⊙Table of Contents for Global Education Outlook 2021, Issue 1
Abstract “Deep learning” refers to the overall participation of learners in teaching. The three perspectives of deep learning—subjectivity, dialogicity, and collaboration—are precisely the defects of exam-oriented education classrooms. Deep learning starts with problems, focusing on elevating from “textbook-level” to “beyond textbook-level.” The pillars supporting deep learning are “dialogue guidance” and “reflection guidance.” Deep learning is a marker of classroom transformation. Innovative teachers face the challenges of absorbing the nutrients of learning science, practicing unit design, and transcending “individual abilityism.”
Keywords Deep learning; unit design; driving questions; situational learning theory; classroom transformation
Author Introduction
Zhong Qiquan / Professor at East China Normal University Institute of Curriculum and Teaching Research (Shanghai 200062)
1. Definition of Deep Learning
(1) “Deep Learning” vs. “Surface Learning”
“Deep learning” (深度学习) is defined as “the active participation of learners in teaching,” which aims to cultivate general abilities encompassing cognitive, ethical, social capabilities, and cultivation, knowledge, and experience. Thus, discovery learning, problem-solving learning, experiential learning, and inquiry-based learning all belong to the category of “deep learning.”［1］Deep learning does not start from textbooks transmitting specific knowledge but begins from revealing problems.In deep learning, learners revolve around problems, leading to various thoughts and solutions, while teachers assess what learners “know” and “can do,” thereby establishing learning rules and engaging in a series of exploratory activities aimed at acquiring the knowledge and skills necessary for problem-solving.
Swedish scholar F. Marton conducted a reading experiment with university students—asking them to read certain chapters of textbooks and news reports, inquiring how they read, analyzing their understanding of textbooks and news reports. The experiment analyzed memory retention after 5-6 weeks as a learning outcome to reveal the relationship between reading methods and learning outcomes. The research indicates that students’ reading methods can generally be divided into two types: one type does not deeply understand the content of textbooks and news reports, merely identifying problem points and focusing on a certain aspect of the article. The learning outcomes of such students are inadequate. The other type focuses on the author’s writing intention or the key points of news reports, as well as what conclusions can be drawn from them, representing a reading method that grasps the overall meaning of the textbook knowledge and news reports. The learning outcomes of such students are excellent.
The so-called “depth” and “surface” of learning specifically begins by comparing the differences between “deep learning” and “surface learning.” This research on “learning differences” has triggered studies on deep learning by N. Entwistle and others. They believe that “deep learning” refers to “meaning-seeking learning,” while “surface learning” refers to “learning focused on individual terms and facts.” The characteristics of the two types of learning form a clear contrast (see Table 1)［2］.
Table 1 Characteristics of Deep Learning and Surface Learning
This means that the difference between the two lies in that deep learning seeks the link between meaning and knowledge, exploring common paradigms and principles, pondering evidence verification and critical discussion, and recognizing the level of self-understanding; while surface learning is a fragmented memory and reproduction of knowledge, signifying meaningless and purposeless learning.
J. McTighe and G. Wiggins found that starting from the characteristic of “exploring common paradigms and principles,” learners think about the connections between knowledge and other knowledge, linking it to experiences, and generalizing it, which can generate deep learning. In other words, deep learning lies in the learner’s ability to develop the “structuring” and “linking” of knowledge［3］. J. Biggs and C. Tang summarized the characteristics of the two types of learning using “verbs.” The characteristics of deep learning often involve higher-order cognitive functions such as “reflection,” “application to different types of problems,” “hypothesis formation,” and “linking to principles,” aiming for authentic problem-solving. In contrast, surface learning often involves mechanical memorization methods such as “memory,” “identification and naming,” “text comprehension,” “rephrasing,” and “description,” aiming for formal problem-solving.［4］
(2) Perspectives of Deep Learning
In the face of rapid social changes, education that simply transmits knowledge has fallen behind the times. As L. D. Fink pointed out, the meaning of learning in deep learning “goes beyond mere knowledge acquisition; it aims to develop broad skills and attitudes (abilities), nurturing the growth of learners’ personalities (humanity). It can be said that it is a learning perspective based on transforming learning paradigms.”［5］While acquiring knowledge is certainly important, what matters more is the ability to confront various problems arising from the surrounding environment and collaborate with different others to seek optimal solutions. In other words, “what can be done”—the ability to use various knowledge and information, possess one’s own thoughts, and create new concepts—becomes particularly important. Therefore, the trend of “core competencies” emphasizes three pillars: 1. Understanding what has been learned and what can be done (acquiring vibrant knowledge and skills). 2. How to apply the understanding and mastered knowledge (cultivating the ability to confront unknown situations with thinking, judgment, and expression). 3. How to navigate life and create a wonderful life (learning disposition and personality cultivation). Thus, what is important is not merely to memorize knowledge but for learners as subjects to connect their learning with real society and practical life during the learning process, seeking deep and broad learning through dialogue with others.
Deep learning encompasses three perspectives. First, subjective learning. Subjective learning refers to children as learners being able to control their own learning, solving problems with their capabilities, and through repeated exercises, recognizing the process and outcomes of such learning, ultimately regulating learning based on their own abilities. Subjective learning particularly emphasizes the “problem setting” and “pre-setting” at the initial stage of teaching, as well as the “reflection” at the conclusion stage of teaching. The type of problems set and faced determines whether children can achieve authentic situational learning, that is, whether they can produce genuine and meaningful learning. Pre-setting can generally be divided into two types: one is to clarify the process of problem-solving, and the other is to vividly depict the goals of learning activities. “Reflection” refers to learners becoming aware of the meaning and value of their learning and sharing it with others. The contexts of reflection generally have three layers of meaning: one is to confirm the reflection on the learning content; the second is to link the current learning content with past learning content or to reflect on it in a generalized manner; the third is to link the learning content with oneself, perceiving the changes in oneself.
Second, dialogic learning. In teaching practice, the meaning of learning is not based on the transmission of knowledge and understanding of explanations but on the functional application and exploratory activities of children’s knowledge. Learning is “a journey from the known world to the unknown world.” Therefore, Sato Gaku defines “learning” as a triadic dialogical practice: “the encounter and dialogue with the objective world, the encounter and dialogue with others, and the encounter and dialogue with oneself.”［6］ In the process of dialogic learning, having a clear problem consciousness and forming links between knowledge and skills is necessary, and sharing each learner’s reflective experiences is also important.
