Schema Theory

Introduction to The Learning Theory

A schema is a mental construct comprising a cluster or collection of related concepts (Bartlett,1932). Schema Theory describes how the mind organizes, stores, and interprets information based on preexisting mental frameworks called schemas. It posits that our minds store information in organized patterns, influencing how we perceive and interpret new experiences. It does this by forming and using categories. Inferences about the properties of new category instances based on knowledge about the category are an essential component of intelligence and thinking. These concepts can range from classifications based on simple concrete properties such as shape and color to high-order abstract properties leading to concepts such as mammal, illegal, commerce, electrostatic force, or beauty.

Schema theory has evolved through the contributions of several influential scholars, each expanding its application across different fields of study. In 1932, Fredric Bartlett introduced the concept of schemas through his research on memory, demonstrating that people construct memories based on preexisting knowledge structures. Jean Piaget applied his work to cognitive development, explaining how children modify schemas through assimilation and accommodation. Richard Anderson brought schema into education, emphasizing its role in reading comprehension and learning.

Schema theory plays a significant role in both education and psychology. In education, schema guides designers in teaching strategies such as scaffolding and activating prior knowledge to improve students’ engagement and transfer knowledge. It also gives students ways to solve problems and make decisions, helping them to interpret complex information quickly. In psychology, schema provides an explanation of how memory works, including why people remember information in structured ways and sometimes distort memories to fit existing schemas.

This visual representation illustrates schema theory by depicting a car at the center, surrounded by interconnected concepts such as maintenance, ownership, fuel, passengers, technology, and infrastructure. By relying solely on images, the diagram encourages viewers to activate their existing knowledge and explore how different aspects of a car are mentally organized and understood. [Image Description]

Origins of the Learning Theory

Frederic Bartlett proposed that prior knowledge plays a crucial role in cognitive processing. His work emphasized how existing schemas influence perception, memory, and thought. In his book Thinking: An Experimental and Social Study (1958), Bartlett explored how cognition is shaped by experience, highlighting the active role of memory in reconstructing knowledge. Understanding cognition and brain function has evolved throughout history, with early foundations dating back to Plato. In Theaetetus, Plato introduced the idea of the mind as a wax tablet, where new experiences imprint memories—an early analogy for how information is stored and processed. When we remember an experience, we read off what was impressed on the wax (Wagoner, 2013, p.1).

Frederic Bartlett was a Cambridge psychologist credited with serial reproduction, later known as schema theory. Bartlett claimed that he adopted this method at the suggestion of his friend and colleague, Norbert Wiener. In 1920, Bartlett conducted a famous experiment in The War of the Ghosts, featured in “Remembering: A Study in Experimental and Social Psychology” (1932), a groundbreaking book exploring how memory is an active and reconstructive process rather than a passive information recording. It challenged previous views of memory as a simple retrieval system. It introduced the idea that people reconstruct memories based on their existing knowledge, experiences, and cultural background—a concept foundational to schema theory. For Bartlett, “schema was to provide the basis for a theory of remembering that was embodied, dynamic, temporal, and social. This did not seem to be the case as he only “provided a hesitant and sketchy account of schema theory (Wagoner, 2013, p.1).”

Jean Piaget was a Swiss psychologist who contributed to developmental psychology. Dissatisfied with the prevailing understanding of childhood learning, Piaget transformed educational theories with his stages of cognitive development. He explored how the brain processes new information from infancy through adulthood, categorizing it into four stages: sensorimotor, preoperational, concrete operational, and formal operational. The schema was a core concept of Piaget’s genetic epistemology, referring to how the world is perceived, interpreted, and reflected upon. Through this, he developed three significant concepts of schema theory assimilation, accommodation, and equilibrium.

