Abstract
A Gagne-style learning hierarchy often permits a large number of alternate linear arrangements (sequences) of instructional objectives. An alternative is described here to traditional methods of choosing between sequences. Its premise is that, for every sequence, a value temed thememory load can be calculated which is theoretically related to the probability that students will fail to recall prerequisite objectives. A graph theoretic approach is taken in presenting an algorithm which generates a minimal memory load sequence from a learning tree, a restricted but frequently encountered type of learning hierarchy. In order to assess the effectiveness of the algorithm in generating low memory load sequences when given hierarchies which are not trees, it was applied to several published examples of learning hierarchies. The results indicated that the algorithm is effective as an heuristic, especially when combined with a hill-descending procedure which attempts to incrementally improve the generated sequence.
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