Third, collaborative learning. Classroom learning activities unfold through communication between teachers and students, and among classmates. This communication is not a relationship of “mutual monologue” but is organized as a relationship of “mutual listening.” In an exchange of monologues, “learning” cannot be formed［7］, because the expression of mutual monologue typically remains at the exchange of known things and cannot face the exploration of unknown things. However, the “dialogical interaction” based on the relationship of “mutual listening” can be realized—creating new concepts that can be collaboratively sought with others based on problem-solving, with the theoretical foundation being Vygotsky’s “zone of proximal development.” Just as PISA’s investigation of “collaborative problem-solving ability” indicates, in problem-solving situations, it is not about going solo but rather about collaboratively facing problem-solving with many people that is important. This dialogue with diverse others has the following three values: one is to structure the knowledge and skills as information that explains to others. Children using their mastered knowledge and skills to explain to others can transform their knowledge into structured knowledge and information with certain connections. The second is to gather diverse information from others. Utilizing the information provided by others helps further enhance the quality of structuring. The third is that while producing new knowledge together with others, one can expect to point towards actionable problem-solving. Therefore, to promote collaborative learning, interactions among children can consider the following three points: one is what knowledge children possess, two is how children process the knowledge and information, and three is what outcomes children expect. Children can compare, link, process, and construct their own knowledge and information with that of others and based on external resources, thus forming new thinking. We can set three types of interaction contexts—situations for internalizing received information, situations for processing information content, and situations for externalizing information—thus richly constructing a “broad” dialogue context. This is very important. In practical teaching, in addition to considering the quality and quantity of information, as well as the methods of construction, specific learning activities and information forms, and learning environments must also be prepared. For example, we can expect thinking tools to guide learners to enhance the quality of dialogue learning based on oral language. Because once information is “visualized” and “operationalized,” it can concretely display the appearance of children engaged in subjective dialogue learning alongside themselves. Through improving teaching efforts, we can showcase the expansion and deepening of children’s thinking.
Deep learning is not a specific teaching method, nor does it deny the role of teachers in school education, but it requires teachers to grasp the essence of learning, constantly thinking about the ideal model of learning necessary for cultivating children’s qualities and abilities. Here, three points must be clarified: one is that “deep learning” merely refers to the learning methods necessary for cultivating children’s qualities and abilities. Deep learning does not treat “subjectivity,” “dialogue,” and “collaboration” as independent elements to be explained, nor does it separately formalize different contexts and activities for each element. Two is that deep learning does not focus on teachers’ teaching behaviors but rather on the quality of learning generated by children; it is not solely based on teachers issuing orders—teachers say to start a dialogue, and thus children passively engage in dialogue. Three is that quality deep learning is both subjective, dialogic, and collaborative learning. If these elements are disassembled and implemented separately, it is not a good strategy. Deep learning should lead to the reconstruction of knowledge structures. That is to say, “subject teaching” is not merely an increase in the quantity of knowledge but a change and refinement of knowledge structures that possess unique value. This means the formation of a state in which “ways of thinking and insights formed in response to different subject characteristics” are developed, that is, the cultivation of “subject literacy.”
(3) Learning Theories Supporting Deep Learning
The learning theories supporting deep learning mainly include three types. One is the knowledge construction process of individuals. Scientific insights into deep learning have been validated in the field of learning science based on the research outcomes of cognitive science, cognitive psychology, and developmental psychology. Research in learning science is based on the view that “knowledge is socially constructed,” confirming that individual knowledge construction processes can be summarized into the following five characteristics: 1. Knowledge is actively constructed by each person. 2. Knowledge is constructed based on existing knowledge. 3. The knowledge construction process is dependent on the subject area and is constrained by innate internal conditions. 4. Human understanding activities are conducted in a socially interdependent context. 5. Once knowledge is constructed, it is difficult for individuals to naturally engage in reconstruction (further deepening and revising) activities.［8］ “Having knowledge” can be said to enhance the accuracy of predicting both near and distant futures, enabling better actions. Therefore, so-called “knowledge” fundamentally can only be constructed through oneself, and this knowledge can only be formed by linking it with the existing knowledge that learners possess. This indicates that even in the same class studying the same content, each child’s knowledge construction process may be different. Humans are natural learners. From the moment of birth, they can actively engage in various types of learning, such as concepts of numbers and distinguishing objects, based on instincts accumulated through long-term human evolution; this is the “power” of learners engaging in “learning.” Through gradually exercising thinking, judgment, and expression, learners can construct knowledge that may be effective in the future. Here, learning is not merely the accumulation of experiences and replication of information but acquiring knowledge and skills in a conceptualized manner that can be utilized in the future. At the same time, by acquiring concepts in specific fields, learning in those fields becomes easier. This is because, at the outset of learning, knowledge that learners are already familiar with is more likely to form links. Fundamentally, humans are a species that engages in collective behavior as a basic way of life. Therefore, the exertion of various qualities and abilities unfolds in a socially interdependent manner. That is, it unfolds in a manner that is dependent on social culture—what is expected in what social culture, what kind of learning processes and outcomes are anticipated from whom—and is influenced by social motives, regulating the learning content and methods.
Two is constructive interaction. The fifth characteristic of knowledge construction mentioned above, that is, “once knowledge is constructed, it is difficult for individuals to naturally engage in reconstruction activities,” is a key focus supporting learners’ deep learning. Once individuals believe they “understand,” they often find it difficult to naturally engage in deepening or revising their knowledge reconstruction activities. Therefore, constructed knowledge, even if superficial or erroneous, is often not recognized by the learners themselves. What is important here is the existence of others who can provide opportunities for correcting the knowledge constructed by learners. When learners convey their understanding of knowledge to others through “discourse,” they often cannot effectively articulate it, prompting them to reflect on their understanding, that is, generating “questions” and “inquiries” related to deep understanding, requiring deeper explanations to be constructed. This fact indicates that the “constructive interaction” learning mechanism inherent to all individuals is at work. In summary, the process of learners deepening their understanding through communication with others, starting from “understanding,” is the “constructive interaction.” Japanese scholar Yoshio Miyake analyzed the dialogue process of graduate students around the question “How does a sewing machine sew?” The results showed that understanding exists at different levels.［9］ The opportunity to reach a deep level occurs when the state shifts from “understanding” to “not understanding.” The reason for this state change is the new questions (problems) generated from different perspectives of others. The understanding levels exhibited in the dialogue between the two graduate students were different. Although there was a level difference at the beginning, the shallower-level student B questioned the deeper-level student A, prompting student A to recognize their “not understanding” state and engage in further exploration, thereby deepening understanding, while A’s explanation also deepened B’s understanding. Therefore, it is not the case that those who understand teach those who do not understand unilaterally, but through questions from others, new “problems” arise from their understanding states, thus deepening each other’s understanding. Once individuals believe they “understand,” it is often difficult to deepen understanding, but through interaction with others, new “problems” and “questions” of “not understanding” can be generated, allowing knowledge construction activities to continue. It can be said that the so-called “constructive interaction” refers to the process in which every participant in the field faces a common “problem,” and their thinking methods before and after participation undergo a “constructive directional change.”