Marvin Minsky, a computer scientist, reintroduced schema theory within the artificial intelligence (AI) field. Minsky aimed to create machines with human-like cognitive abilities, such as perception and understanding of the world. While addressing these complex challenges, he encountered Bartlett’s work and recognized that humans rely on stored knowledge to carry out many cognitive processes—processes he sought to replicate in AI. To achieve this, he realized that machines needed structured knowledge similar to humans’ schemas to process information.

As a solution, Minsky introduced the concept of frames to represent knowledge in artificial intelligence. His idea expanded upon schema theory by offering a more structured and detailed approach to knowledge representation. He envisioned frames as dynamic structures that continuously interact with new information from the environment. Each frame contained slots designed to hold specific values, with default values filling in when real-world data was incomplete or missing. This innovation laid the groundwork for knowledge representation in AI, allowing machines to interpret and respond to ambiguous or incomplete information more effectively.

An educational psychologist, Richard Anderson, introduced the educational community to schema theory in 1977.  Anderson was interested in how students process information, particularly in reading and language development. He sought to explain why some students struggled with reading comprehension and how teachers could support them by leveraging their existing knowledge structures. Schemata provides a form of representation for complex knowledge that, for the first time, provides a principal account of how old knowledge might influence the acquisition of new knowledge.

Brief Introduction to Schema Theory

This video further illustrates the principles of schema theory, demonstrating how mental frameworks influence learning, memory, and the interpretation of new memory.

Historical Context

Broader historical and intellectual movements profoundly influenced the development of schema theory in psychology and cognitive science. From the dominance of behaviorism in the early 20th century to the cognitive revolution and its modern applications, schema theory evolved in response to changing views on how humans process and organize knowledge.

• Behaviorist approaches, such as classical conditioning (Pavlov) and operant conditioning (Skinner), explained learning as a stimulus-response relationship, mainly ignoring how the mind organizes and interprets information.
• However, in Europe, Frederic Bartlett (1932) challenged behaviorism with his research on memory reconstruction, laying the groundwork for schema theory. His “War of the Ghosts” experiment demonstrated that memory is not a passive recording but an active process influenced by prior knowledge (schemas).

By the mid-20th century, psychologists began recognizing behaviorism’s limitations, particularly its failure to explain complex cognitive abilities like language, problem-solving, and memory. This led to the cognitive revolution, a shift in focus toward internal mental structures and processes. The emergence of neuroscience and computer science aided this transition. Ultimately, the cognitive revolution took hold, and people realized that cognition was crucial to genuinely appreciating and understanding behavior. The evolution of schema theory reflects a broader historical shift in psychology—from behaviorism’s rigid, external focus to a dynamic, cognitive approach that values internal mental structures. Influenced by AI, cognitive science, and neuroscience, schema theory remains a cornerstone in understanding how humans learn, remember, and interpret the world.

Various schools of thought have emerged in psychology, each bringing unique perspectives and insights into understanding human behavior and mental processes. The table below provides a brief overview of some of the major schools of psychology, their primary focus, historical period, and notable figures:

1. Although Piaget’s research on cognition began in the 1920s, cognitive psychology did not become mainstream until 1960

Comparison with Other Theories

Schema theory is a cognitive-based theory of learning that explains how knowledge is structured, stored, and used. Unlike other learning theories, such as behaviorism, constructivism, and social learning theory, it offers a unique perspective on how prior knowledge influences learning and memory.

• Schema theory rejects behaviorism’s focus on external reinforcement, emphasizing internal mental structures.
• Schema theory aligns with constructivism, recognizing that learning is an active and knowledge-based process.
• Both theories recognize that previous experiences shape learning, though social learning theory places greater emphasis on external influences (e.g., role models, media, social context).

While schema theory differs from other learning theories, it does not necessarily contradict them. Instead, it offers a complementary framework, explaining how knowledge is organized and used in learning. By integrating elements from constructivism, cognitive science, and AI, schema theory remains one of psychology and education’s most versatile and widely applied theories.

