Understanding (also called intellection) is a psychological process related to an abstract or physical object, such as a person, situation, or message whereby one is able to think about it and use concepts to deal adequately with that object.
An understanding is the limit of a conceptualisation. To understand something is to have conceptualised it to a given measure.
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It is difficult to define understanding. If we use the term concept as above, the question then arises as to what is a concept? Is it an abstract thing? Is it a brain pattern or a rule? Whatever definition is proposed, we can still ask how it is that we understand the thing that is featured in the definition: we can never satisfactorily define a concept, still less use it to explain understanding.
It may be more convenient to use an operational or behavioural definition, that is, to say that somebody who reacts appropriately to X understands X. For example, one understands Swahili if one correctly obeys commands given in that language. This approach, however, may not provide an adequate definition. A computer can easily be programmed to react appropriately to commands, but there is a disagreement as to whether or not the computer understands the language (see the Chinese room argument).
According to the independent socionics researcher Rostislav Persion:
In the cognitive model presented by MBTI, the process of introverted thinking (Ti) is thought to represent understanding through cause and effect relationships or correlations. One can construct a model of a system by observing correlations between all the relevant properties (e.g. The output of a NAND gate relative to its inputs). This allows the person to generate truths about the system and then to apply the model to demonstrate his or her understanding. A mechanic for example may randomly, or algorithmically probe the inputs and outputs of a black box to understand the internal components through the use of induction. INTP, ISTP, ESTP, and ENTP all use Ti and are usually the best of the 16 types at understanding their material environment in a bottom-up manner. These types may enjoy mechanics and digital electronics because of the 1 to 1 correlation between cause and effect relationships in these fields. Understanding is not limited to these types however as other types demonstrate an identical process, although in other planes of reality; ie. Social, Theological and Aesthetic. A potential reason for the association of understanding with the former personality types is due to a social phenomenon for asymmetrical distribution of gratification. In the field of engineering, engineers probe or study the inputs and outputs of components to understand their functionality. These components are then combined based on their functionality (similar to computer programming) to create a larger, more complex system. This is the reason why engineers attempt to subdivide ideas as deep as possible to obtain the lowest level of knowledge. This makes their models more detailed and flexible. It may be useful to know the formulas that govern an ideal gas, but to visualise the gas as being made up of small moving particles, which are in turn made up of even smaller particles, is true understanding. People who are understanding (through the use of Ti) usually value objects and people based on usefulness, as opposed to the people who use extroverted thinking (Te) who view people or things as having a worth. In order to test one's understanding it is necessary to present a question that forces the individual to demonstrate the possession of a model, derived from observable examples of that model's production or potential production (in the case that such a model did not exist beforehand). Rote memorization can present an illusion of understanding, however when other questions are presented with modified attributes within the query, the individual cannot create a solution due to a lack of a deeper representation of reality.
Another significant[citation needed] point of view holds that knowledge is the simple awareness of bits of information. Understanding is the awareness of the connection between the individual pieces of this information. It is understanding which allows knowledge to be put to use. Therefore, understanding represents a deeper level than simple knowledge.
Gregory Chaitin, a noted computer scientist, propounds a view that comprehension is a kind of data compression[1]. In his essay 'The Limits of Reason', he argues that 'understanding' something means being able to figure out a simple set of rules that explains it. For example, we 'understand' why day and night exist because we have a simple model - the rotation of the earth - that explains a tremendous amount of data - changes in brightness, temperature, and atmospheric composition of the earth. We have 'compressed' a large amount of information by using a simple model that predicts it. Similarly, we 'understand' the number 0.33333... by thinking of it as one-third. The first way of representing the number requires an infinite amount of memory; but the second way can produce all the data of the first representation, but uses much less information. Chaitin argues that 'comprehension' is this ability to compress data.
The concepts of comprehension, thought and understanding are also used in the short science fiction story Understand by Ted Chiang.
In a Thesis Book called "A study of Quality Improvements By Refactoring" made at the University of Antwerp on 2006 by Bart Du Bois (Phd) and promoted by many notable professors, the author explains that for a programmer to understand how to work with a new piece of code or a new system, five levels of abstraction have to be understood.
This is done by simply asking these five questions: (the following table was first plotted by Nancy Pennington in 1987 in a book called Comprehension Strategies in Programming)
The study defines understanding as conquering of all of the five abstractions. Since this model works for any type of software, as it is more about the programmer's mind than about the computer functionality, and software can repeat almost anything human, this model is very close to the ultimate definition of understanding of anything thinkable.
