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Identifying potential “dropouts” from college physics classes
Author(s) -
Wollman Warren,
Lawrenz Frances
Publication year - 1984
Publication title -
journal of research in science teaching
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.067
H-Index - 131
eISSN - 1098-2736
pISSN - 0022-4308
DOI - 10.1002/tea.3660210406
Subject(s) - mathematics education , dropout (neural networks) , drop out , identification (biology) , science education , mathematics , computer science , biology , machine learning , demographic economics , botany , economics
Hudson and Rottman (1981) established that mathematics ability is probably a secondary factor influencing dropout from college physics courses. Other factors remain to be found for predicting who will drop out or at least have difficulty with the course. When mathematics ability is coupled with general indicators of performance (total GPA and ACT natural science), prediction of performance for those who complete the course is substantially improved. Moreover, discriminant analyses reveal who will have at least some difficulty, but not who will drop out. The problem of isolating specific weaknesses of students who have difficulty persists. Physics achievement appears to depend on mathematics ability only to the extent that students possess the ability to utilize mathematics knowledge for solving physics problems. Identification of the specific aspects of this ability as well as the specific deficiencies leading to dropout should be the object of future research. For the present, interviews might be more revealing than group testing methods.