Item univocality and inter-item interference
(from P.A.Wozniak, Economics of Learning)
Univocality of item is not as much about minimizing the complexity of the synaptic patterns as it is about making sure that disjoint patterns are used for different items. Similar wording or even similar associations evoked by two separate items results in inter-item interference, which very often leads to confusion, providing wrong answers in reference to well-remembered pieces of knowledge, insufficient neural stimulation, and lack of uniform memory consolidation over the synapses involved in both the interfering and the interfered with synaptic pattern.
A very typical interference problem is the terminological ambiguity. For example, there is nothing wrong with the question "What is the formula for marginal rate of substitution?" as long as the substitution of two competing products is concerned. However, as soon as we move to the isoquant analysis of the production process, the marginal rate of substitution of capital for labor starts interfering with the until now simple picture. Indeed, in the latter case, the correct term to use is the marginal rate of technical substitution; however, this terminological nuance does not help much to eliminate the interference problem. A very simple solution to the above interference problem is to provide strong context clues in the question. For example:
Q: What is the formula for marginal rate of substitution of products X and Y?
A: dX/dY
Q: What is the formula for marginal rate of technical substitution of capital and labor?
A: dC/dL
Though the solutions to the problem of interference usually appear to be very simple, the mere process of locating potentially interfering item poses a formidable challenge to a knowledge system developer. Indeed, there is only one true and tried method of eliminating interfering items: memorizing the entire knowledge system. Only the neural network of the human brain can on-the-fly spot the problematic similarities. This clearly illustrates the fact that practically no subject knowledge system for active recall system based on repetition spacing should be designed in detachment from the natural learning process. This naturally increases the development cost manifold.
Let us now consider a case of strong semantic inter-item interference. The following items all deal with the problem of diseconomies of scale; however, no explicit interference problem strikes the eye at first:
Q: What is the frequently quoted argument for the U-shaped cost function?
A: most companies work at about 90% of their maximum capacity
Q: Why does the theory claim that every firm must reach a point of constant returns to scale with increasing output?
A: by striving to push the production to the limits, the company must at some point decrease its cost efficiency (overloading people, machinery, facilities, etc.)
Q: Why corporations may encounter problems when growing beyond a certain point (cf. River Rouge plant)?
A: because of managerial problems
Q: What is the main factor of diseconomies of scale in the US economy?
A: trade union activity
Upon a closer look, it appears that semantically, all the above items ask the same question "What are the reasons of diseconomies of scale?", while each of the item provides a different answer. Naturally, this is a trouble in the making for the student. It want take long before he or she starts confusing the River Rouge case with trade union activity problems, or attribute the U-shaped cost curve to dimension-related managerial problems. In a well-designed, interference free knowledge system, there is little choice but to use some of enumeration techniques to list the most important factors contributing to decreasing returns to scale.
As I tried to demonstrate, knowledge system authors have little choice but to memorize their own knowledge systems before making them available to a wider group of students. Both the terminological and semantic interference make one of the most important factors that greatly reduce the effectiveness of working with self-instruction systems based on active recall.