plates collide, and that Asia is a continent that includes India and most of Russia, among other facts. Some children might even have a basic understanding of concepts such as shifting tectonic plates and continental drift. The issue of prior knowledge not being activated by learners has been extensively researched, particularly in children, who often have not yet become resourceful in using cognitive control strategies to use their prior knowledge strategically. On the other hand, well integrated knowledge will become active on its own. In essence, it is not enough for prior knowledge to be available; it must also be (consciously or unconsciously) activated and applied to guide the learning process. The teacher’s role is critical here, tasked with mapping out what children should already know to fully comprehend the content of a new lesson and activating it, for example, with pre-questions (retrieval practice) or advance organisers. Second, even if learners activate some prior knowledge, it must also be relevant to the learning task to be beneficial. Students might know about India’s extensive colonial history with Britain and that the British set up hill stations in the foothills of 2.1 Knowledge Matters: A Learning Perspective 11 the Himalayas, but this information is not really helpful when trying to understand geological formations in the Himalayas. Students might even hold misconceptions (i.e., faulty beliefs) about plate tectonics, such as wrongly assuming that each conti- nent rests on a separate tectonic plate, with continental boundaries aligning with plate edges. This could make it harder for them to grasp the role of India in the northward thrust that continues to elevate the Himalayas today. Irrelevant and faulty prior knowledge might even hinder subsequent learning (Simonsmeier et al., 2022). Finally, prior knowledge should ideally be congruent with the new information even when activated and relevant. For instance, within the geography curriculum, certain words such as plate, drift, and mantle possess technical definitions that may prove challenging for students to comprehend because of the presence of alterna- tive, common-sense or common-language meanings of those words that significantly deviate from their technical connotations. The greater the necessary reorganisation of existing knowledge, the more challenging it can be for learners to integrate new information into existing knowledge schemas. While higher congruency between prior knowledge and new information usually enhances learning of the new infor- mation, it has been demonstrated that highly incongruent new information can also be effectively learned, particularly when it triggers a significant level of surprise in learners (Brode, 2021). Thus, the complex nature of prior knowledge underscores that its impact on learning depends on more than simply the quantity of knowledge available. However, if prior knowledge is activated, relevant, and congruent, then its impact on learning can be significant. 2.1.3 Why Complex Cognitive Skills Require Knowledge While knowledge storage and schema building in long term memory are important, they are not enough. We are greedy. Education should also have the ambition to engage with this knowledge and foster the acquisition and use of complex cognitive skills in students such as critical thinking, problem solving, and reading compre- hension. However, the question arises as to whether critical thinking or any of these complex cognitive skills can be generically taught across or without reference to specific knowledge domains. If you ask historians to describe what critical thinking is, they say very similar things to what mathematicians say. Hence, it is natural to think that they are the same skill. In contrast, they are in fact a collection of super- ficially similar skills (e.g., evaluating the relevancy of certain things or determining the validity of an argument) and/or procedures (i.e., the steps to take in carrying out research) that rely on different underlying cognitive processes. While it should be acknowledged that the idea of teaching generalised critical thinking skills is attrac- tive, we should not consider those processes as a collection of skills that can be employed at any given time or in any given context. Those who have endeavoured to teach complex skills such as critical thinking as a separate course in the curriculum have operated under the assumption that it is a skill akin to driving a car, and once acquired, can be applied in any given situation. They assumed students who learned 12 2 How Knowledge Matters to think critically in history lessons about, for instance, the role of the French revolu- tion in nation-building in Europe, would transfer those critical thinking skills to novel situations, such as critical thinking about zero-emission policies to combat global warming. Or they assumed that students who learned to solve open-ended problems in physics would be able to transfer that skill to solve problems in psychology. The steps seem similar or identical, but cognitive science research has revealed that crit- ical thinking or other complex cognitive skills are not of that nature. The processes of thought are intricately intertwined with the content of the thoughts themselves; in other words, with domain-specific knowledge. In a landmark experiment, researchers presented participants with a scenario illus- trating an ill-defined problem wherein an X-ray, capable of treating a tumour, also posed the risk of damaging a lot of healthy tissue: Suppose you are a doctor faced with a patient who has a malignant tumor in his stomach. It is impossible to operate on the patient, but unless the tumor is destroyed the patient will die. There is a kind of ray that can be used to destroy the tumor. If the rays reach the tumor all at once at a sufficiently high intensity, the tumor will be destroyed. Unfortunately, at this intensity the healthy tissue that the rays pass through on the way to the tumor will also be destroyed. At lower intensities the rays are harmless to healthy tissue, but they will not affect the tumor either. What type of procedure might be used to destroy the tumor with the rays, and at the same time avoid destroying the healthy tissue? (Gick & Holyoak, 1983, pp. 3) Participants were thus tasked with determining how to use the X-ray to eliminate the tumour, a problem that only a minority solved within 20 min. Subsequently, another group was exposed to a military scenario mirroring this dilemma, but it was solvable in the same way. In this scenario, a general plans to seize a fortress situated at the heart of a country. The fortress is accessible by several roads, but each is heavily mined. While small groups can pass the roads safely, a large force would detonate the mines. To overcome this, the general splits his army into smaller units, sends each along a different