One of the main goals of cognitive psychology is to understand the relationship between knowledge and learning. To do so, researchers developed the information processing model (IPM) in the early 1950s, which has been used as the modal model of cognition since that time. The IPM consists of three main components, including sensory memory, working memory, and long-term memory (Neath & Surprenant, 2003). Sensory memory processes incoming sensory information for very brief periods of time, usually one-half to three seconds. The amount of information held at any given moment in sensory memory is limited to five to seven discrete elements such as letters of the alphabet or pictures of human faces. Working memory refers to real-time information processing in which meaning is assigned to incoming information from a text, pictures, or math problem. Long-term memory refers to a permanent repository of knowledge in memory. To use a computer analogy, sensory memory corresponds to inputting information via the keyboard, working memory corresponds to information current on the computer screen, and long-term memory corresponds to the computer's hard drive.


Information processing theory assumes that ongoing mental activity in working memory is aided by different types of information in long-term memory that supports thinking and problem solving. At least three categories of knowledge are stored in long-term memory, including declarative, procedural, and self-regulatory knowledge (Anderson, 1976; Tulving, 1972). Declarative knowledge refers to the facts and concepts. Procedural knowledge refers to how to do things. Self-regulatory knowledge refers to knowledge individuals have about themselves as learners, what they know, and how to control their learning. All three types of knowledge are important. However, even large amount of declarative and procedural knowledge, without self-regulatory knowledge to support it, does little to help people survive and adapt successfully.

Declarative knowledge is a broad category that includes facts, concepts, and the relationships between concepts that lead to an integrated conceptual understanding of a domain of knowledge. Declarative knowledge includes thousands of facts such as the names of colors, numbers, coins, and trees. Concepts consist of two or more units of factual information that are used to understand a broader phenomenon such as human rights or social justice. Often concepts are phenomena that can be described abstractly, such as freedom or happiness, even though these phenomena do not exist in the physical world. Declarative knowledge also includes integrated conceptual knowledge that is sometimes referred to as structural knowledge or mental models (Halpern, 2003).

One of the most important organizational units in memory is the schema, which refers to an organized body of information about some distinct domain of knowledge. For example, every adult has a “car schema” in which information about different types of cars is organized. A car schema could be organized in several ways using either the cost or size of the car to generate subcategories. Anyone asked to name a luxury car could quickly name cars such as Rolls Royce or Bentley as examples. Most other people who share the same cultural group would have this information organized in a similar way as well.

Procedural knowledge is knowledge about how to do things, ranging from simple action sequences such as brushing one's teeth, to complex actions such as driving a car. Most adults possess an enormous amount of procedural knowledge, which enables them to perform complex activities such as grocery shopping easily because those procedures are automated though practice. Although there are many different types of action sequences, there are three sequences of special importance, including complex scripted actions, algorithms, and heuristics that are stored as single entities in memory.

Scripts refer to extended action sequences and plans that are stored in memory as single units of knowledge. Each person possesses thousands of scripts, for activities such as getting dressed, driving a car, dining at restaurants, and social interactions that save enormous amounts of time because scripts can be activated intact from memory. Scripts are analogous to schemata. Whereas schemata help individuals organize declarative knowledge about a topic or domain, scripts help people organize and remember steps in a complicated action sequence. Algorithms and heuristics can be thought of as “mini-scripts.” An algorithm is a rule for solving a specific problem that always works, whereas a heuristic is a rule of thumb for solving a problem that often works, but not always. For example, an algorithm could be used to compute the average of 1,000 scores by adding all the scoring and dividing the total by the number of scores. A simple heuristic could also be used to estimate the average by sampling seven scores at random, rank ordering the scores, and using the middle score as an estimate.

Self-regulatory knowledge is knowledge about how to regulate one's memory, thought, and learning (Schunk & Zimmerman, 2006). Declarative and procedural knowledge alone are not sufficient to be an adaptive learner. In addition, individuals must possess knowledge about themselves as learners and about the skills they need to learn effectively. Self-regulatory knowledge can be divided into two types, including domain specific knowledge and domain general knowledge (Alexander, 2003). The former is knowledge individuals possess about themselves with regard to a domain such as mathematics or a sub-domain such as geometry. In contrast, the latter includes general knowledge such as learning strategies that enable people to adapt and self-regulate across all domains.

Domain specific knowledge refers to knowledge that is encapsulated within a particular domain of learning such as mathematics, history, and literature. Sometimes domain specific knowledge is referred to as topic knowledge, although this term suggests knowledge about a specific topic such as geometry within a broader domain such as mathematics. Domain specific knowledge is extremely important in the development of expertise and skilled problem solving (Ericsson, 2003). Cognitive psychologists once believed that it was possible to capture the knowledge of experts through interviews and observation, and in turn, help novices become experts quickly. However, researchers discovered that experts become experts slowly through years of hard work, deliberate practice, and guidance from other experts. Most experts have deep knowledge in one domain, yet shallow knowledge in other domains, due in large part to the amount of time they invest in developing expertise in their chosen domain. Expertise in one domain usually does not transfer spontaneously to other domains, although it can be facilitated through direct instruction and analogical cues, which help the learner understand the relationship between two different problems.

Domain general knowledge refers to knowledge that is equally useful to learners across domains and topics. Domain general knowledge often is referred to as meta-cognitive knowledge, which includes knowledge of cognition and regulation of cognition (Schraw, 2006). The former includes strategy knowledge and conditional knowledge, while the latter includes knowledge of regulatory skills such as planning, monitoring, and evaluation of learning. Metacognitive knowledge enables learners to identify problems and self-correct by changing strategies.


