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ERIC Number: ED372094
Record Type: Non-Journal
Publication Date: 1994-Apr
Pages: 19
Abstractor: N/A
Reference Count: N/A
Using Artificial Neural Networks in Educational Research: Some Comparisons with Linear Statistical Models.
Everson, Howard T.; And Others
This paper explores the feasibility of neural computing methods such as artificial neural networks (ANNs) and abductory induction mechanisms (AIM) for use in educational measurement. ANNs and AIMS methods are contrasted with more traditional statistical techniques, such as multiple regression and discriminant function analyses, for making classification or placement decisions in schools and colleges. Classification rates obtained with multiple regression and discriminant analysis were compared with ANN (back propagation) and AIM methods across a number of plausible models of algebra proficiency that included measures of arithmetic ability, high school achievement, test anxiety, and gender. Analyses were conducted on a sample of 290 male and 310 female college freshmen for the entire sample and for each gender. At each stage 10 randomly selected subsets were used to train and test the neural computing methods. In general, ANN and AIM methods outperformed the more traditional methods. Results suggest that neural computing methods may lead to higher rates of classification accuracy, particularly when underlying models are nonlinear. Included are four tables, and one figure. (Contains 17 references.) (Author/SLD)
Publication Type: Reports - Research; Speeches/Meeting Papers
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A