road, and has them converge on the fortress at the same time. The “convergence” solution to the military problem is analogous to the X-ray problem (i.e., scattering the forces to avoid collateral damage and having forces converge at the point of attack). Despite reading this story immediately prior to addressing the medical problem, they failed to perceive the analogy with the convergence solution, as depicted in Fig. 2.2. Remarkably, solution rates surged when the story was explicitly mentioned (Gick & Holyoak, 1983). This underscores the idea that employing the analogy in a novel situation was not the main challenge; rather, the difficulty lies in recalling it and seeing its need or Fig. 2.2 Schemes that illustrate the principle underlying the convergence solution 2.1 Knowledge Matters: A Learning Perspective 13 usefulness. These findings offered crucial insights into teaching critical thinking. The challenge in transferring critical thinking skills lies in the fact that domain-specific examples on how to think critically should be offered to students (they will act as worked examples for future similar tasks), and that they are archived in long-term memory, and resurface only using specific triggers. The ability to think critically about open-ended problems such as the radiation problem described above is facilitated by vast knowledge in the specific area. Knowl- edge plays a role in solving these problems in at least three ways (Willingham, 2019). First, as described earlier, knowledge from long-term memory assists working memory because of the experts’ ability to chunk new information into new or already existing coherent wholes. Recognising a situation similar to a previously encoun- tered one helps you identify areas of strength and weakness, thereby freeing up valuable thinking space in working memory. Second, the recognition process of the open-ended problem (‘ah, this is a radiation problem’) can still be applied to components of a yet more complex, open-ended problem. Complex critical thinking may involve the application of multiple simpler solutions from memory, which can be combined when solving new, more complex problems. The final way in which knowledge can contribute to critical thinking is by enabling the individual to employ thinking strategies in combination with domain-specific knowledge, stored in long- term memory. When discussing the recognition of underlying structures such as in the radiation problem, the issue arose from having an effective thinking strategy stored in memory, yet failing to retrieve it due to a lack of recognition of its relevance for the particular situation. However, some situations that require critical thinking can be easily identified. For instance, we can teach graduate students in a certain domain to evaluate the logic behind scientists’ arguments and prompt them to assess whether students can infer causal claims with the scientific methodology used. Those graduate students should have no difficulty recognising the type of problem they are facing and may have already stored the correct thinking strategy in long-term memory, in this case combined with some statistical knowledge. They know what needs to be done, yet they might still face the problem of not having the necessary domain-specific knowledge, which may hinder their ability to utilise the strategy. Complex cognitive skills such as critical thinking are therefore not a collection of skills that can be employed at any given time or in any given context. They should be seen as a form of thought that requires knowledge with which to engage. The same applies to the other complex cognitive thinking skills to a greater or lesser extent. It would be amazing if we could teach our students to solve open-ended problems in geography in a way that would also improve their ability to solve open-ended problems in mathematics, yet this is not how our brains work. Problem-solving in geography requires students to learn specific geographical content knowledge. No matter how much we train students to solve math problems or teach them Latin, it does not make them better ‘problem-solvers’ or ‘logical thinkers’ in other domains such as in a natural science (De Bruyckere et al., 2020; Thorndike, 1923). It remains clear that without relevant prior knowledge, cross-discipline learning is not what many in education once hoped it would be. Interestingly, the PISA 2022 survey included Creativity as an additional domain, yet it revealed a high correlation between 14 2 How Knowledge Matters mathematical knowledge and creativity, indicating a strong relationship between higher-order skills and domain knowledge (OECD, 2024; Ward & Kolomyts, 2010). However, this does not imply that problem-solving without prior knowledge is consistently ineffective. At times, it can be beneficial to start with problem-solving in a particular topic to identify what students already know and need to learn and motivate them to dig deeper. This is often the case in goal-free or goal-nonspecific problems where learners are given information (objects, their mass, angle of incline, friction) and then are asked to determine what they can do with it (Ayres, 1993; Van Merriënboer & Kirschner, 2017). Take the following example from Van Merriënboer and Kirschner (2017, p. 73): Usually, learners receive goal-specific problems, such as “A car with a mass of 950 kg accelerating in a straight line from rest for 10 seconds travels 100 meters. What is the final velocity of the car?” This problem could easily be made goal nonspecific by replacing the last line with ‘Calculate the value of as many of the variables involved here as you can.’ Here, the learner would calculate the final velocity, acceleration, and force exerted by the car at top acceleration. And if the word ‘calculate’ was replaced by ‘represent,’ the learner could also include graphs and the like. Nonspecific goal problems invite learners to move forward from the givens and to explore the problem space, which may help them construct cognitive schemas. This can then be followed by more targeted teaching and knowledge building, and finally returning to problem-solving to reinforce their understanding (Kapur, 2008). 