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Abstract

Content

Introduction

The work deals with the issues of forecasting and analysis of the quality of student’s education.

Evaluation of the effectiveness of the processes of preparing specialists is very important for the analysis and management of the university as an educational environment.

This task is difficult to formalize because of the heterogeneity, intellectuality and dynamism of interaction of subjects, so to describe the exact behavior of such a system by classical mathematical methods is very problematic.

1. Goal and tasks of the research

This work is devoted to solving the problem of forecasting the quality of students' professional education depending on their personal characteristics and other factors. It is solved on the basis of the application of artificial neural networks and is reduced to the development of a neural network model, which is able to functionally describe the dependence of the student's professional knowledge and skills on personal factors.

2. Structure of the mental portrait

Among the important factors that influence the learning process of a student are a combination of individual psychophysiological features of a person. To identify them, popular psychological methods of personality analysis were used:

All 6 tests contained more than 200 tasks, which significantly complicated the presentation of tests and increased the testing time with a large number of interviewed students. In order to speed up the process of personality testing, a system was developed to automate the interviewing of students and the processing of responses to psychological tests [8].

3. Building and researching a neural network model

3.1 Designing a Model

The amount of residual knowledge depends on the student's mentality and some other factors described in the previous section. This connection is difficult to formalize, i.e. it is difficult to describe it mathematically. In such cases, as mentioned above, it is advisable to use a neural network, which will reveal the existing connection. The following objective information is available for teaching the neural network:

The neural network will be trained on the mental portraits of a group of students and their grades.

Research on the quality of neural network forecasting was based on mental portraits that did not take into creativity and some other factors. Input signals are the main mental characteristics of students, which are described in the previous section, and statistics of student attendance. Neural network input signals form vector X = (x1, x2, …, x6), which components are described in Table 1.

Table 1 – Input parameters of the neural network

Input signal name Signal code
Type of motivationx1
Type of temperamentx2
Intelligence quotient (IQ)x3
Social intelligencex4
Special abilitiesx5
Student attendancex6

The output neural network generates signals that determine the predicted examination score. In the model, the evaluation is encoded by a four-digit binary code (Table 2).

Table 2 – Binary representation of output signals

Examination score Binary code
20 0 0 1
30 0 1 0
40 1 0 0
51 0 0 0

3.2 Building a Model

The Deep Learning Toolbox for the MATLAB environment [10] was a toolkit for creating a neural network model.

The neural model of examination evaluation forecasting is based on the Feed forward neural network [9]. According to the researches [11], for the function approximation task, it is enough to use 2–3 layers to ensure the implementation of any nonlinear dependence between input and output. In the model described in this article, 2 neuronal layers were used.

3.3 Results

Prediction of students' progress was made on the educational set, consisting of mental portraits and examination scores of 60 students.

The results show that in the array of students with a grade of 3, due to the large number of training examples, the probability of accurate prediction at 100 restarts for training reaches the value of 0.9, which cannot be seen in other arrays. This experiment emphasizes the need to further seek to increase the amount of training set.

Conclusions

The task of predicting the performance of students by their mental portraits is set.

The prototype of the system of building a mental portrait of a student on the basis of psychophysiological testing of personality is developed.

On the basis of the results of psychometric assessment of students' personalities the educational set for the adjustment of the neuromodel is constructed. The executed researches have shown possibility of application of the neural network approach to the decision of a problem of prognosis of students' progress and a direction of the further work on improvement of quality of model.

When writing this essay, the master's work is not yet complete. Final completion: May 2020. The full text of the work and materials on the topic can be obtained from the author or his manager after the specified date.

References

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