Fundamental Tenets of the Theory

Key Concepts

Schema theory is based on several core tenets that describe how knowledge is structured, stored, and used in the human mind. These tenets explain the processes of learning and cognitive development by emphasizing the role of prior knowledge, mental frameworks, and the continuous adaptation of schemas. As individuals encounter new information, they either assimilate it into existing schemas or modify it through accommodation to integrate new concepts. This dynamic process enables more efficient problem-solving, comprehension, and decision-making. Additionally, schema theory highlights the importance of contextual learning, where new knowledge is more effectively retained when connected to familiar experiences and concepts. The following is a list of key concepts in schema theory:

• Schemas as Knowledge Structures: Schemas (or schemata) are mental structures that represent knowledge about a concept or type of stimulus, including its attributes and the relationships among those attributes. (Alba & Hasher, 1983).
• Assimilation, Accommodation, and Equilibrium: These are the processes by which schemas are modified and balanced.
• Role in Memory and Learning: Schemas are essential for encoding, storing, and retrieving information. They guide our attention and influence how we interpret new information, making learning more efficient by allowing us to categorize and recall knowledge quickly (Karunarathna et al., n.d.).
• Influence on Perception and Behavior: Schemas shape our perception of the world and behavior. For instance, social schemas guide our expectations and interactions with others, while self-schemas influence how we see ourselves and act accordingly (Hegde, 2022).
• Dynamic Nature: Schemas are not static; they can change over time as we acquire new experiences and information. This dynamic nature allows for cognitive flexibility and adaptation to new situations.

Schema theory provides a robust framework for understanding learning and development. It posits that we do not learn isolated facts but rather organize knowledge into interconnected mental frameworks called schemas. These schemas, acting as mental blueprints, provide a structure for interpreting new information. As we grow and experience the world, our schemas evolve, becoming more complex and sophisticated, progressing from simple concepts in childhood to more abstract understandings later in life. According to schema theory, learning is an active process of constructing and refining these mental models through assimilation (fitting new information into existing schemas) and accommodation (modifying or creating new ones when necessary). This continuous adaptation of schemas drives cognitive growth and development. Crucially, schema theory highlights the role of prior knowledge; activating relevant schemas allows learners to connect new information to what they already know, making it more meaningful and memorable. Schemas also influence comprehension and memory by guiding attention, facilitating inferences, and providing a framework for organizing information. For educators, schema theory emphasizes the importance of activating prior knowledge, helping students make connections between concepts, and providing scaffolding to support schema development, ultimately leading to deeper understanding and cognitive growth.

Supporting Mechanisms

Schema theory is grounded in cognitive mechanisms and processes that explain how schemas are formed, modified, stored, and utilized. These mechanisms are crucial in ensuring that learning is efficient, structured, and adaptive, enabling individuals to interpret new experiences based on prior knowledge. Here is a structured breakdown with real-world examples of how each affects the cognitive processes and mechanisms that make schema theory:

Processes

• Schema Activation: The process of retrieving relevant schemas from memory when encountering new information.

• • Example: When reading a story about a beach vacation, your mind might activate schemas related to sand, ocean, and sunbathing to help you understand and visualize the narrative.

• Schema Refinement: The process of updating and refining schemas based on new experiences and information.

• • Example: If you have only seen small dogs and then encountered a large breed like a Great Dane, your dog schema might be refined to include a broader range of sizes.

• Schema Integration: The process of combining multiple schemas to create a more comprehensive understanding.

• • Example: When learning about different types of mammals, you might integrate your schemas for dogs, cats, and horses into a more general schema for mammals, incorporating common traits like being warm-blooded and having fur.

Mechanisms

• Assimilation: Incorporating new information into an existing schema without changing the schema.

• • Example: A college student initially struggles to remember all the different facets of schema theory, but schemas help group concepts into meaningful categories over time.

• Accommodation: Modifying an existing schema or creating a new one when new information does not fit.

• • Example: When someone remembers a phone number, they do not remember the list of digits. They remember them as a set of numbers, area codes, three digits, and four digits.

