Classification of high and low achievers in a music sight-reading task

Abstract

The unrehearsed performance of music, called ‘sight-reading’ (SR), is a basic skill for all musicians. It is of particular interest for musical occupations such as the piano accompanist, the conductor, or the correpetiteur. However, up until now, there is no theory of SR which considers all relevant factors such as practice-related variables (e.g. expertise), speed of information processing (e.g. mental speed), or psychomotor speed (e.g. speed of trills). Despite the merits of expertise theory, there is no comprehensive model that can classify subjects into high- and low-performance groups. In contrast to previous studies, this study uses a data mining approach instead of regression analysis and tries to classify subjects into predetermined achievement classes. It is based on an extensive experiment in which the total SR performance of 52 piano students at a German music department was measured by use of an accompanying task. Additionally, subjects completed a set of psychological tests, such as tests of mental speed, reaction time, working memory, inner hearing, etc., which were found in earlier studies to be useful predictors of SR achievement. For the first time, classification methods (cluster analysis, regression trees, classification trees, linear discriminant analysis) were applied to determine combinations of variables for classification. Results of a linear discriminant analysis revealed a two-class solution with four predictors (cross-validated error: 15%) and a three-class solution with five predictors (cross-validated error: 33%).

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