Predicting Performance in High Technology Fields Using Rate Measures

Abstract

Modern organizations require dynamic, and cognitively-complex, information-bases from their personnel. Methodology and knowledge requirements change constantly with new technological innovations. The use of traditional paper-and-pencil measures of achievement and ability cannot accurately assess learning acquisition in novel situations. Prediction criteria based on rate at which information is acquired rely less on past experience and education, and more on changes in performance over time. The present research, based on these information-processing assessment techniques, used Computerized Experimental Learning Techniques (CELTS) to measure rate and level of learning. These CELTS were used to predict the performance of students enrolled in Computer Science, Electrical Engineering, and Mechanical Engineering classes. CELT-based learning level parameters contributed to prediction of course grade in the Computer Science and Engineering courses. Unique contributions of CELT learning rate measures were also determined after the variance in students' grade point averages were accounted for. Applications of the current research are important in the development of selection criteria for dynamic personnel postions. Future research is needed to document specific cognitive requirements of individual professions, and to develop computer-based techniques which assess these functions.