• Equilibrium: The process of balancing assimilation and accommodation, creating a stable understanding.

• • Example: A child from a collectivist culture may develop a schema emphasizing teamwork, while a child from an individualist culture may develop schemas prioritizing personal success.

Strengths and Limitations of the Theory

Strengths

Applying schema theory offers a deeper and more comprehensive understanding of how humans learn and develop. This perspective allows educators, psychologists, and others interested in cognitive processes to approach learning and development with greater insight and effectiveness. By recognizing the role of schemas in shaping thought and behavior, we can refine instructional methods, therapeutic approaches, and artificial intelligence models. Ultimately, schema theory enhances our understanding of knowledge acquisition, cognitive growth, and how individuals interact with the world around them.

Schema theory provides a strong framework for understanding comprehension and interpretation, making it one of its greatest strengths. It effectively explains how individuals construct meaning by utilizing pre-existing mental structures. One key advantage is its ability to account for the efficiency of information processing, as schemas reduce the need to analyze every new piece of data from scratch. Instead, they enable quicker and more automatic interpretation of information. Additionally, schema theory facilitates inference and gap-filling, allowing individuals to generate meaning even when faced with incomplete information. For example, when reading a story or witnessing an event, schemas help fill in missing details and enable logical inferences. Furthermore, schemas play a vital role in pattern recognition and meaning-making, assisting individuals in identifying recurring structures in language, social interactions, and visual stimuli. This highlights the importance of top-down processing, where prior knowledge and expectations actively shape how new information is perceived and understood.

Beyond comprehension, schema theory provides insight into how memory is structured and retrieved. Schemas are organized frameworks that help categorize and store information efficiently, making recall more accessible. Related knowledge is also triggered when a schema is activated, facilitating retrieval and reinforcing learning. Additionally, schema theory explains memory reconstruction and distortion, as individuals often rely on existing schemas to interpret past experiences. This can sometimes lead to inaccuracies, as memories are not always precise replications but reconstructions influenced by prior knowledge. Understanding these cognitive processes helps explain why individuals may misremember events or recall information in a way that aligns with their existing schemas.

The practical applications of schema theory span multiple disciplines, highlighting its versatility and relevance. In education, schema theory informs instructional design by emphasizing the activation of prior knowledge, forming meaningful connections, and the structured organization of new information to enhance learning. In artificial intelligence, schema-like structures contribute to developing intelligent systems capable of understanding language, reasoning, and human-like interaction. AI knowledge representation often incorporates schema-based principles to improve machine learning and decision-making processes. In clinical psychology, schema theory plays a crucial role in understanding maladaptive schemas, such as negative self-perceptions or dysfunctional relationship patterns. Therapists use this knowledge to help individuals restructure harmful cognitive patterns, leading to more effective psychological interventions and behavioral changes.

Schema theory provides a comprehensive and valuable framework for understanding cognition, memory, and learning. Its ability to explain various cognitive processes, from perception to information retrieval, makes it a fundamental concept across numerous fields. By applying schema theory, researchers and practitioners can develop more effective strategies for education, artificial intelligence, and psychological treatment. Recognizing the impact of schemas on human thought and behavior allows for better instructional practices, improved mental health interventions, and more advanced AI systems, ultimately enhancing how individuals acquire, process, and utilize knowledge.

Limitations

Schema theory has faced criticism for several limitations. One major concern is its vagueness and lack of specificity, as the concept of a “schema” is often broadly defined as a “mental framework,” “organized knowledge structure,” or “pattern of thought” without a clear, universally accepted definition (Krasny et al., 2007). Additionally, the lack of predictability in schema theory makes it difficult to test rigorously as a scientific theory, since its adaptability reduces its ability to generate concrete predictions (Thorndyke & Yekovich, 1979).