Like other experts, skilled teachers possess different types of knowledge that facilitates classroom practice. Shulman (1987) suggested that skilled teachers possess knowledge about domain content, pedagogy, learners and student development, as well as educational contexts, and educational ends, purposes and values. Many educators view content and pedagogical knowledge as essential to effective teaching. Content knowledge refers to knowledge in a particular domain, such as mathematics, science, social studies, reading, and language arts. Pedagogical content knowledge has been defined as “a collection of teacher professional constructions, as a form of knowledge that preserves that planning and wisdom of practice that the teacher acquires when repeatedly teaching a certain topic” (Hashweh, 2005, p. 273).

Content knowledge is domain-specific in nature, whereas many teachers have endorsed domain-general pedagogy that emphasizes constructivist teaching. The fundamental idea of constructivism is allowing students to connect to the learning environment through problem-based learning, inquiry activities, and dialogues with others. By allowing students to construct knowledge as learners, the educational goal is to help them think critically about concepts. There are many strategies that a teacher might employ when teaching a particular content areas: (a) scaffolding, which allows the learner to make sense of complex tasks; (b) modeling, which requires the teacher to think aloud about problem solving; while (c) coaching, guiding, and advising requires the teacher to probe the students' thinking. Experiences should be genuine and relevant to the learners and inquiry is used as an approach for students to engage in discovery learning.


Knowledge facilitates information processing and long-term learning by providing an integrated conceptual network of information in long-term memory. Knowledge in isolation (i.e., inert knowledge) is of little value, whereas organized knowledge is powerful because it enables people to sort and store information in memory, predict and judge, and evaluate their learning accurately. Knowledge also enables individuals to process information more efficiently (Neath & Surprenant, 2003).

Recent research emphasizes the importance of constructed knowledge, distributed cognition, and distributed knowledge. Constructivism refers to the assumption that knowledge is constructed actively by learners, rather than transmitted passively through lecture, discussion, or observation. Constructivism assumes that active learning is better because knowledge is understood in a deeper, more relevant way. An extension of constructivism is the assumption that knowledge and learning are more sophisticated when mutually shared across multiple learners in an active dialogue. This is referred to often as distributed cognition. In contrast, distributed knowledge refers to knowledge that is distributed across two or more individuals, but may be distributed across hundreds of individuals, such as knowledge about complex technological products. Knowledge can also be distributed between humans and human artifacts such as books and tools such as calculators.

Distributed cognition and knowledge are topics of considerable debate for both practical and theoretical reasons. Many educators assume that mutually constructed meaning is more dynamic than individually constructed meaning, and some believe that knowledge exists only as a distributed set of beliefs and assumptions across multiple individuals (Zhang & Patel, 2006). In addition, many have argued that complex ideas and knowledge require multiple contributors to exist at all. From a theoretical standpoint, researchers are interested in how to best foster distributed cognition across multiple people and/or machines, and how to represent knowledge in human and machine databases in a distributed manner.

Like students, teachers possess different types of knowledge that are essential to effective teaching (Shulman, 1987). Teachers develop this knowledge slowly over time, often taking 5 to 10 years of teaching practice to develop deep expertise. Both students and teachers construct most higher-order conceptual knowledge through personal experiences, reflection on experiences, and dialogue with other students and teachers (Ericsson, 2003). Individuals also construct metacognitive knowledge that enables them to self-regulate within their domain of expertise. Constructed executive knowledge is assumed to be stored in long-term memory in sophisticated schemata and scripts that enable the individual to perform a variety of complex skills with a high degree of efficiency.


Alexander, P. A. (2003). The development of expertise: The journey from acclimation to proficiency. Educational Researcher, 32, 10–14.

Anderson, J. R. (1976). Language, memory, and thought. Mahwah, NJ: Erlbaum.

Ericsson, K. A. (2003). The acquisition of expert performance as problem solving. In J. E. Davidson & R. J. Sternberg (Eds.), The psychology of problem solving (pp. 31–83). Cambridge, England: Cambridge University Press.

Halpern, D. F. (2003). Thought and knowledge: An introduction to critical thinking (4th ed.). Mahwah, NJ: Erlbaum.

Hashweh, M. Z. (2005). Teacher pedagogical constructions: a reconfiguration of pedagogical content knowledge. Teacher and Teaching: Theory and Practice 11(3), 273–292.

Neath, I., & Surprenant, A. M. (2003). Human memory: An introduction to research, data, and theory (2nd ed.). Pacific Grove, CA: Brooks/Cole Publishing.

Schraw, G. (2006). Knowledge: Structures and processes. In P. Alexander & P. Winne (Eds.), Handbook of educational psychology (2nd ed., pp. 245–264). San Diego, CA: Academic Press.

Schunk, D. H., & Zimmerman, B. J. (2006). Competence and control beliefs: Distinguishing the means and the ends. In P. Alexander & P. Winne (Eds.), Handbook of educational psychology (2nd ed., pp. 349–367). San Diego, CA: Academic Press.

Shulman, L. (1987). Knowledge and teaching: foundations of the new reform. Harvard Educational Review 57, 1–22.

Tulving, E. T. (1972). Episodic and semantic memory. In E. Tulving & W. Donaldson (Eds.), Organization of memory (pp. 381–403). San Diego, CA: Academic Press.

Zhang, J., & Patel, V. (2006). Distributed cognition, representation, and affordance. Pragmatics & Cognition 14, 333–341.