Landamatics, or Algo-Heuristic Theory as it was originally called, was developed by Lev Landa in the early 1950’s.

Landa (1975) said, “It is common knowledge that pupils very often possess knowledge that is necessary in a certain subject, but they cannot solve problems. Psychologists and teachers often explain this by saying that their pupils do not know how to think properly, they are unable to apply their knowledge, the processes of analysis and synthesis had not been formed in their minds, . . .”.

Landa believes knowledge is made up of three elements:

1. image – the mental picture of an object,

2. concept – the knowledge of the characteristics of an object,

3. propositions – the relationships the object and it’s parts to other objects.

Specification of Theory
(a) Goals and preconditions
Processes – Sets of operations: Operations are transformations of (or changes to) material objects or mental models.

(b) Principles
1. It is more important to teach algo-heuristic processes versus prescriptions.
2. Processes can be taught through prescriptions and demonstrations of operations. (Operations = changes of mental or material knowledge)
3. Discovery of processes is more valuable than providing formulated processes.
4. Individualize instruction.

(c) Condition of learning
1. Instructional processes are influences directed by a “teacher” and directed at transformation. (teacher refers to any teaching agent, live or material, i.e. books, AV, computer)
2. Instructional processes are affected by teacher actions or instructional operations.
3. Instructional processes can be affected by certain conditions.
– external conditions, student psychology, teacher knowledge
4. There are three types of instructional rules: descriptive, prescriptive, and permissive. Descriptive rules are statements about what occurs. Prescriptive rules are statements about what should be done. Permissive rules indicate possible alternatives to prescriptive rules.

(d) Required media

(e) Role of facilitator
Teaching involves solving instructional problems; the teacher has to determine and perform actions that should be executed in order to meet objectives.

(f) Instructional strategies
Determining Content

1. Uncover process underlying expert learners and mastery level performers.
2. Describe the process with a hypothetical descriptive model.
3. Test the correctness of the model.
4. Improve the model if necessary.
5. Optimize the model if possible.
6. Design the final algorithmic or non-algorithmic process to allow the learners to perform on a mastery level.
7. Identify learning procedures leading to the development of algorithm or heuristic performance.
8. Design algo-heuristic teaching procedures.
9. Design algo-heuristic based training materials.
10. If necessary, create a computer-based or other media based programmed instruction.
11. Design methods for evaluation.

Instructional Method 1 – The step-by-step approach
1. Present the procedure to the student and demonstrate problem solving.
2. Develop the first operation.
3. Present a problem that requires the first operation and practice that operation.
4. Develop the second operation.
5. Present a problem that requires application of both operation and practice.
6. Develop the third operation.
7. Present a problem that represents all three problems.
8. Proceed until all problems are mastered.

Instructional Method 2 – Developing individual operations
1. Determine whether the student understands the meaning of a direction in the a prescription and its operations.
If yes:
2. Present a problem that requires application of the problem.
3. Name the operation (give the learner a self-command) before he/she executes the problem.
4. Present the next problem and have the learner give the command internally.
5. Continue practicing the operation until mastery.
If no:
2. Explain what the student does not understand.
3. Test the correctness of understanding and allow for practice. Provide extra explaination and practice.
4. Go to #2 under “yes” above.

(g) Assessment method
Student is able to complete the operation at a mastery level.

Application– Complex Sciences such Neurosciences.

strategic knowledge in neuroscience represented as an algorithm

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