Three is typical and adaptive practitioners. Through constructive interaction, knowledge can be repeatedly reconstructed and understood, ultimately deepening and constructing into high-quality knowledge, that is, advancing “proficiency” through deep learning dependent on that knowledge domain. Yoshiyuki Hatano distinguishes two types of practitioners: one is “typical practitioners” who can quickly solve familiar problems; the other is “adaptive practitioners” who can flexibly organize knowledge and skills in novel situations, pointing towards new realms. At first glance, typical practitioners are efficient but can only function within predetermined states and situations. In contrast, adaptive practitioners can flexibly apply their knowledge and skills in any situation. The so-called cultivation of core competencies primarily focused on activating knowledge and skills is not about cultivating typical practitioners who apply learned knowledge skills verbatim, but rather about cultivating adaptive practitioners. Therefore, to evaluate whether true learning has taken place, it is important not to measure whether one can directly apply learned knowledge but rather to examine whether one can flexibly apply learned knowledge. Then, what kind of learning environment is needed to cultivate adaptive practitioners? The conditions for adaptive practitioners’ environments include: 1. Constantly encountering novel situations; 2. Engaging in dialogical interaction; 3. Being liberated from rigid external constraints; 4. Being part of a collective that values understanding. Specifically, the first condition is frequently encountering novel phenomena that differ radically from predictions based on existing knowledge, thereby forming new learning motivations—correcting the possibilities of applying already mastered knowledge and seeking new knowledge construction. The second condition is dialogical interaction, meaning that in the process of conveying their insights to others and absorbing others’ insights, motivations such as “realizing the insufficiency of their understanding,” “analyzing their understanding in detail,” “organizing their thoughts,” and “correcting from various perspectives” are at play. The third condition is to not rush due to external constraints but to respond calmly. Only then can solid deep understanding activities unfold. If one is overly eager for results, they will only be limited to mastering procedural knowledge. The fourth condition is recognizing the importance of deep learning, reflecting one’s value by playing a role as a member of the group. Once a group encourages deep understanding, individuals are more likely to engage in metacognition, which helps recognize the importance and future usefulness of understanding in that knowledge domain. In other words, when encountering new situations, learners can further trigger activities that deepen understanding.
2. Characteristics of Deep Learning Design
(1) Teaching Starting from Children’s Questions
Based on core competencies, deep learning places great importance on cultivating children’s problem-finding and problem-solving abilities.“Questioning” is an indispensable factor in achieving deep learning. This is because children are the subjects of learning; the questions, problem awareness, and inquiry spirit generated by children are the driving forces behind their learning. As required by the new era, children need the ability to actively discover problems and collaborate with others to solve problems. Every child must possess the ability to identify various “problems.”
One significant manifestation of exam-oriented education classrooms is the loss of the “question-and-answer” dynamic.［10］ The mere demand to “know the answers” leads to the extinction of “knowledge desire” (the desire to know) and “learning desire” (the desire to learn). The loss of the “question-and-answer” dynamic merely reflects the desire to obtain ready-made answers without thought, thus shortening the state of “question-and-answer.” This state distorts the process of “question-and-answer,” signifying a loss of the essence of learning. Humans begin to think from questioning. The research on the topic of “questioning” (where to start questioning, when to question, how to question) is also indispensable for achieving deep learning. In this sense, deep learning emphasizes teaching that starts from problems.
Children may not be able to immediately adapt to teaching that starts with questioning. So, what is difficult about children’s questioning in teaching? There are four reasons［11］. The first is that children lack perspectives for forming questions. Children often enter teaching activities in a confused state without knowing how to form questions, feeling bewildered. Therefore, teachers must provide children with perspectives and clues to form questions. The second is not understanding the “effectiveness” of questioning. In questioning teaching, teachers need to set aside time for children to solve problems and give children a place for questioning in unit design; otherwise, children will not experience the effectiveness of questioning. The third is the lack of an environment for posing and solving problems. To enable children to form and solve problems, an environment must be created where children can form and solve problems even without teacher instructions. In an environment that excludes children’s questioning and is solely teacher-centered, children will not experience the effectiveness of questioning teaching. The fourth is the lack of a sense of security to freely form and solve problems. If teachers consistently adopt a dismissive attitude, responding with “I’m busy, don’t interrupt,” then children will not be able to question naturally. In other words, an environment must be created where children can actively ask questions and engage in collaborative problem-solving. Without children’s questioning, there can be no true “deep learning.”
At a part-time high school in Boston, the traditional teaching convention of “teacher questions, student answers” was transformed into an experiment that allowed students to “learn to question.” The learning topics included science, mathematics, and social studies taught by three teachers. Class size: 20 students. Question focus: smoking. The purpose of organizing questions: to develop an inquiry plan. To the surprise of the three teachers, students enthusiastically raised a large number of questions that they had never seen before: 1. Why is smoking harmful? 2. What does a cigarette contain? 3. If smoking is dangerous, why is it allowed to sell cigarettes? 4. Cigarettes contain numerous chemicals, why? 5. Who invented cigarettes? 6. Why do warnings need to be attached to cigarette packs? 7. How can one quit smoking? 8. What can be done to prevent stores from selling cigarettes? 9. Are there places for smokers to join for quitting, similar to detox centers? 10. When will we see a smoke-free world? 11. Why is smoking legal? 12. What can be done to ban smoking? They then prioritized the questions and ultimately chose “Why do warnings need to be attached to cigarette packs?” as the theme for their inquiry plan. This questioning mobilized students’ high learning enthusiasm. They first investigated the focus on the health impacts of smoking, such as why these chemicals are added to cigarettes, the mechanism of nicotine addiction and its elimination methods, as well as how smokers start smoking, how to quit smoking, and how to participate in “World No Tobacco Day” activities, etc. Through this inquiry, they learned that cigarettes contain three harmful substances—tar, nicotine, and carbon monoxide, along with about 70 carcinogens and approximately 200 other harmful substances, which can directly lead to various cancers and nicotine dependence (vasoconstriction) and arteriosclerosis. They also understood that banning smoking is a complex issue intertwined with multiple factors. Students learned the chemical components of cigarettes and the scientific mechanisms of nicotine addiction, gaining insight into the impact of smoking-related diseases on individuals and society, as well as understanding the laws and public policies regarding smoking bans. Through such teaching, students gained a deep understanding of the impact of smoking and used their knowledge to express their stance against smoking through various methods. Finally, they analyzed the warnings on cigarette packs, gaining numerous insights, and considered eye-catching phrases for cigarette pack designs such as “Killer Cigarettes!!” “Quit Smoking—Doctor’s Orders!” “Smoking? Cancer!” “Smoking Equals Death” “Smoking is Chronic Suicide!” “Life Hanging by a Thread!”
