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instructional_design:structural_learning [2011/03/15 16:59] jpetrovic [What is structural learning theory?] |
instructional_design:structural_learning [2011/03/16 12:00] jpetrovic [What is the practical meaning of structural learning theory?] |
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===== What is structural learning theory? ===== | ===== What is structural learning theory? ===== | ||
- | Structural learning theory suggests that structures (problems) that a learner must learn, need to be formed as rules. Those rules can be simplified into **lower-order rules** (//atomic components//) which represent most basic concepts learner needs to know when dealing with a problem from given domain. By combining these atomic components and application of more complicated to lower order rules new **higher-order rules** are derived. Higher-order rules can be used to solve complex problems in the whole domain. | + | Structural learning theory suggests that structures (problems) that a learner must learn, need to be formed as **rules** performed on a **domain**. |
- | Rules, according to the structural learning theory have three parameters: | + | A domain here is defined as a set of characterizing **inputs** and **outputs**. Inputs and outputs can be anything, even a process, an idea or a concept. For example: |
+ | |||
+ | * list of verbs (input) -> present participles (output). | ||
+ | |||
+ | Operations performed on given inputs are called rules, and they generate unique outputs. Rules can contain different levels of abstraction and are always defined with three parameters: | ||
* **domain** - its allowed **inputs**, | * **domain** - its allowed **inputs**, | ||
* **range** - its expected outputs, and | * **range** - its expected outputs, and | ||
- | * **procedure** - the sequence of **operations** to perform **on the inputs**. | + | * **procedure** - the sequence of **operations** to perform **on the inputs**. |
- | In accordance with structural learning theory, first step in instructional design or learning is **definition of the problem domain through structural analysis**. Problem domain can be both well- and ill-defined((An ill-defined domain is one in which rules are quite simple, yet there is no direct complete solution like chess, or poetry writing)). In case of an ill-defined domain, it should be divided into well-defined sub-domains which can generate at least one rule. Domain sets the inputs and desired outputs for problem solving. | + | For example: a rule //form present participle// has the domain of all English verbs, the range of present participles and the procedure of adding "-ing" ending to the verb. |
- | Domain definition is followed by **construction of hierarchy of rules** for well-defined domains. Rules should be explained on prototype problems, but can also leave some **gaps** in problem solving procedure, which **are then converted into higher-order problems** containing gap rules. Higher-order rules are then used to fill the gap, but can also validate lower level rules. | + | Rules can be simplified into **lower-order rules** (//atomic components//) which represent most basic concepts learner needs to know when dealing with a problem from given domain. By combining these atomic components and application of more complicated to lower order rules new **higher-order rules** are derived. Higher-order rules are rules which can have other rules as inputs or outputs (for example mathematical theorems) and they can be used to solve complex problems in the whole domain. |
- | An important part of the theory is also **prior knowledge (rules)** of the learner, that will **enable construction of new rules**. This knowledge can be examined by instructor, that can be both human or artificial. | + | Structural learning theory further attempts to identify components crucial for solving the given problem and is based on the procedure called //structural analysis//. Structural analysis is performed in the following steps: |
- | Structural learning theory's applications have been made in **mathematics** and **language learning**. | + | - The first step is to identify problem domain inputs and outputs, or even only outputs (representative problems). |
+ | - Rules should be defined and explained on each representative problem. Problem domain can be both well- and ill-defined((An ill-defined domain is one in which rules are quite simple, yet there is no direct complete solution like chess, or poetry writing.)). In case of an ill-defined domain, it should be divided into well-defined sub-domains which can generate at least one solution rule. | ||
+ | - Each solution rule should be converted into a new higher-order problem and new higher-order rules for solving them. | ||
+ | - Redundant rules should be eliminated and the whole process repeated until simple enough rules are reached. | ||
+ | An important part of the theory is also **prior knowledge (rules)** of the learner, that will **enable construction of new rules**. This knowledge can be examined by instructor, that can be both human or artificial. | ||
===== What is the practical meaning of structural learning theory? ===== | ===== What is the practical meaning of structural learning theory? ===== | ||
+ | Structural learning theory's applications have been made in **mathematics** and **language learning**. | ||
===== Criticisms ===== | ===== Criticisms ===== | ||
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===== Bibliography ===== | ===== Bibliography ===== | ||
- | [[http://web.cortland.edu/frieda/id/IDtheories/4.html|Structural Learning Theory.]] | + | [[http://web.cortland.edu/frieda/id/IDtheories/4.html|Instructional Design Theory Database Project: Structural Learning Theory.]] Retrieved March 15, 2011. |
[[http://www.odu.edu/educ/roverbau/Class_Websites/761_Spring_04/Assets/course_docs/ID_Theory_Reps_Sp04/Scandura_Chapman.pdf|Scandura, J. M. Structural learning theory. Instructional Design Theories and Models: An Overview of Their Current Status: p215–245. 1984.]] | [[http://www.odu.edu/educ/roverbau/Class_Websites/761_Spring_04/Assets/course_docs/ID_Theory_Reps_Sp04/Scandura_Chapman.pdf|Scandura, J. M. Structural learning theory. Instructional Design Theories and Models: An Overview of Their Current Status: p215–245. 1984.]] |