2.1.4 Reading Comprehension “Reading is the basis for the acquisition of knowledge, for cultural engagement, for democracy, and for success in the workplace” (Castles et al., 2018, p. 5). Moreover, its importance in education cannot be overstated as it is essential for further learning in all subjects. At the same time, reading is one of the most complex mental acts a person can do and entails the development of cognitive thinking skills in five areas, the so called ‘big five’: phonics, phonemic awareness, vocabulary, fluency, and language comprehension (National Reading Panel, 2000; National Research Council, 2000; RAND Reading Study Group, 2002; Pearson & Cervetti, 2015). In the following paragraphs it will become clear to you as a skilled reader just how many complex processes are being executed in your mind while you read this book, and why this is important when teaching reading to students. The schematic model in Fig. 2.3 (Willingham, 2017) summarises the complexity of the cognitive processes involved in reading at roughly three highly interconnected levels: (1) letters and phonemes; (2) words; and (3) sentences, paragraphs, and full texts. When teaching reading, the most effective programs first address the develop- ment of alphabet knowledge, phonemic awareness, and oral language (i.e., listening and oral fluency), the so-called prereading skills. Most children already possess relatively developed spoken-language skills, including knowledge of the meanings 2.1 Knowledge Matters: A Learning Perspective 15 Fig. 2.3 Model of reading (Willingham, 2017) of many spoken words, when they start to learn how to read (Castles et al., 2018). Subsequently, a focus on explicit and implicit vocabulary instruction, comprehension, and phonics instruction with repeated opportunities to read—silently and aloud— to develop fluency is considered the most powerful combination for early reading instruction (Hattie, 2023). The Reading Rope, a visual representation created by Hollis Scarborough, helps us understand the complex processes involved in how the brain learns to read (see Fig. 2.4). The two meta-strands, language comprehension and word recognition, once again contain their own subsets of distinct components. 16 2 How Knowledge Matters In the case of language comprehension those are background knowledge, vocabu- lary, language structures, verbal reasoning and literacy knowledge, and with regard to word recognition respectively phonological awareness, decoding and sight recogni- tion are involved. Language comprehension and word recognition are interconnected and interdependent, so when students progress toward fluency with sufficient prac- tice and instruction, word recognition becomes increasingly automatic, and language comprehension becomes increasingly strategic (Scarborough et al., 2009). With sufficient mastery, the initially heavy demand on students’ working memo- ries while reading is gradually reduced. This means they can commit more cogni- tive resources to comprehending what they are reading, that is, the complex mental processes involved in constructing meaning from and in individual sentences, across paragraphs, and finally in full texts. Principles similar to those in reading also apply Fig. 2.4 Reading rope (Scarborough et al., 2009) 2.1 Knowledge Matters: A Learning Perspective 17 to arithmetic fluency and math problem solving as among others David Geary (2011), and Xin Lin and Sarah Powell (2022) have shown in their work. In what follows, we describe how knowledge elements play a role at each of the interconnected levels as described in the overall Model of Reading depicted in Fig. 2.3, to facilitate the gradual development of those complex reading processes that distinguish successful from struggling readers, and how background knowledge plays a particularly important role in deep reading comprehension at the level of sentences, paragraphs, and full-texts. 2.1.4.1 Letters and Phonemes Letter knowledge and phonemic awareness are the first requirements for learning how to decode text in alphabetic languages (Ehri et al., 2001; Ruan et al., 2018), namely to be able to “distinguish one letter from another, hear individual speech sounds, and know the mapping between letters (and letter groups), and speech sounds” (Willingham, 2017 p. 47). Consequently, an important component of initial reading instruction is effectively teaching letters and awareness of individual speech sounds, so that students’ knowledge of the relationships between speech sounds and (groups of) letters (the alphabetic principle) can become increasingly automatised (Hattie, 2023). With sufficient practice, students can then apply it through phonics; that is, use their knowledge of the relationships between speech sounds and (groups of) letters to decode text, and read familiar and unfamiliar words. 2.1.4.2 Words Word-specific knowledge. While initially very demanding, with extensive practice students can gradually automate the letter-sound translation process (sounding a word out), and reduce the demands on their working memories. As the comprehen- sion processes in reading in the next phases are so demanding, it is very impor- tant that readers can free up enough space in working memory by making reading faster and easier. At the word level, this requires word-specific knowledge (Perfetti, 2007), which implies that readers need to develop very strong relationships between word sounds, spellings, and meanings (see the middle part of Willingham’s model, Fig. 2.3), which in turn will provide them with nearly automatic word recognition. Extensive practice in reading individual words and later sentences and texts, as well as explicit spelling and morphology instruction (Graham & Santangelo, 2014) and explicit vocabulary instruction, can boost this process. That way, students’ reading fluency is built over months and years. Vocabulary breadth and depth. Vocabulary knowledge in the broad sense is very important, as both vocabulary breadth (i.e., knowing many words) and depth (i.e., having many and strong connections between words) matter to reading comprehen- sion (Elleman et al., 2009; Nation & Snowling, 1998; Ouellette, 2006). Deep vocab- ulary knowledge not only includes knowing the meanings of words (i.e., semantics), 18 2 How Knowledge Matters their structure (i.e., morphology), spelling, and use (i.e., grammar), but also links to other words (i.e., word/semantic relationships). In terms of vocabulary breadth, research has shown that readers need to know up to 95 percent of the words in a text to understand it at a general level, and even 98 percent to really comprehend it (Carver, 1994; Hsueh-Chao & Nation, 2000; Schmitt et al., 2011). If this so-called coverage drops to 80 percent, readers can at best only understand the overall gist of a text (Nation, 2001), and most readers would find it so difficult to extract the gist that they would likely give up. Sometimes, the meaning of unknown words can be derived from the context, but only to a limited extent. To do so, readers would already need to have a relatively large vocabulary, and the coverage in the specific text they read would need to be nearly 98 percent, or only one unknown word for every 50 known words (Hsueh-Chao & Nation, 2000; Laufer, 1992; Laufer & Yano, 2001). Moreover, too much task-switching between problem- solving and comprehension during reading occupies working memory resources required for understanding and can therefore interrupt the flow of reading (Csik- szentmihalyi, 1990; Kirschner & De Bruyckere, 2017; Willingham, 2017). Regular interruptions