This case reflects a characteristic where students’ questions are no longer fragmented but rather a “series of related questions” bursting forth. Just like a farmer cultivating virgin land, it requires plowing, weeding, sowing, fertilizing, watering, and then guarding the seeds to sprout; it is a systematic operation that requires careful nurturing and skill. Merely plowing or watering will not cause the seeds to sprout. This fact indicates how much wisdom energy will be generated once students learn to ask questions, while the teachers supporting the questioning activities possess high teaching organization skills. “Learning to ask questions” is a fundamental skill for students to think and engage in deep learning; it is a turning point leading to new discoveries and an important link in enhancing students’ awareness that “they are the masters of their own learning.” The teaching that emphasizes “learning to ask questions” signifies the end of teacher-centered teaching and the emergence of learner-centered teaching. Schools should teach students the methods of questioning. Because if teachers only ask questions and students only answer, then students will not understand truly valuable questions, but merely learn to answer questions that have ready-made answers. D. Meier pointed out that “excellent teaching methods start from students understanding how to ask questions and students being able to answer the questions they truly want to know themselves.” By learning to ask questions, students mobilize their wisdom, engage in exercises of diverse thinking abilities, and simultaneously cultivate their character and personality unconsciously.［12］
(2) Focus on Rising from “Textbook Level” to “Beyond Textbook Level”
The “learning topic” is the starting point for children to engage in inquiry in teaching. Therefore, the selection and design of deep learning topics are crucial. Canadian educator K. Egan believes that worthwhile topics for exploration should reflect the changing interests of students aged 5 to 18, possessing sufficient complexity, diversity, and plurality, and that topics meeting this deep standard require three conditions: “breadth,” “depth,” and “participation.” Specifically, “breadth” requires topics to encompass multi-disciplinary materials. “Depth” requires topics to gradually increase the intricacy of inquiry. As connections deepen, topics must allow for a more complex and nuanced understanding of the nature of knowledge. “Participation” requires topics that can withstand multi-faceted exploration, meaning they must not only generate breadth and depth that transcend mere accumulation of knowledge but also provide topics for each individual’s cultural activities.［13］ If learning topics lack challenges, students will not attempt errors, make discoveries, or experience the joy of learning. Key points for setting learning topics include: 1. Topics that puzzle children, meaning topics that can lead children to attempt errors, reasoning, and verification processes. 2. Topics that children cannot easily solve using existing knowledge, skills, cognitive methods, and strategies. 3. Topics that allow children to experience attempts, errors, reasoning, verification, leaps, and discoveries, enabling them to grasp new knowledge and skills, cognitive methods, and strategies. 4. Topics that can foster children’s persistent interest in seeking “answers” and love for inquiry.
Achieving deep learning is quite challenging. The so-called “participation” does not merely refer to learners sitting in chairs; rather, it refers to each learner being fully engaged in an enterprising state of participating in learning activities. Therefore, the entire class must tackle the issues of “not understanding” and “not being able to.” For some learners, problems that are already known and easy to answer cannot trigger their deep thinking, making it difficult to ensure their deep learning. We need to break free from traditional teaching that satisfies “understanding” and “ability” and challenge the teaching of “not understanding” and “not being able to” as the true requirement for deep learning. Abandoning the teacher’s explanation-centered approach in favor of setting “project-based learning” (PBL) and launching long-span challenges is the key to the success of deep learning. Here, the carefully designed learning topic should be a question that, even if “not understood” today, can be “understood” tomorrow; even if one person “does not understand,” they can rely on the “scaffolding” built by teachers and classmates, aided by collective wisdom to comprehend the topic (i.e., the zone of proximal development). Confronted with such a topic, at the moment of questioning at the beginning of teaching, all learners must be in a state of seeming understanding but not completely understanding, and teachers need not worry about this. In other words, in subject teaching, it is necessary to design questions that make students feel unconfident or uncertain.
At the pioneer school of Sato Gaku’s “learning community,” almost all teaching designs encompass two parts—sharing learning design (design based on textbook level) and challenging learning design (design beyond textbook level).
For example, a public class on “Reflection of Light” (Physics for Grade 1) taught by teacher Mitsuaki Shiozaki at Kijima Junior High School began with distributing laser pointers, mirrors, and whiteboards to each group to allow students to experience the reflection of light. After conducting the experiment of “the angle of incidence equals the angle of reflection,” the teacher and students jointly entered the learning of challenging topics. In the challenging topic, Shiozaki teacher pasted prepared “reflectors” (reflectors behind bicycles, reflectors on workers’ uniforms at night, reflectors used as road signs) on the blackboard and illuminated them from an inclined surface. According to the law of reflection, they should reflect like mirrors, but in reality, when students stood in the reflection position, they could not see any reflected light. Regardless of the direction from which the light was emitted, it would reflect towards the light source after passing through the reflector. Students exclaimed in surprise, what mechanism is hidden in the reflector?
Then, Shiozaki teacher distributed a small piece of vinyl sheet (a transparent yellow reflector) to each student, allowing them to explore the “mystery of the reflector” in groups. This vinyl sheet has a rough surface, but the back is very smooth. Students observed under a microscope and created models, discovering that there are countless triangles on the reflector, but the microscope only reveals a blurry image. Soon, a group of students who solved the mystery appeared, discovering that the reflector (vinyl sheet) was embedded with countless prisms. In fact, there is another type of reflector, with a spherical granular configuration. This involves the law of refraction of light, which is also the theme of the teacher’s next lesson. The brilliance of Shiozaki’s teaching lies in achieving deep learning through the scientific inquiry process of “model exploration.”［14］
The pioneering school of Sato Gaku’s “learning community”—Chigasaki City Hamano Sato Elementary School—introduced numerous practical reports on deep learning through the “Shonan Seminar.” Teacher Junshi Hamida taught a third-grade science class titled “The Incredible Colors of Flowers,” first prompting students with photos of 12 types of flowers. After each child created a “Flower Encyclopedia,” labeling the names and classifications of the flowers, they focused on the colors of the flowers, using ultraviolet photographs to present the colors of flowers as recognized by insects, discovering the differences in insects residing within the flowers, organizing an engaging learning experience. This was an excellent study of teaching materials and learning design.［15］ Teacher Hiroshi Hori’s fifth-grade art class on “Clay Sculpture” used “clay sculpture” to express the artistic worlds of K. Kollwitz, V. Kandinsky, and J. Miro. Although these challenging learning designs require considerable effort, teachers are enthusiastic about them due to the endless charm and possibilities of “challenging learning” (deep learning). What is invaluable is that children are also enthusiastic about challenging learning, and the lower the academic ability, the more interested they are in challenging learning. This is because, through “challenge,” a solid foundation for learning can be established, helping to escape the predicament of low academic ability［16］.