Lack of awareness of this pattern for evaluating abstractions, may bring about many Cognitive biases when complex ideas are explained. The idea of Refactoring in software is similar in the cognitive field to the repetition of an argument, with minor focus changes/enhancements to the argument each time.
In Catholicism and Anglicanism, understanding is one of the Seven gifts of the Holy Spirit.
Concepts are understood by establishing relationships with prior knowledge. But what are the kinds of relationships which help lend meaning to new concepts? Norman identified the "isa", "hasa", "cause", "act", "iswhen", "location", and "object" relationships, among others. Therefore, it appears that meaningful learning of some kind can occur when appropriate links are made to any of a variety of kinds of relevant prior knowledge, including:
Superordinate knowledge, which is broader and more inclusive. For example, for teaching the concept of erosion, you might relate it to the superordinate concept of movement of material, if the learners already learned what that is.
Coordinate knowledge, which is on the same level of breadth and inclusiveness. For example, erosion might be related to the opposite kind of movement of material, the coordinate concept of sedimentation (the depositing of material in layers), if the learners already learned what that is.
Subordinate knowledge, which is narrower and less inclusive. For example, erosion might be related to the subordinate concept of wind erosion, if the learners already learned what that is.
Experiential knowledge, which is specific cases of the new knowledge. For example, erosion might be related to the little gully that was formed in the dirt outside the school in the last big rain storm, if the learners were already familiar with that.
Analogic knowledge, which is similar but outside the content area of interest. For example, erosion might be related to sanding down some wood, if the learners were already familiar with that.
Causal knowledge, which indicates how something influences or is influenced. For example, erosion might be related to its effects on transportation (e.g., washing out dirt roads), if the learners were already familiar with that.
Procedural knowledge, which indicates how something is used. For example, erosion might be related to methods of contour plowing for preventing water erosion on farmland, if the learners were already familiar with that.
It is important to note that superordinate, coordinate, and subordinate knowledge can be of two types: kinds or parts. Any concept can be a kind of something or a part of something; it and a coordinate concept are both kinds of the same superordinate concept, or parts of the same superordinate concept; and it has both kinds and parts of itself. A circulatory system is a part of an organism and a kind of body system. Its parts include a heart and arteries and veins; and its kinds include 2-chamber circulatory systems and 1-chamber systems.
As can be seen from the above examples, each of these types of prior knowledge has a corresponding type of relationship which can contribute to one's understanding. It may be useful to think of these relationships as dimensions of understanding, many (but not all) of which will be important for any given idea that is to be understood. This is related to the notion of "breadth of understanding".
Principles, or interrelated sets of principles called causal models, are a very different kind of understanding. The water cycle is a causal model in which various changes (evaporation, condensation, and precipitation) occur, and a variety of other changes (events) influence them (temperature, humidity, wind, convection currents, and so forth). Causal models are understood primarily by: (1) establishing relationships between the real events that constitute a causal model and the generalities (principles or causal models) that represent them, and (2) learning about the network of causal relationships among those events (changes). This type of understanding will not be further discussed in this module, but you will have an in-class exercise to invent some instructional tactics for teaching it.
It is helpful to think in terms of obstacles to initial acquisition of conceptual understanding and obstacles to retention of that understanding. Understanding is quite the opposite of memorization in that acquisition is what is difficult; retention is relatively easy. Since acquisition is mainly a matter of relating the new knowledge to appropriate prior knowledge, there are three major obstacles. First, the appropriate prior knowledge must indeed have been acquired already. Second, the appropriate prior knowledge must be "activated" that is, it must be brought to mind. And third, the proper relationship between the new knowledge and the prior knowledge must be learned. The more links which are created with relevant prior knowledge, the greater the depth and/or breadth of understanding.
Once conceptual understanding has occurred, retrieval problems are relatively rare. However, if some piece of meaningful knowledge is not used for a long time, it can undergo what David Ausubel calls "obliterative subsumption" (I love that term!). To the extent that conceptual knowledge is subsumed under a broader, more inclusive representation of it, lack of use can result in the more detailed refinement being merged back into the subsumer from which it sprang, becoming indistinguishable from it. The more similar it is to its subsumer, the more quickly it is learned, but the more quickly it can also be forgotten.
How can you tell if someone understands? It is a lot more difficult to measure (or test for) understanding than to measure rote memorization. This is because understanding cannot be directly observed. It can only be inferred from various observable behaviors. There are observable behaviors for each of the kinds of relationships. They include contextualizing, comparing and contrasting, analyzing, instantiating, analogizing. and so forth. For causal understanding, they include such things as explanation (making an inference), prediction (describing an implication), and solution (solving a problem).
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