Abstract
The use of the Kalman filter in the Dutch pupil monitoring system LISKAL makes it possible to reduce with 30 to 50% the estimation error variance resulting from Bartlett's well-known cross-sectional factor score estimator. The PC-program LISKAL is based on a longitudinal LISREL model of reading achievements of pupils in primary education. It combines past and present information from educational test scores to estimate optimally individual pupil's achievements in reading skills: decoding speed, reading comprehension and spelling. It is explained how LISKAL works and how it can be used by the teacher in the class room. Most LISKAL results are given in graphical form. Different kinds of information are provided by LISKAL: 1) the individual pupil's achievements are compared at successive time points with those of a normative group, 2) the pupil is compared with itself at previous points in time, 3) on the basis of the model predictions can be made of future achievements to enable the teacher to take timely intervention measures, 4) by comparing predicted development with actual development the effect of intervention measures can be evaluated, 5) in addition to individual developmental curves the program also generates mean curves for groups of pupils as, for example, classes or schools. In the present version of LISKAL achievement variables are present as standardized variables in the form of z-scores, having mean 0 and standard deviation 1 at each point in time. This implies relative assessment in relation to a normative group. However, teachers are often interested in absolute developmental curves, especially for low achieving pupils. The problem of estimating absolute developmental curves can also be solved by means of the Kalman filter. The LISREL model on which it has to be based is more complicated, however. Absolute assessment will be added as an option in a new version of the LISKAL program based on a new research project with a nationwide sample of 3430 pupils.
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