make reading less fluent, more difficult, and less enjoyable. For the same reason, looking up the meaning of unknown words in, for instance, online glossaries is only of limited use, and they have the additional disadvantage that word definitions are offered in just one context, whereas the meaning of words is precisely very sensitive to the context in which it appears (Willingham, 2017). The importance of vocabulary depth is somewhat more difficult to understand, but we will try to illustrate it with an example. Students may know that a scorpion is an animal with a pair of grasping pincers and a long tail. If that is all they know, do they then know the word ‘scorpion’? Yes, but only in a shallow way. Deeper knowledge of the word would also invoke connections to concepts like ‘predatory’, ‘the desert’, ‘stinger’, and ‘venomous’. This is important, as different facets can be required for understanding in different contexts, and authors often omit information (i.e. background knowledge) they expect their readership to know. So if students only know that a scorpion is an animal with a pair of grasping pincers similar to a crab’s that has a long tail, a precautionary message like ‘Don’t pick up a scorpion, and definitely watch out for its tail.’ might inform them to a certain extent. However, someone who also knows that it has a venomous stinger, and uses it not only to hunt, but also to defend itself, will be much better informed before approaching a scorpion. This example shows how words are situated in meaning networks, and how they activate related words. That depth of your vocabulary knowledge helps you fill in the ‘gaps’ between sentences and paragraphs, as authors implicitly expect you to possess certain background knowledge to understand the ideas conveyed in the texts they write, yet they do not explicitly include it all. That would simply render most texts extremely long and unreadable. Besides the richness of those word relationships, also the speed you can access them with facilitates reading (Oakhill et al., 2012). As mentioned above, that speed is determined by the strength of the relationships between word sounds, spellings, and meanings, also known as readers’ word-specific knowledge (Perfetti, 2007). 2.1 Knowledge Matters: A Learning Perspective 19 Sentences, paragraphs, and full texts. When it comes to deep reading compre- hension at the sentence, paragraph, and text level, the construction-integration (CI) model of text comprehension (Kintsch, 1998; Kintsch & van Dijk, 1978) remains the most detailed interactive model to describe the mental processes in the minds of readers (integrated in the lower part of Willingham’s model; Fig. 2.3). It describes how the words and syntax are situated in a text’s surface structure, from which readers later create sentence representations (also called propositions). Readers then construct a text-base model (or idea-web; Willingham, 2017) by connecting these propositions in the text’s main idea. This text-base model represents quite literally ‘what the text says’ and is subsequently inserted in their working memories. Besides sufficiently automated basic reading processes described above at the letter, phoneme, and word level, students also need knowledge of syntactic rules and text structure to facilitate this mental construction of the main idea of the paragraph or text they read. However, this main idea of the text alone is not enough for deep understanding. This is where background knowledge comes into play. Only through the integration of the text-base with relevant background knowledge and experiences from long-term memory (readers’ pre-existing schemas) can readers truly come to a deeper under- standing of a text. The mental model that is subsequently created is called the situa- tion model (Zwaan & Radvansky, 1998), and is a reader’s dynamically constructed detailed mental representation of the text. Besides readers’ overall language and decoding ability as described, now the breadth and depth of their background knowl- edge, including vocabulary, comes into play, which differs among students, thus leading to distinct situation models for each reader. Four intersecting dimensions of background knowledge can thereby be distinguished (McCarthy & McNamara, 2021): (1) amount (how many relevant concepts readers already know); (2) accuracy (how correct the knowledge is that readers already possess); (3) specificity (how related their knowledge is to the information in the text); and (4) coherence (how interconnected the knowledge is that readers already possess). The good news is that situation models are also cumulative; so, if the same reader becomes more knowledgeable about a topic, the schemas and situation model will evolve (Kintsch, 1998). However, if students lack the necessary background knowl- edge to integrate the text base, a less effective situation model results, and they consequently experience more difficulty understanding a text (Kendeou & Van Den Broek, 2007). The previous sections have shown that “while word knowledge speeds up word recognition and thus the process of reading, world knowledge speeds up comprehen- sion of textual meaning by offering a foundation for making inferences (Hirsch, 2003, p. 12).” Let us now more closely consider how background knowledge facilitates deep reading comprehension. The Importance of Background Knowledge. A review study on experimental research with primary-school aged children on the role of background knowledge in reading comprehension (Smith et al., 2021) has confirmed the critical importance of both reading ability (the upper part of Willingham’s model, Fig. 2.3) and background 20 2 How Knowledge Matters knowledge (the lower part) for deep reading comprehension. It was found that higher levels of background knowledge on a topic, both in terms of quantity and quality, consistently lead to better text comprehension in both high- and low-ability readers, and that increased background knowledge impacts reading comprehension differ- ently in students with distinct reading ability. Highly knowledgeable students with low reading ability can even compensate for the latter at the level of the text-base model and improve overall comprehension of a text (Recht & Leslie, 1988), but still experience some difficulties in making inferences at the level of the situation model. On the other hand, increased knowledge in students with high reading ability operates more directly at the level of the situation model, deepening understanding even more so than in the case of high-knowledge low ability readers. In sum, these results show that background knowledge is very important to achieve deep text comprehension besides well-developed reading ability (see also van Bergen et al., 2018, 2021). We can now easily see from the previous sections that students’ deep reading comprehension won’t improve unless we also pay serious