A general model for a quantitative description of individual growth in learning was developed on the basis of two basic assumptions concerning growth processes generally and the learning process in particular. The first assumption is that the performance P is P(T k ), where T is the learning time and k a parameter that represents the growth form. The second assumption is that the learning rate is proportional to the material yet to be learned. It was proved theoretically that the general growth model is more comprehensive than the existing models. Data obtained in this study, in both short-and long-run learning sequences in a classroom as well as in problem solving, were processed through the model. Published data in such areas as intelligence growth, mastery learning, free recall, and social maturity were also processed through the model. The applicability of the model to other growth processes such as height and weight growth and physical and chemical rate reactions is discussed. These findings suggest that the general growth model governs the growth phenomena.

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Theoretical and experimental support for the hierarchical relations between the abilities of solving computational problem components were obtained. The general hypothesis was that the ability in each stage of the hierarchy is composed of the sum of the products of its prerequisites and their respective path coefficients. Accordingly, three hypothetical relations were tested, using a sample of 266 eleventh-grade students studying in 14 schools spread over the three main districts in Israel and a strength-ofmaterial test and path analysis of the data were carried out.The main findings are that mathematical concepts and laws comprehension account for 67 per cent of the variance in the ability of students to derive a suitable formula for the solutions of one unknownvariable. This derivation, together with mathematical ability, account for 89 per cent of the variance in verbal computational problem-solving ability. The hypotheses were accepted at a level of significants of p < 0.0001. These findings imply that particular emphasis should be placed on practice in formula derivation. Other activities related to verbal computational problem-solving ability should be reserved for home assignment.