Another limitation is the oversimplification of cognitive processes, as schema theory does not fully explain how new information is integrated in complex and novel ways beyond simple assimilation or accommodation (Rumelhart & Norman, 1980). It also neglects lower-level processing, as it emphasizes top-down, knowledge-driven cognition while often overlooking more fundamental, bottom-up processes (Havé et al., 2021). Finally, measuring schemas presents a challenge, as they exist as internal mental representations rather than observable behaviors, making direct assessment difficult (Bartlett, 1932). Despite these limitations, schema theory remains influential in understanding cognition, learning, and memory.

Instructional Design Implications

Practical Applications

Schema theory encourages instructors to recognize that learners do not come as empty vessels but bring knowledge and schemas to the learning process. It allows instructors to facilitate active learning by encouraging students to construct their knowledge while modifying existing schemas. Effective instructional design for complex learning should focus on two key goals: first, fostering the development of problem-solving and reasoning skills, and second, promoting the automation of schemas for the consistent elements of a task or problem (Mayer & Fiorella, 2021). In other words, students must learn to think and solve problems. However, they also need to develop self-efficacy for the routine aspects of those problems to focus their cognitive resources on the novel challenges.

Schema theory plays a crucial role in instructional design by emphasizing the importance of connecting new information to students’ existing knowledge structures or schemas. Here are some ways it informs lesson design, activities, and assessments:

Lesson Design

1. Activating Prior Knowledge: Instructors often begin lessons by activating students’ prior knowledge, helping them connect new information to what they already know. This can be done through discussions, brainstorming sessions, or quick reviews.
2. Organizing Content: Lessons are structured to build on existing schemas, gradually introducing more complex concepts. This scaffolding approach ensures that students can integrate new information more effectively.

Assessments

1. Formative Assessments: Frequent, low-stakes assessments like quizzes, reflections, and peer reviews help instructors gauge students’ understanding and adjust instruction accordingly. These assessments check how well students integrate new information into their schemas.
2. Application-Based Assessments: Assessments that require students to apply their knowledge in new contexts (e.g., problem-solving tasks, projects) help instructors evaluate the depth and flexibility of students’ schemas.

The following example illustrates how schema theory can be applied to develop a cybersecurity training module that ensures employees at varying skill levels acquire, retain, and use essential security practices in real-world situations.

Activities

1. Use of Analogies and Examples: Activities often include analogies and examples that relate new concepts to familiar ones, making it easier for students to understand and remember new information (Neumann & Kopcha, 2018)
2. Interactive Learning: Hands-on activities, group work, and discussions are designed to help students actively engage with the material, facilitating the construction and reinforcement of schemas.
3. Pre-reading activities: Enable learners to establish top-down semantic and structural frameworks before engaging with a text. To effectively implement pre-reading strategies, remember the four Ps: Preview, Predict, Prior Knowledge, and Purpose.
4. In-depth discussion: Following an instructor-led lecture can help learners expand and deepen their understanding of a specific course topic.

Strategies

Schema Theory highlights the significance of prior knowledge in shaping new learning experiences, proposing that individuals interpret new information through the lens of their existing mental frameworks or schemata. Since comprehension and meaning-making are deeply rooted in these cognitive structures, effective instructional strategies should prioritize activating, expanding, and refining learners’ prior knowledge. Learners can revisit previously acquired concepts and skills by identifying similarities and differences in a given case or problem, reinforcing their understanding.

Effective instructional design based on schema theory ensures learners establish meaningful connections between new information and existing knowledge. One way to achieve this is by integrating real-world scenarios, helping learners recognize the practical relevance of their studies. Case studies and problem-solving activities encourage active engagement with cognitive structures, making learning more dynamic and applicable to real-life situations. By actively applying knowledge, learners can strengthen their schemas and improve their ability to transfer learning to new contexts.