attention to building their background knowledge, or “word and world knowledge” (Hirsch, 2003). Yet in reading instruction, despite the fact that deep reading (and listening) comprehen- sion requires students to make inferences that depend heavily on background knowl- edge, much teaching time is devoted to generic reading strategy instruction depicted as ‘inferencing skills’, such as finding the main idea of a text based upon signal words, to boost reading comprehension (see Sect. 2.2 for a broader discussion on the origin of these ideas). However, besides the clear initial value in practicing these comprehension strategies (Hattie, 2023; Willingham & Lovette, 2014), the same research base has equally shown that after an initial surge, the effects of reading strategy instruction on students’ reading comprehension quickly reach a plateau, and have little further impact (Elleman, 2017; Rosenshine & Meister, 1994; Stevens et al., 2019; for a full overview see Willingham, 2023). That is because the goal of comprehension strategies is to activate students’ background knowledge. However, if the relevant background knowledge is lacking, conscious comprehension strate- gies cannot activate it. Recent research has highlighted that the effects of reading strategy instruction are therefore significantly strengthened by instruction in back- ground knowledge (Peng et al., 2023), and that the relation between knowledge and reading is indeed bidirectional and positive throughout the elementary years: in other words “knowledge begets reading, which begets knowledge” (Hwang et al., 2023). A distinct approach to reading instruction therefore argues that, besides a focus on fluent reading ability, more content-rich instruction time in school should be dedi- cated to the well-thought out and balanced accumulation of background knowledge to allow better reading comprehension (Cabell & Hwang, 2020; Cervetti & Hiebert, 2019; Hirsch, 2003, Hirsch, 2016; Hwang & Duke, 2020; Neuman et al., 2014; Willingham, 2006; Willingham, 2017; Willingham & Lovette, 2014). As described in the previous section, it is important to underline, however, that building back- ground knowledge “[…] is not just accumulating facts; rather, children need to develop knowledge networks, comprised of clusters of concepts that are coherent, generative, and supportive of future learning in a domain” (Neuman et al., 2014, p. 147). Reading and listening to different expository and narrative texts on the 2.2 Knowledge Matters: A Sociological Perspective 21 same subject for extended periods of time, and talking about the information and concepts they contain, can boost reading comprehension and vocabulary in the class- room (Hirsch, 2003; Wright et al., 2022). This could be particularly helpful for disadvantaged students, who depend mostly on schools to be exposed to advanced vocabulary and rich content knowledge (Hart & Risley, 2003; Hirsch, 2006; Will- ingham, 2017), whereas more advantaged students might improve more rapidly thanks to the language boost and solid knowledge base they receive outside the school environment, the so-called Matthew effect (Kaefer et al., 2015; Pfost et al., 2014; Rigney, 2010; Stanovich, 1986). Moreover, the often challenging transition for students from ‘learning to read’ to ‘reading to learn’ from the fourth-grade onwards is often associated with the fact that, besides problems related to coding and fluency skills (Goodwin, 2011), particularly disadvantaged students might lack sufficient background knowledge to really grasp the meaning of the expository texts they increasingly need to read in school to learn about all kinds of important topics in different subjects (Chall & Jacobs, 2003; Willingham, 2017). These text types are particularly demanding, as their informative nature builds on readers’ knowledge of specific topics (Beck & McKeown, 1991). A common knowledge base, built system- atically and cumulatively in school from an early age onwards, could address many of these challenges, while at the same time ensuring deep learning experiences for all students, in line with the findings from cognitive psychology research as described above. Up until now we have focused on the importance of knowledge for learning and the acquisition of complex cognitive skills such as critical thinking and reading comprehension. In what follows, we will discuss how the current situation came about with reflections of a sociological nature on the role of knowledge in education over the years (Sect. 2.2), followed by an account of its importance from a democratic and emancipatory viewpoint (Sect. 2.3). 2.2 Knowledge Matters: A Sociological Perspective In section two, we take a sociological perspective into why and how knowledge has been displaced in education. Starting with different views on knowledge, we further explain how constructivist and neoliberal sentiments have changed the role of knowledge. We then introduce a new line of thought that shows how to bring knowledge back into education. 22 2 How Knowledge Matters 2.2.1 From Objectivist to Constructivist Thinking Perspectives Knowledge has often been associated with concepts such as ‘truth’, ‘fact’, ‘social inequality’, and ‘class differences’. These topics gained significant attention during the early 1970s when the New Sociology of Education (NSOE) emerged. During this period, concerns regarding social class and distributional effects in education became increasingly evident. In this century, we can assume that a so-called “objec- tivist” (sometimes also referred to as positivist or instructivist) view of knowledge was more prevalent. Objectivism views knowledge as independent of individual and social contexts, devoid of value judgments, and purely objective. The statement 3 + 2 = 5 is a fact. That Michelangelo created frescoes on the ceiling of the Sistine Chapel in Rome is a fact. That the natural behaviour of a body is to stay in the same place or to move in a straight line at a constant speed and, without outside influences, a body’s motion preserves its status is a fact (hard science). Objectivists emphasise the development of comprehensive theories and universally applicable knowledge, aiming to uncover ‘absolute truths’ about the world. However, this perspective has been criticised for neglecting the social dimension of knowledge, which can poten- tially lead to an absolutist stance that asserts universal truths without considering their situatedness and potential biases. One of the new concerns on the objectivistic view the New Sociologists brought to the fore in the 1970s was the social differen- tiation in education and the reproduction of social inequalities that were associated with the exclusionary structures of educational knowledge. Who had access to the knowledge of the powerful? Did a child from a working-class family acquire the same knowledge as one with highly educated