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Professor Paul Horst's Legacy: A Differential Prediction Model for Effective Guidance in Course Selection
Author(s) -
Clemans William V.,
Lunneborg Clifford E.,
Raju Nambury S.
Publication year - 2004
Publication title -
educational measurement: issues and practice
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.158
H-Index - 52
eISSN - 1745-3992
pISSN - 0731-1745
DOI - 10.1111/j.1745-3992.2004.tb00162.x
Subject(s) - horst , selection (genetic algorithm) , differential (mechanical device) , test (biology) , computer science , interface (matter) , psychology , medical education , engineering ethics , mathematics education , artificial intelligence , medicine , engineering , paleontology , tectonics , biology , aerospace engineering , bubble , maximum bubble pressure method , parallel computing
The role of testing in determining college admissions and the impact of that testing on high school students are commanding increasing attention. There is emerging evidence that admissions decisions made solely on the basis of high school record can serve the college as well or better than decisions based, at least in part, on scores obtained on admissions tests. Further, though there is a recognized need for guidance, the tests in current use for admission provide limited information for helping students in making course selections that would make the best use of their abilities. This article reviews an alternative, long neglected and little known, for testing at the high school‐college interface that would meet this need. Testing to provide differential prediction of college performance, as proposed by Paul Horst, focuses on allowing students to make the best match between their achievements, interests, and goals with the undergraduate programs of study available to them.