Complex concepts should be broken down into smaller, manageable units to enhance comprehension (chunking). This approach prevents cognitive overload and allows learners to integrate new knowledge without gradually becoming overwhelmed. Analogies can also be powerful tools to connect familiar and unfamiliar concepts, making abstract ideas more accessible. For example, likening the structure of an atom to a solar system helps reinforce understanding by drawing parallels to a well-known concept.

Scaffolded learning experiences should progressively increase in complexity, aligning with cognitive development. By structuring instruction to build on prior knowledge, learners can develop a more sophisticated understanding over time. Providing feedback and opportunities for revision enables learners to refine and expand their schemas as they progress. Encouraging metacognitive strategies fosters self-monitoring and evaluation, leading to deeper engagement with the material and improved long-term retention.

Schema-based instruction enhances learning outcomes and supports the development of essential self-learning skills. These skills include metacognition, which helps learners reflect on their thought processes, and problem-solving, which enables them to apply knowledge flexibly. Once schemas are constructed and automated, instructional strategies should shift toward schema elaboration to ensure continued cognitive growth. This process enhances schema resolution by refining, expanding, and sophisticating acquired knowledge through instruction in related domains.

Schema elaboration is further facilitated by inter-schema or parallel-schema interactions, which involve comparing and contrasting an acquired schema with a less-developed one. This interaction strengthens schema refinement by encouraging learners to evaluate their existing knowledge critically. Through this process, learners can identify gaps in their understanding and make meaningful adjustments to their cognitive frameworks. As a result, schema elaboration supports more advanced thinking and deeper knowledge integration across related concepts.

Worked examples are another effective method for promoting schema construction and automation. These examples provide learners with step-by-step guidance, allowing them to observe correct problem-solving approaches before attempting similar tasks independently. However, because worked examples may not always encourage deep cognitive engagement, they should be supplemented with conventional problem-solving exercises. The completion-problem effect addresses this gap by combining worked examples with partially completed problems, requiring learners to actively participate in learning and reinforce their schema development.

Contexts

Schema theory provides a scientific foundation for designing structured, adaptive, and practical learning experiences across various educational settings, including classrooms, online education, and workplace training. By leveraging schema theory, educators and trainers can enhance learning outcomes by activating prior knowledge, structuring content logically, minimizing cognitive overload, and incorporating real-world applications. These approaches ensure that learners acquire new knowledge and apply and retain it more effectively, ultimately making educational and training programs more impactful and engaging. Furthermore, understanding the similarities and differences in how learners process information allows instructional designers and educators to foster deep comprehension, facilitating knowledge transfer and enhancing learner self-efficacy.

A practical application of schema theory can be observed in corporate onboarding programs, where structured learning experiences help new employees adapt quickly and efficiently to their roles. For example, a company training program may begin by activating prior knowledge through an interactive orientation session connecting new information to employees’ experiences. The training content is then organized systematically, starting with foundational company policies before progressing to role-specific responsibilities. Complex procedures are broken down into smaller, manageable steps to prevent cognitive overload, reinforced through hands-on simulations or real-world applications. Employees confidently develop transferable knowledge to navigate various workplace situations by engaging in scenario-based training. This structured approach enhances knowledge retention and fosters self-efficacy, ensuring that employees feel prepared to handle real-world challenges effectively.

Schema theory informs instructional design across classroom instruction, online learning, and corporate training by emphasizing prior knowledge activation, scaffolding, and meaningful context. In all three settings, instruction is most effective when it connects new information to learners’ existing schemas, allowing for assimilation and accommodation. Strategies such as pre-training, guided inquiry, and real-world examples help learners integrate new knowledge efficiently (Mayer & Fiorella, 2021). Additionally, chunking information to reduce cognitive overload is a universal strategy that aligns with Cognitive Load Theory. Whether through structured classroom discussions, multimedia-enhanced online courses, or workplace simulations, instructional designers ensure that learning materials are organized, relevant, and progressively built upon existing schemas.