parents? From this time onwards, soci- ologists have posited a connection between the organisation of knowledge in schools and broader social inequalities and power dynamics. Authors such as Michael Young criticised these exclusive knowledge systems (Young, 1971) by uncovering their intimate connections to social class structures. The knowledge taught in schools was considered a tool to reinforce the dominance of the ruling social group and its perspectives. For instance, knowledge of the history and reign of the Tudors was seen as interesting and valuable for a particular ruling class in charge of society, while knowledge of how to make concrete was seen as inferior to that of the liberal arts. For this reason, the New Sociology of Education sought to uncover the hidden interests lying beneath the surface of educational knowledge and how it was taught (Moore, 2007). Over the years that followed, this perspective evolved and found expression in various theories about what happens when learning takes place (e.g. constructivism), and about what can be known (e.g. postmodernism). Radical constructivists assert that knowledge is constructed from and deeply embedded within an individual’s social and cultural environment. Isaac Newton’s first law of motion acquires meaning because learners connect the new information to their prior (folk?) experiences of motion (‘the ball on the grassy field did not move until I kicked it!’). They emphasise the role of human agency in shaping knowledge, 2.2 Knowledge Matters: A Sociological Perspective 23 recognising that it is influenced by societal norms, cultural perspectives, and indi- vidual experiences, and thus knowledge can only be constructed in a particular social context. A postmodern perspective takes this further and states that “objective truth” is nothing more than the dominant viewpoints of the powerful elite among the variety of human experiences and the multitude of perspectives. However, these views on knowledge run the risk of relativism, where all knowledge is considered subjective and context-specific, potentially undermining the possibility of grounding knowledge in any objective truth. The problem that arises from this line of thinking is that it reduces knowledge to its context and the knowers that possess it. This shift in conceptualising knowledge resulted in the abandonment of knowledge as ‘truth’ or ‘fact’ to knowledge as nothing more than the viewpoints of typically dominant social groups. This also shifts the validation of knowledge from the content itself to the identity of the knower. Knowledge is no longer considered objective or neutral, but is seen as a tool the oppressive elite uses for political control. Although the absolute objectivist notion of knowledge was abandoned long ago, constructivist and postmodern views on knowledge have found fertile ground in education (Moore, 2013), both in thinking about what we want our children to learn (content) and in how we want to achieve this (teaching). It is important to note, however, that these changes in the role of knowledge in education are much older than the 1970s. As Hirsch (2016) persuasively argues, these changes have roots in the romantic movement (eighteenth century) with the ideas of Wordsworth and Hegel that inspired Dewey, whose ideas then became a part of the so-called progressive movement in the US around 1920. The trends in the 1970s represent one of the latest continuations of these forms of thought, which have now garnered considerable influence. 2.2.2 Skills for the Twenty-first Century and Neoliberal Influences These tendencies were amplified with the rise of neoliberalism. The focus of educa- tion shifted from cultural and civic socialisation towards a focus on employability and economic growth (Meyer & Benavot, 2013). Although almost two centuries earlier Herbert Spencer (1861) had already argued for a utilitarian and practical business- oriented education, this recent change can be attributed to the impact of globalisa- tion and the need for nations to design their educational systems and curriculum as tools for economic development, and even national competitiveness (Yates & Young, 2010). This led to the abandonment of the idea of education as a goal in itself and shifted the focus to skills and competencies (a term borrowed from human resource management terminology) that students could later employ in the labour market. As argued by Wheelahan et al. (2022), the shift towards prioritising skills in educa- tion is rooted in human capital theory. Initially descriptive, emphasising the link 24 2 How Knowledge Matters between education and jobs, it evolved into a normative stance, asserting education should focus on employability. By the 2000s, it had become prescriptive, demanding education to align with workforce needs, leading to government policies shaping and funding education accordingly. The call for developing skills relevant to the twenty-first century is the latest form of this trend (Rotherham & Willingham, 2010), and can be traced back to 2003, when the twentieth anniversary of an influential report (A Nation at Risk, 1983) trig- gered numerous analyses of the progress of American education. The introduction of new fundamental skills was recommended, including computer literacy, and various generic—and by the authors considered transferable—cognitive skills, such as crit- ical thinking and problem-solving. However, as shown in Sect. 2.1, complex cogni- tive thinking skills such as critical thinking, communicating, working together (i.e., collaborating) and problem-solving are not only grounded in knowledge, but have also been key components of human progress throughout history. This is evident from early advancements in astronomy and mathematics in antiquity, such as the devel- opment of alphabets and writing, the construction of the Egyptian pyramids, and the development of Greek philosophical thought, to scientific progress in the Middle Ages, including Avicenna’s early medical practices and Johannes Gutenberg’s inven- tion of the printing press using movable type to name but a few. The main difference with the skill movement was that these skills should now become universal, and not be left to chance for the happy few. Private industry and labour market economists have played a significant role in advocating for competencies such as complex thinking and communication skills (Levy & Murnane, 2013). At the start of the millennium, the top skills demanded by the most prominent and most influential companies in the United States had shifted from traditional skills such as reading, writing, and mathe- matics to complex skills like ‘teamwork’, ‘critical thinking’, ‘problem-solving’, and ‘interpersonal abilities’. By 2015, the interest in the so-called twenty-first-century skills had become a global phenomenon, as evidenced by the contribution of the World Economic Forum, which