The reason challenging learning has such efficacy contains two secrets: first, in groups, the learning of four students is generally balanced. Observing the four children in a group, despite their significant differences in academic ability and knowledge, the differences in their thinking inquiry abilities are not as pronounced. Second, unlike high-ability children who can “develop from the basics to advancements” and “from understanding to application,” low-ability children need to understand foundational knowledge through challenging learning and form understanding through application. This explains why low-ability children are particularly keen on learning through challenging learning. Therefore, challenging learning can yield miraculous effects for both the realization of deep learning and the attainment of academic goals.［17］
(3) The Two Pillars Supporting Deep Learning: Dialogue Guidance and Reflection Guidance
Focusing on the three types of dialogue, “dialogue guidance” is an important support for deep learning. “Dialogue guidance” refers to teaching that involves mutual dialogue. Here, it is emphasized that distinguishing whether the dialogue is between one person or several people is meaningless. This is because when individuals engage in deep thinking, they may engage in self-talk or utilize unspoken internal language to repeatedly dialogue with themselves. Similarly, solely emphasizing dialogue with others is also inappropriate. Merely transmitting each other’s thoughts, even if the scope of thinking is expanded, does not necessarily lead to depth. The dialogue in deep learning is not limited to dialogue with others but refers to “three types of dialogue”—dialogue with oneself, dialogue with others, and dialogue with objects, as well as the interactions between these three dialogues. Particularly, only through repeatedly weaving one’s thoughts and insights through dialogue with oneself and dialogue with objects can one move toward deep learning. So, what kind of dialogue can enhance thinking? The dialogue in deep learning is not only “dialogue with others” but also “dialogue with objects” that are laden with the excellent thoughts of sages, natural phenomena, mathematical phenomena, or moral values, as well as “dialogue with oneself” aimed at constructing one’s framework and mental models of thought. Historically, a fundamental deficiency in “collaborative learning” teaching has been the weakness of these three dialogues, especially the dialogue with oneself. To construct a “thinking classroom culture” in deep learning, the entire class must approach problems that are at the cognitive limits—every learner possesses preliminary thoughts; almost all learners find themselves in a state of “not understanding”; learners feel the joy of “not understanding”—thus, genuine exploratory learning becomes possible. This journey from “not understanding” to “discovering joy” is a philosophy of deep thinking. When learners weave the meanings and relationships of objects in their explorations, the “philosophical level” of dialogue is constructed, and it signifies that the precision and frequency of true “deep learning” can be realized. “Philosophical dialogue” has various meanings, dialogue methods, and practical cases.
Japanese scholars have pointed out seven rules that must be followed in the dialogue of “problems, thinking, and expression.” Japanese scholar Kazuo Kobayashi advocates elevating the quality of classroom dialogue to the height of “philosophical dialogue” rules, including: 1. Speak freely. 2. Do not negate or embellish. 3. Allow silence, and focus on listening. 4. Engage in mutual exchange. 5. Speak based on experience, not just knowledge. 6. Allow for inconsistent or changing views. 7. Be tolerant of misunderstanding.［18］ Here, “speak freely” is the iron rule of philosophical dialogue. Creating an environment where students can speak freely is easier said than done. This is because the traditional teaching goal is to achieve “understanding” and “ability,” and every child believes that revealing their “not understanding” or “not being able to” to others is a shame. Therefore, if teachers merely tell learners “to speak freely,” “the classroom is a place for making mistakes,” “if you don’t understand, say you don’t understand,” etc., it will not work. To open this situation, it is necessary to create an environment for paired discussions and group discussions, allowing learners who may not dare to express themselves in front of the entire class to share their thoughts. Pair discussions are particularly effective for creating a dialogue learning environment. Because in pair discussions, there are no bystanders, only speakers and listeners, deep learning can be achieved more easily. Of course, to ensure genuine deep learning, it is not enough to merely introduce pair discussions and group discussions; the quality of pair discussions, group discussions, and “dialogue with others” in the entire class must also be enhanced. The important issue in constructing a classroom culture of “thinking” based on philosophical dialogue is how to discover the value of “not understanding,” continuously probing the basis and specific cases of claims with questions such as “why, how, where to start, for example,” striving to uncover each other’s “misunderstandings.” When relationships of mutual discussion and listening form between learners, it can be said that philosophical dialogue has occurred. In such a classroom, expressions like “Oh, I see!” “Good example, understood!” “Hey, what’s going on?” “Hmm, that’s how it is.” “Hey, that’s great!”—these expressions filled with learners’ joyful laughter reflect the rich and high-quality process of deep learning, revealing the dramatic climax of each learner’s learning.
Focusing on the level of reflection, “reflection guidance” is another indispensable support for deep learning. Reflection guidance corresponding to deep learning needs to meet three conditions: based on subject literacy and deep learning; subjective dialogical deep learning; and learners’ awareness of their learning depth. Otherwise, genuine deep learning cannot occur, nor can the three effects of deep learning be enhanced—“knowledge can be retained in long-term memory, can be flexibly applied in unknown situations, and both teachers and students can experience the joy of learning.” To achieve genuine deep learning that meets the above three conditions, the “reflection” at the end of teaching aimed at learners’ awareness of their learning depth is indispensable. As previously mentioned, reflection has three functions: 1. Confirming learning content. 2. Linking the current learning content with past learning content, understanding and generalizing it. 3. Connecting learning content with oneself, experiencing personal changes. Recently, many teachers place a factual confirmation as a “summary” at the end of teaching, focusing on consolidating knowledge and skills. This type of reflection at the end of teaching not only confirms factual summaries but also links the learned knowledge and skills with existing knowledge, understanding knowledge in new and different contexts, and applying knowledge is commendable. So, what kind of reflection guidance can realize deep learning? Traditional teaching research lacks dialogue guidance and reflection guidance at the end of teaching. Although previous research has pointed out the importance of reflection, reflection at the end of teaching is merely a decoration. Many teachers do not recognize what “reflection” is and have rarely interpreted “the value and meaning of every learner’s learning process.” When we are committed to achieving genuine deep learning, whether we can set the ideal reflection based on subject literacy as a directional goal and conduct reverse design teaching is the key to success. At the end of learning activities, conducting residual reflections is very important. Specifically, reflection can allow learners to feel numerous resonances, one of which is a sense of fulfillment［19］: At the end of learning activities, even without words, one can feel a pleasant mood, perceiving a sense of fulfillment in the learning activity, and looking forward to the next learning activity. This sense of fulfillment is supported by the following elements: one is a “sense of achievement.” It is important to produce a feeling of “indeed” at the end of learning activities, actually feeling “I can do it” or “I can probably do it.” Two is the “sense of self-efficacy.” It is also important to feel personal growth at the end of learning activities. Feeling a sense of efficacy allows one to affirm their posture in the learning activity and triggers motivation for participating in subsequent learning activities, generating a surge of energy while gaining self-trust. Three is the “sense of unity.” Feeling the joy of “everyone learning together” through learning activities and realizing the value of collaborative learning is equally important. In learning activities, collective activities exceed individual activities, where the expectation is to confirm one’s thoughts, communicate, and develop each other’s thinking, genuinely experiencing the superiority and joy of collaborative learning. Reflection is not only narrative but also sharing each learner’s visible insights regarding the meaning, value, and relationships of the learning process, and putting them into practice—this is important for both teachers and learners. Classroom learning is a continuous process of teaching conducted daily; every learner expresses the meaning and value of their learning process, and teachers connect excellent reflections as a whole for thinking and sharing, fostering learners’ proactive attitudes towards subsequent teaching, leading to gradual changes in teaching over time.
3. Challenges in Implementing Deep Learning
(1) Absorbing the Nutrients of Learning Science
Since the 1980s, cognitive scientists have regarded deep learning as “the fundamental principle of school education,” conducting a series of designs, developments, and validations of curricula, teaching materials, and teaching, such as “project-based learning,” “problem-based learning,” “embodied design” based on embodied cognition, “computer-supported collaborative learning” (CSCL), and “mobile-assisted seamless learning” (MSL), etc. These new teaching paradigms share important common features［20］.
