APPLICATION OF NEURAL NETWORK MODEL FOR ANALYSIS AND EVALUATION OF STUDENTS Individual DIFFERENCES | ||
| The International Conference on Electrical Engineering | ||
| Article 6, Volume 1, 1st International Conference on Electrical Engineering ICEENG 1998, March 1998, Pages 47-60 PDF (1.99 M) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/iceeng.1998.60076 | ||
| Author | ||
| H. M. HASSAN | ||
| Lecturer at dept. of educational technology, at college of specified education, Benha, Egypt. Currently with dept. of Computer Engineering, Eng. Academy, Tajoura, Libya. | ||
| Abstract | ||
| This work belongs to a novel research direction adapted to Artificial Neural Network (ANN) technology with cognitive - emotional Interactions of educational technology. That research direction, is basically adopted for the study of fundamental design principles required for solving some educational issues. So, interpretation and prediction of cognitive data associated with brain function and students' interactive behavio- during learning/teaching process have to be well studied. In other words, the combination of both ANN and educational technologies motivates, and supports well, the new research studies planned for by solving for some critical problems related to learning/teaching process. In this paper, the analytical results obtained from computer simulation for education measurement are presented. These results given herein, were carried out though the design of a realistic Feed Forward Neural Network (FFNN) model simulating both of teachers' and students' behaviors in our classrooms. More properly, the problems that observed due to individual differences of learning students' level and their response performance were considered. Interactions of students' and teachers' response performance is evaluated using a MultiLayer Perception (MLP) as an ANN model trained by back propagation of errors under supervision (With a teacher). The relation between the desired and obtained outputs of the NILP model is used to measure the response performance of learning process. The mean value of relative errors obtained, and the variance value of this error is computed, for compari5ion of teachers' ability, and students' response considering individual differences. Thus, tine obtained results include computations, that many times repeated to illustrate the learning processes individualities they carried out for four teachers and nine students differfmt groups (each includes eight students). The paper is organized as follows. At the next section, an introduction is given that to show how the new approach of ANN models applications in educational technology is acceptable and realistic. In section 11 the suggested ANN model description is briefly introduced. The obtained anaklical results and some comments are shown at section III in four numeric tables and four graphical figures Finally some conclusive remarks are given at section IV. | ||
| Keywords | ||
| artificial neural network; educational technology; Education measurement; and Individual differences | ||
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