outlined 16 essential proficiencies for education in the twenty-first century (see Table 2.1). This economic undertone is also strongly reinforced by international educational policy actors, such as the European Union with its European Qualification Frame- work, which allows cross-country comparison of skills; the Programme for Inter- national Student Assessment (PISA) led by the OECD to assess selected student competencies; and professional training programs led by UNESCO (Mulder et al., Table 2.1 World economic forum—education for the twenty-first century (adapted from Scheerens et al. (2020) Foundation literacies Literacy and numeracy; scientific literacy, ICT literacy, financial literacy, cultural literacy, civic literacy Competencies Critical thinking, problem-solving, communication, collaboration Character qualities Creativity, initiative, persistence, grit, adaptability, curiosity, leadership, social and cultural awareness 2.2 Knowledge Matters: A Sociological Perspective 25 2007, as cited in Goudard et al., 2020). While these competencies often require a foundation in domain-specific knowledge, expectations frequently overlook this requirement. They are commonly depicted as generic abilities that, when mastered, can work in a wide range of situations. Some arguments surrounding this trend (‘Give a man a fish, and you feed him for a day. Teach a man to fish, and you feed him for a lifetime’) even suggest that the sheer volume of new knowledge being generated diminishes the importance of actual content; it posits that the means of acquiring information have become more crucial than the information itself. This leads some to claim that as knowledge dates so quickly, teaching is no longer valuable (De Bruy- ckere et al., 2015). These claims also underestimate the extent to which knowledge is required to make sense of answers provided by new powerful AI-driven technologies such as ChatGPT. Information in itself, though, does not equate to understanding that information. One can, for example, ask ChatGPT to “describe the decisive moment in the 2003 Rugby World Cup Final”. The answer: ‘In the dying moments of the first half of extra time, England was awarded a penalty in front of the posts. Jonny Wilkinson, England’s fly-half, stepped up and successfully kicked the drop goal, securing three points for England. This iconic moment occurred with just 26 s left on the clock, propelling England into a 20–17 lead’ will, however, not make much sense unless one has some background knowledge of the sport, including what a drop-goal is, what a fly-half is, and what extra time means. Moreover, it is incorrect as the decisive moment actually came in the second period of extra time, when, with 30 s remaining, Wilkinson kicked a drop goal (not a penalty kick) to break the 17–17 tie. Consequently, knowledge is also required to know whether an answer is likely to be correct, reflects biases (such as has been the case in, for example, depictions of people of different ethnicities), or is even simply the result of a so-called AI hallucination (as in this particular case). This all leads to concerns that an exaggerated emphasis on generalised 21st-century skills, and a blind trust in technology conflict with our understanding of human learning as described above, and may fail to adequately serve students—particularly those from disadvantaged backgrounds—grappling with social inequity (Rotherham & Willingham, 2010). All of the above, however, does not mean that education should not have any links to employment (as we will discuss below), nor that complex thinking skills are not important; they very much are. Rather, it underscores the necessity of recognising that domain-specific knowledge is crucial when imparting skills to students, and that generalised lessons on, for instance, critical thinking are not that productive. In this sense, we need to bring knowledge back in. 2.2.3 Bringing Knowledge Back in At the onset of this century, a group of scholars, self-identifying as social realists, expressed discontent with these trends, asserting that they had downgraded knowl- edge in education (Barrett, 2024). This downgrading denies learners access to what 26 2 How Knowledge Matters they call powerful knowledge, which most often affects students from disadvantaged backgrounds the most (Wheelahan, 2010). For this reason, they argued for the need to ‘bring knowledge back in’ (Muller, 2000; Rata, 2012; Wheelahan, 2007; Young, 2007). Taking a realist perspective, they recognise the social nature of knowledge in its production (which withholds it from absolutism) yet reject the reduction of knowledge to knowers (which counters relativism). They do this by acknowledging that some knowledge is more objective than others in ways that transcend the imme- diate conditions of its production (Moore & Young, 2001). In other words, while knowledge is socially produced, some types of knowledge are more powerful, and, yes, ‘better’, than others. With this better knowledge, however, it is not meant that it is beyond debate, or that it is fixed. “Better knowledge means the best knowledge we have, and the best means we have for creating new knowledge for the kind of world we envisage for the next generation” (Young & Lambert, 2014, p. 31). Based on this idea and the theories of Bernstein and Durkheim, Michael Young (2009, 2013) produced a theory of powerful knowledge. He positions the production of powerful knowl- edge within specific social and intellectual groups, often represented by academic disciplines. This disciplinary knowledge holds more power as compared to everyday knowledge because it is produced in “communities of inquiry” that use specific methods to create and validate claims of knowledge (Young & Muller, 2015). In this context, academic disciplines such as mathematics, physics, and history are valuable because they can generate focused discussions that ensure reliability, revisability, and the emergence of new insights (Muller & Young, 2019). The powers of disciplinary knowledge reside in going beyond individual experi- ences, providing individuals with a robust framework to understand the world. For example, it can help students understand that the Earth is round, a realisation that goes beyond the visual appearance of a seemingly flat horizon. Although this concept may seem profound to new learners, the underlying geometry and arithmetic are surpris- ingly straightforward. The ancient Greek mathematician Eratosthenes ingeniously demonstrated this almost two and a half millennia ago. By observing differences in the shadows cast by vertical objects at the same moment in different locations on Earth, Eratosthenes not only inferred the curvature of the Earth, but also calculated its circumference. Rooted in fundamental geometry and arithmetic, his calculations provided compelling evidence for a spherical Earth. Disciplinary knowledge