Feature one, driving questions. An important characteristic that ensures the quality of deep learning is the “driving questions” that lead teaching. Driving questions must be closely connected to the realities of the world, making learners feel that the problems they face are meaningful and significant. Driving questions provide the context for organizing and conducting project activities. They offer continuity and consistency for the entire project activity aimed at learning goals and scientific practice. As students seek solutions to driving questions, integrative understanding of core scientific concepts develops. High-quality driving questions can evoke students’ passionate learning moods, bringing about the necessity and significance of problem-solving, as well as an excellent sense of authenticity. J. S. Krajcik points out that high-quality “driving questions” have several characteristics: 1. Students can design and implement investigations aimed at answering the questions, meaning they should be “actionable.” 2. They should contain rich scientific content that meets important learning goals and is related to the actual work of scientists, meaning they should be “valuable.” 3. They should depend on important rather than trivial contexts, meaning they should be “context-dependent.” 4. They should stimulate learners’ strong interests, meaning they should be “meaningful.” 5. They should not cause harm to individuals, organisms, or the environment, meaning they should be “ethical.” In deep learning, driving questions can sometimes be chosen by curriculum and teaching designers, or they can be selected by students and teachers. In project-based learning, starting from students’ own driving questions is also beneficial for students to raise meaningful questions, but if students are required to meet the characteristics of high-quality driving questions, it is extremely difficult, especially in meeting the valuable learning goals criterion.
Feature two, focusing on learning goals. To ensure deep learning based on curriculum standards, a thinking process starting from learning goals must be utilized to ensure that the teaching materials meet the main learning goals. Krajcik generally takes three steps in this process: 1. Select core concepts; 2. Decompose core concepts; 3. Develop cognitive tasks that can reflect the desired learning outcomes. The selection of core concepts mainly adopts two criteria: first, core concepts must be explanatory to achieve understanding of various phenomena. Second, core concepts must be necessary for subsequent learning, meaning they have developmental significance, or are essential for understanding related contexts. For example, the properties of matter particles are one such instance. The properties of matter particles can be used to explain many phenomena—whether water evaporates or a chemical reaction occurs, the mass of its particles remains conserved. In fact, the properties of matter particles are stipulated as “core ideas of science” from kindergarten to high school science education frameworks. Because the properties of matter particles are also core ideas necessary for understanding various developmental phenomena such as photosynthesis and respiration. Once the core concepts are selected, they must be decomposed into components and concepts, then expanded and confirmed. The decomposition task is indispensable for curriculum designers to understand the core concepts themselves and which parts of these concepts are needed in curriculum design. Of course, the components must be suitable for students’ ages and grade levels. Next, learning goals are described from the perspective of learning outcomes to make inferences about students’ ability to apply core concepts. Through learning outcomes, core concepts are integrated with scientific practice. Learning outcomes reflect the practices of scientists in professional fields, namely, processes that describe phenomena, use models to explain data, and validate scientific explanations of hypotheses.
Feature three, participating in scientific practice. The goal of science is to explain and predict various phenomena, such as erosion, diseases, rusting, plant growth, free fall motion, etc. Scientists engage in various scientific practices to answer questions—posing questions, designing and implementing investigations, using evidence, and making explanations. Scientists may not gain new scientific understanding based on this series of steps, but explaining and predicting phenomena occurring in the world based on evidence, models, and theories is a common trait of all scientists. In fact, science is a nonlinear attempt. Based on certain results in practice, one may realize that the practice itself should be altered. For example, collecting information about a certain context may correct the original question, collaboratively revise the investigation plan, and the results of data analysis may also lead to modifications in the experimental design itself. In deep learning, students engage in inquiry around driving questions by combining newly learned concepts. They need to continuously investigate driving questions over a certain period, which differs from traditional science teaching. Traditional science teaching resembles cooking based on a recipe, characterized by relying on short-term activities, while deep learning requires students to use an explanation framework that includes three elements (claim, evidence, reasoning). The so-called “claim” refers to students’ thoughts on the phenomena being investigated. The so-called “evidence” refers to the scientific data obtained from various sources, such as observations, literature, and data from investigations that support the claim. The so-called “reasoning” indicates the justification of the claim in relation to the evidence, using appropriate scientific concepts to demonstrate why the data supporting the claim is important. By providing this explanation framework, it supports both teachers and students in the process of explanation in scientific teaching.
Feature four, collaborative learning. Deep learning offers opportunities for students, teachers, and community members to collaboratively explore problems and concepts. When every student in the classroom poses questions, writes explanations, draws conclusions, comprehends information, discusses data, and presents results, they become a “learning community.” For example, teachers require students to comment and provide feedback on each other’s explanations; through collaboration, students construct shared understanding around scientific concepts. Dialogue with classmates and adults outside the classroom also helps in understanding the nature of this professional field. M. Azmitia once stated, “Even if teachers let go of control in teaching, students will not collaborate.” Teachers must help students learn to respect others’ views and develop collaborative skills, which is indispensable. Based on relevant experiences, since students almost have no collaborative experience, it requires a year of honing. For example, first let students write down their insights, then compare them with classmates’ insights, and finally let them write “My insight is based on this reason, similar to or different from my classmates’ insights.” This approach helps students learn to engage in mutual exchanges of their insights.
Feature five, technological support and conceptual creation. If technological tools possess learning functions, they will help make the classroom an environment where students actively construct knowledge. D. E. Edelson points out that there are three reasons for using technology in schools: First, it is suitable for scientific practice; second, it can prompt information through past interactions; third, it provides opportunities for transforming classrooms from an indoctrination model. Students can use the Internet to collect data, create and analyze charts, build models, discover information, share information, or produce multimedia works. Thus, learning technology can expand the classroom world, becoming a powerful cognitive tool that promotes students’ inquiry. Research in learning science shows that students achieve better learning outcomes by externalizing constructed knowledge through their works. First, by creating works, students’ understanding is constructed and reconstructed. When students reflect on their works, they can actively apply scientific concepts. Second, learning is not linear or fragmented units, and evaluation should not be based on fragmented information. The creation of works must undergo a process from posing questions, designing investigations, to collecting and interpreting data, and making scientific explanations. Therefore, students can develop their understanding through various project-based learning, and teachers can evaluate higher-order cognitive outcomes based on the works. Third, when students publish or showcase their creative products, they can gain motivation for creation from the understanding provided by others. Works are concrete and explicit; students can receive sharing and evaluation from teachers, classmates, parents, and community members. In particular, critical evaluations provide students with feedback on strengths and weaknesses, prompting them to reflect and revise their works, thereby developing their understanding.