Despite these similarities, the implementation of schema-based instruction varies across settings due to differences in learner demographics, engagement methods, and learning goals. In classrooms, schema theory is often applied through direct instruction, discussions, and hands-on activities, allowing for social interaction and immediate feedback. In contrast, online learning relies more on multimedia presentations, adaptive learning technologies, and self-paced modules to facilitate schema construction individually (Mayer & Fiorella, 2021).  Conversely, corporate training focuses heavily on experiential learning, application, and real-world problem-solving to reinforce professional schemas and accelerate expertise development (Salas et al., 2012). Additionally, corporate settings prioritize efficiency and applicability, while classroom and online education emphasize foundational learning and conceptual understanding.

Conclusion

In summary, schema theory provides profound insight into how individuals organize, interpret, and retrieve information, highlighting its pivotal role in shaping cognition and behavior. By examining the dynamic interplay between existing mental frameworks and new experiences, schema theory underscores the adaptive nature of human understanding. Its applications span diverse fields, from education and artificial intelligence to psychology and communication, offering invaluable tools for fostering learning and improving interactions. As research on schema continues to evolve, the theory holds promise for addressing complex cognitive processes and advancing our comprehension of how knowledge is structured and utilized in an ever-changing world.

Review what you have learned using the H5P interactions below:

Image Descriptions

The image depicts a top-down view of a silver car positioned centrally on a white background. Surrounding the car in a circular pattern are 18 individual icons, each contained within a circular frame. Each icon displays a silhouette or object related to car maintenance and operations. Lines connect each icon to the car, indicating different aspects of automotive care or functionality. The icons are equally spaced, forming a clock-like arrangement. Details of individual icons, such as silhouettes of people interacting with the car in various ways (e.g., refueling, cleaning), as well as objects like tires and a fuel pump, are visible. [Return to Car]

References:

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Bartlett, F. C. (1932). Remembering: A study in experimental and social psychology. Cambridge University Press.

Havé, L., Priot, A., Pisella, L., Rode, G., & Rossetti, Y. (2021). Unilateral body neglect: schemas versus images? In Oxford University Press eBooks (pp. 244–266). https://doi.org/10.1093/oso/9780198851721.003.0015

Hegde, P. (2022). A Brief Overview of Schemas Theory. International Journal of Innovative Research in Computer Science & Technology (IJIRCST), 10(8), 1–4. https://ijircst.org/DOC/ebch_1462-1.pdf?form=MG0AV3

Karunarathna, I., Gunathilake, S., Kapila De Alvis, K., Rajapaksha, S., Warnakulasooriya, A., Athulgama, P., … & Perera, N. From Encoding to Retrieval: Optimizing Learning.

Krasny, K. A., Sadoski, M., & Paivio, A. (2007). Unwarranted Return: A response to McVee, Dunsmore, and Gavelek’s (2005) “Schema Theory Revisited.” Review of Educational Research, 77(2), 239–244. https://doi.org/10.3102/003465430301958

Mayer, R. E., & Fiorella, L. (2021). The Cambridge Handbook of Multimedia Learning. Cambridge University Press.

Neumann, K. L., & Kopcha, T. J. (2018). The use of schema theory in learning, design, and technology. TechTrends, 62(5), 429–431. https://doi.org/10.1007/s11528-018-0319-0

Rumelhart, D. E., & Norman, D. A. (1980). Analogical processes in learning. https://doi.org/10.21236/ada092233

Salas, E., Tannenbaum, S. I., Kraiger, K., & Smith-Jentsch, K. A. (2012). The science of training and development in organizations. Psychological Science in the Public Interest, 13(2), 74–101. https://doi.org/10.1177/1529100612436661

Thorndyke, P. W., & Yekovich, F. R. (1979). A critique of schemata as a theory of human story memory (No. P-6307). Santa Monica, CA. Rand.

Wagoner, B. (2013). Bartlett’s concept of schema in reconstruction. Theory & amp; Psychology, 23(5), 553–575. https://doi.org/10.1177/0959354313500166