thus provides learners with more dependable interpretations and insights into the world, allowing them to explore topics and subjects their experiences alone would never let them have access to. It also serves as a language that allows individuals to question its own foundations and the authorities from which it derives. The acquisition of disciplinary knowledge enables individuals to envision alternative possibilities and think beyond the confines of their immediate surroundings. For these reasons, Young advocates inclusive access to powerful knowledge in education, asserting equal educational rights for all children. He argues that if ‘better knowledge’ exists, everyone should have the right to access it (Young & Lambert, 2014). This does not mean that powerful knowledge is the silver bullet for all our problems, and that we should just start teaching the ‘best’ knowledge in our disci- plines and everything will be alright. Muller (2023) points out that this is expecting 2.3 Knowledge Matters: A Democratic Perspective 27 too much from the concept. What constitutes powerful knowledge changes as disci- plinary knowledge itself evolves; in some areas, such as environmental science, quite rapidly (Yan, 2015). Furthermore, what counts as powerful knowledge in particular disciplines, such as history, is itself subject to debate, and contextually differentiated (Sheehan, 2021). For these reasons it is important to clarify that this perspective does not assert the presence of an unchanging or universally accepted canonical knowl- edge across disciplines. It is also important to point out that disciplinary knowledge in the academic disciplines themselves on the one hand, and disciplinary knowl- edge in schools on the other, are two different things. Disciplinary knowledge in the disciplines needs to be translated by expert teachers and subject specialists. While powerful knowledge cannot serve as a silver bullet, it can help us as a future-oriented principle where we strive towards a more desirable future and reappreciate knowledge and the emancipatory and democratic qualities it brings. 2.3 Knowledge Matters: A Democratic Perspective In a democratic society devoted to equal opportunities and governance for and by the people, one of our democratic responsibilities, namely deciding what our children should know, is complex. What we know plays a significant role in shaping our identities and who we are, or are perceived to be (Moore, 2000). Deciding what our children should learn does not only play a role in what we want the future of our society to be like, but also in who we want our children to become. This leads us to a very difficult question: What kind of knowledge is so important that we will not leave its transmission up to chance? A question that becomes ever more difficult to answer as the production of knowledge in our society grows. One could argue that the response to this question depends heavily on the answer to a different question: what is the purpose of education? While there are many possible answers, most can be divided into four broad categories: personal empowerment, cultural transmission, preparation for work, and preparation for citizenship (Wiliam, 2013). These broad philosophies do not exclude one another but are sometimes in conflict. A balance is needed, as one without the other can have unwanted consequences. For example, an education system focused only on preparation for work could lead to an instrumentalist view where only knowledge that is considered “useful” for economic growth is taught. Such an instrumentalist view might overlook the intrinsic value of knowledge for personal empowerment and cultural transmission and diminish the broader benefits that a well-rounded education can bring. It also lends itself to the inference that employers should have a significant voice in deciding what children learn, and it’s not obvious that they would wield this power in a way that benefits society as a whole. The knowledge and skills we think everyone will need for further economic devel- opment could also be misguided, since we don’t have a crystal ball to tell us what the future holds. On the other hand, an educational system solely focused on personal empowerment might lead to a disconnect between the knowledge and skills taught 28 2 How Knowledge Matters in schools and what students need to function in the labour market upon which our societal prosperity relies. Discovering and maintaining this balance and determining what should be taught is a project that never ends, given that an effective system today may prove inadequate in the future as society changes. However, choices need to be made. In the next paragraphs, we will take a closer look at the aim of preparing our children for citizenship in a democratic society and how knowledge plays an important role in this process. When examining knowledge from a democratic perspective, one cannot overlook the ideas of E.D. Hirsch and his notions of cultural literacy. Hirsch (1988, p. xiii) defined being culturally literate as “possessing the basic information needed to thrive in the modern world”. With this concept, he pointed out the significance of back- ground knowledge in language comprehension, and how disadvantaged students rely primarily on schools to provide this knowledge. As explained in the first section, student’s already acquired knowledge (we refer to this as ‘background’ knowledge) acts as a cognitive scaffold, enabling students to connect added information and their pre-existing understanding of the world. When communicating, we assume a vast amount of shared background knowledge. For disadvantaged students, who may face limitations in exposure to a rich array of experiences and information outside schools, this can limit them, not due to a lack of ability, but because of a lack of access to knowledge (remember the scorpion in the section on reading comprehension?). This is why, when knowledge is no longer explicitly addressed in schools, or assumed to be primarily constructed from children’s own experiences, the most disad- vantaged students suffer the most. This is problematic not only for these individuals but also for society as a whole. As Hirsch (2009) states, shared knowledge fosters a sense of commonality among diverse citizens in a democratic society. In a society characterised by cultural diversity, a common body of knowledge ensures that citi- zens can engage in informed discussions, debates, and decision-making processes. It promotes a sense of belonging and inclusivity, as individuals draw upon shared references that go beyond individual differences. One can imagine that when access to this shared knowledge is hindered or not evenly distributed, issues of inequality in education may widen. This is why the erosion of the role of knowledge within the educational

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