(2) Practicing Unit Design
The prerequisite for deep learning is to transform “unit design,” which is the lever to pivot “classroom transformation.” For many years, frontline teachers in China have mostly been satisfied with “lesson hourism” and are not familiar with “unit design.” In fact, unit design plays a crucial role in curriculum development and teaching practice: without unit design, curriculum development is merely creating a pile of garbage; without unit design, lesson plans remain at the level of fragmented knowledge and skills training. So, what is a “unit”? A “unit” is merely a unit of “design, implementation, and evaluation” in teaching. In the history of curriculum development worldwide, various unit designs exist, but fundamentally it oscillates between “disciplinary units” (knowledge units) and “experiential units” (life units). For instance, Japan criticized “knowledge units” in the 1920s, advocating for the design of “life units” aimed at cultivating “self-disciplined learners,” encouraging “integrated science learning”—implementing “large integrated subjects” in lower grades, “medium integrated subjects” in middle grades, and “small integrated subjects” in higher grades. Traditional school education views classroom teaching as a form of information transmission, requiring children to consolidate the transmitted knowledge through rote memorization. This equates “teaching” with “learning,” that is, “teaching is learning.” However, today’s teaching cannot be reduced to teachers merely transmitting information; it requires teachers to provide a “learning space” where every child can actively participate and where learning interest and experience can be stimulated. For children, teachers assume the role of “facilitators.” Therefore, when thinking about the design of teaching units, the key question is not the difference between “large units” and “small units” but rather to transcend the binary opposition between “discipline” (knowledge) and “experience” (life), seeking the evolution of unit organization. So, what differences exist between the unit design anticipated by the new curriculum reform and the old unit design?
According to Sato Gaku, the old unit design was for teachers’ “teaching,” designed according to a “goal-achievement-evaluation” “stair-step” model; while the new unit design is for students’ “learning,” designed according to a “theme-exploration-expression” “mountain-climbing” model. This unit design focuses on organizing activities of “exploration,” “expression,” and “exchange,” which is referred to as “3E activity composition.” In other words, “lesson plan design” is the teaching plan developed by teachers based on unit goals, material views, and evaluation standards, reflecting the goals of that lesson; while “learning plan design” signifies the design of “children’s learning activities.” According to constructivist design principles, “learners do not simply memorize the information provided by teachers; learning is a meaning construction process in both personal and social senses. The role of teachers is not to help students fill knowledge tanks but to ignite the flame of wisdom.” Thus, this design of learning activities emphasizes not the content of “lectures” set by teachers but rather the planning of “learning.” It must meet six conditions: “context, collaboration, scaffolding, tasks, presentation (externalization), reflection,” which help promote learners to achieve genuine learning better.［21］ In summary, this is a design of “learning (activity) units” that focuses on children’s learning activities, distinct from the old “knowledge units.” Learning essentially is a form of “cultural participation.” What “participatory learning theory” emphasizes is not merely the transmission of scientific concepts of specific subjects but the cultivation of practical abilities in real-life situations, challenging authentic issues in the real world. This not only expands the space for children’s inquiry but also, the closer it approaches the essence of the topic, the more it requires learners to engage in a higher-order cognitive process of integration. R. J. Marzano’s focus on higher-order thinking abilities in unit design is a prime example. As early as the 1980s, the United States listed the cultivation of children’s higher-order thinking abilities as an important educational topic and initiated active research on “thinking teaching.” In 1988, Marzano published the results of his research on thinking teaching—“dimensions of thinking.” In 1992, he further validated the practical framework of thinking dimensions as a reconstruction of unit design, revealing the importance of knowledge and context in thinking teaching. He emphasized that frontline teachers must consider the following three questions when cultivating students’ higher-order thinking abilities: how should the ideal model of knowledge teaching be thought about? How should knowledge teaching be integrated with general thinking skills teaching? How should the dependency of learning contexts be addressed?［22］ This means that “deep learning” should emphasize both the acquisition of subject content (knowledge dimension) and the training of “thinking abilities” (cognitive process dimension). In 1997, Marzano further introduced a unit design model based on learning dimensions aimed at promoting the teaching and evaluation of higher-order thinking abilities in school classrooms. The learning dimensions, serving as metaphors guiding unit design, include five levels: dimension one, learning attitudes and perceptions; dimension two, acquisition and synthesis of knowledge; dimension three, expansion and refinement of knowledge; dimension four, meaningful application of knowledge; dimension five, mental habits.［23］ This framework is distilled from teaching practice, clearly delineating several categories or levels of learning, indicating that learning must unfold based on some form of knowledge and is continuously constructed, thus aiding the cultivation and evaluation of higher-order thinking abilities in school settings. In fact, Marzano implemented three models expressing this unit design process: 1. Knowledge-focused model; 2. Argument-focused model; 3. Student inquiry-focused model.［24］ These three models are primarily distinguished by dimension differences, with no one being inherently ideal or unfolding in a specific order. Their commonality is that the shift from emphasizing knowledge to emphasizing thinking ability does not position the two in binary opposition but rather clarifies the inseparable relationship between knowledge acquisition and thinking ability cultivation, seeking diverse educational practices that cultivate thinking ability.
(3) Transcending Individual Abilityism
The greatest obstacle to implementing deep learning lies in individual abilityism. Traditionally, in the school education scene, the emphasis has been placed on individual abilities, such as academic performance and social skills, viewed as issues of personal knowledge and ability mastery, while traditional psychology often focuses on the individual’s internal aspects. Clearly, the individual is the unit of analysis in psychology. L. B. Resnick pointed out that school learning differs from learning in daily life: first, in external learning, the emphasis is on sharing cognition among collaborators, while in schools, the focus is on individual cognition issues; second, external learning emphasizes utilizing tools as much as possible during collaborative tasks, while in schools, it emphasizes pure mental tasks without using tools; third, external learning emphasizes making inferences adapted to various contexts, while in schools, it emphasizes symbolic operations; fourth, external learning emphasizes unique abilities, while in schools, it emphasizes generalized abilities.［25］ In summary, school education aims at individuals engaging in non-tool-assisted activities, learning generalized knowledge through pure symbolic operations in their minds, that is, school education is not dependent on situational resources and socially historical artifacts. In a sense, this is a view that elevates “bare personal ability.” The fallacy of individual abilityism lies in its characteristics of “non-mediated,” “super-contextual,” and “non-interactive.” It can be said that this is a view of activity, knowledge, and meaning that considers human activity as not requiring any mediation, believing knowledge is trans-contextual, and its meaning and value are invariant.［26］
First, the mediating nature of activity. J. S. Bruner clearly analyzes the mediating nature of ability and its importance. He believes that human abilities are amplified through various tools. For example, a car enhances mobility, eyes enhance sensory ability, language and theory enhance reasoning ability, etc. However, “amplification” does not imply an increase in personal ability. Humans do not face the world as individuals in a closed system but rather as users of tools in a system that includes both people and tools. Human activity should not be analyzed with the individual as the unit. Vygotsky (L. Vygotsky) advocated that activities mediated by tools should be viewed as an analytical unit. He distinguished between two types of tools: one is technical tools that form objects, structures, and systems; the other is psychological tools such as symbols and language.［27］ The importance of tool-mediated activities lies in the fact that, when individuals combine with the objective world, they also form relationships with others. For instance, even when a worker uses a saw to cut wood, the saw is a tool made by someone, thus forming an indirect communicative relationship between the worker and the creator and improver of the saw. When one thinks, they inevitably use social language as mediation, thus forming a dialogical relationship. The domain of “I” is not merely the body bounded by skin; for example, in the case of using crutches, the body of “I” combines with the crutch to form a system, thereby moving the boundary of “I” further into the world. In fact, it is unimaginable to have activities without tools; non-mediated activities are merely a fantasy. This means that the educational view in schools—that tools are external things, and the task of school education is merely to cultivate the internal mental activities of the body without any mediation—is a view that must be transformed. Symbols are important for thinking activities, but this is not because they are tools for operational representation, but precisely because they are tools for social communication. Therefore, tool-mediated activities in school education are natural, and organizing children to actively utilize diverse resources in activities is undoubtedly necessary.
