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Teaching Algorithms and Learning Algorithms

Authors: Klaus Bung;

Teaching Algorithms and Learning Algorithms

Abstract

Abstract *A11 teaching processes can be precisely specified by means of Helmar Frank's six didactic variables (each of which can in turn be interpreted as a vector of vectors). These are: learning system, teaching system, subject matter, target standard, environment and teaching algorithm. In order to improve understanding and retention of the subject matter, teaching programmes use longer and more complex teaching algorithms than do conventional instruction and lectures. Retention requires a certain amount of well‐spaced repetition. The control of the repetition function can be exercized by the teaching system or by the learning system, provided the latter is willing and able to do so. Conventional instruction leaves the control of the repetition function (and other learning functions) to the learning system and often fails to reach the target standard because it has overestimated the ability or willingness of the learning system to control its own learning activities. Programmed instruction makes a cons...

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
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