Second, the contextual nature of knowledge. The so-called “super-contextuality” is a viewpoint that views individual ability as an entity, cutting it off from the context that develops this ability. Tests such as intelligence tests and personality tests seek measurement orientations that do not relate to the context in which certain knowledge and the application of knowledge are involved, which is a practice aimed at seeking knowledge that is super-contextual. The so-called “super-contextualization” refers to the act of severing the specific behaviors and intelligences generated in a certain context from their field, treating them as general knowledge skills and intelligence. In fact, this differs from the super-contextualized linguistic markers; it does not mean that specific behaviors and intelligences lose their contextual meanings, but rather that the contexts from which specific behaviors arise are deprived and assigned a different context. In this sense, the so-called “super-contextualization” is a change of context—some context that was “privileged” before this change. For instance, intelligence tests measure knowledge that is super-contextualized, but they place the achievements of people from various social cultures under the privileged context of Western school culture, often viewing cultural differences as ability differences, leading to statements like “people from a certain cultural circle lack certain abilities” or “people from a certain cultural circle excel in certain abilities compared to other cultural circles.” Individuals exercise intelligence in their respective contexts, possessing intelligences that depend on contexts. Therefore, the relationship between each context and the intelligences achieved through activities in their respective contexts should be discussed. Discussing intelligences that are not context-dependent can only be a fantasy. Thus, the cultivation of such intelligences naturally should become a primary goal of school education.
Third, the communicative nature of meaning. Everyone’s mind contains “meaning,” which refers to the communicative view carried and exchanged by language symbols. Thoughts first form in an individual’s mind, which are then translated into thinking patterns through socially circulated language. In this view, “development” refers to the process of internalizing various “meanings” existing in the pre-existing world through social language. Just as thinking about relatively stable things involves writing meanings into dictionaries, it is treated as a process. However, the entries in dictionaries merely represent a “case” that has been assumed, not an absolutely immutable “meaning” that can be utilized at any time. J. S. Brown pointed out, “Knowledge is akin to language.” This language does not attach fixed meanings but marks the products of activities in certain contexts of the existing world. Just as the term “carp” in a seafood market price list differs from the phrase “carp jumping over the dragon gate” in terms of meaning in different contexts, meaning is not immutable but can be generated and communicated at any time through activities confronting the world. The “meaning” that is realized is merely a certain state in this process. Therefore, school education should not focus on the transmission of “meaning” but should aim at generating meanings that respond to contexts, actively engaging every child in communication. This means overcoming the view that “knowledge is recorded in textbooks, existing in teachers’ minds” and confronting the real world.
Emphasizing the mediating nature of activities, the contextual nature of knowledge, and the communicative nature of meaning is collectively referred to as “situational learning theory.” This theory encompasses various studies such as Vygotsky’s “zone of proximal development,” “social distributed cognition,” and “situational learning.” A common point of these studies is that the analysis unit is not the individual but the activity system containing multiple others and artifacts in specific contexts. Situational learning research does not aim to dissolve individuals into contexts, nor does it ignore individuals, but rather captures individuals in relation to contexts. Ecological psychology originates from Darwin’s research on human ecology, emphasizing the influence of ecological environments on individuals. Therefore, R. H. Moos states, “Individuals’ behaviors are not determined by inherent characteristics of each person, such as personality and attitudes, but are strongly influenced by environmental factors.” A. W. Wicker also points out that the core concept of this theory is “behavior setting,” through which the interaction between organisms and environments is extracted as the analysis unit.［28］ The so-called “behavior setting” does not refer to a simple physical space; it is a bounded, self-regulating hierarchical system composed of a series of behaviors aimed at implementing a sequence of actions called “behavior setting programs,” potentially replacing interactions among people and components outside of people. The “situational learning theory” rejects the old binary opposition model between individuals and environments, emphasizing understanding both as a whole.
Deep learning is a marker of classroom transformation. “Teaching is for not teaching,” just as the “learning responsibility transfer model” advocates, “teachers must shift from fully assuming teaching responsibilities to a stage where both teachers and students share responsibilities, gradually moving towards the stage of children’s autonomous learning and application.”［29］ Deep learning signifies pointing towards higher-order academic abilities, meaning it cannot be satisfied with low-order academic goals—mastery of knowledge and understanding and skills; it must elevate from these low-order academic goals to high-order academic goals—cultivating traits such as effective communication and collaboration, critical thinking, and “learning disposition.” At the same time, deep learning also signifies pointing towards teacher learning. If one avoids the perspective of learning science and the practice of classroom transformation while merely singing praises of deep learning, it is no more than a modern version of “the emperor’s new clothes.” In exam-oriented educational classrooms, teachers are the owners of knowledge, while children are viewed as ignorant beings, and the task of classroom teaching is merely to allow children to swallow the standard answers transmitted by teachers. In deep learning classrooms, children participate as “subjects of learning” in collaborative inquiry activities designed by teachers based on children’s knowledge differences, nurturing each learner’s core competencies through dialogue and reflection, knowledge construction, and emotional cultivation. The famous French novelist Marcel Proust once said a thought-provoking passage—
The only real travel, the only way to rejuvenate, is not to appreciate new landscapes but to gaze at this universe with different eyes, with the eyes of another, with the eyes of countless others, to look at the myriad worlds they each gaze upon, and to look at their own myriad worlds.［30］
Proust said that people travel the world seeking new things, entering unfamiliar lands, encountering strange landscapes, thereby gaining new knowledge and expanding their horizons. However, regardless of how novel the scenery one encounters, if they appreciate it with the same eyes, then this world remains flat and small. The gaze with which one appreciates the landscape needs to change; only then can it be called true travel. If we replace this “travel” with “learning,” then “appreciating new landscapes” corresponds to “gaining new knowledge.” Knowledge may increase significantly, but if the perspective for processing that knowledge does not change—if that is the case, then even if one increases their knowledge, it is in vain; they have not grown. This is the same principle as “the quality of the mind cannot be determined merely by memory ability.” Only by appreciating the world with a completely different gaze, this “qualitative change” is “the only true travel,” and it is “true learning.” This type of learning is entirely different from the knowledge indoctrination, rote memorization, and test memory of exam-oriented education. The emphasis here is on “using discerning eyes,” that is, engaging in “true travel” using “different eyes” to bring about a “qualitative change.” Deep learning is such a learning process that unfolds “true travel” with “different eyes” to produce “qualitative changes.”
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