Search

In this paper is shown the application of neural networks in order to predict academic marks that will be obtained for the students in the subjects of Data Structures I and II, included both in the Informatics Engineering curricula at Higher Polytechnic Institute José Antonio Echeverría in the Republic of Cuba. The main motivation for the present work is justified because selected subjects have a high level of complexity, demanding from the student to be rigorous and a permanent dedication. As a consequence the academic results obtained at the present time are not satisfactory. To reach the goal mentioned above a software based on MATLAB tool was developed and the marks obtained previously by students in some subjects and others data of interest were used. Two neural networks were employed, both with the same architecture, but each one trained with the specific data of each subject (Data Structures I and II). A group of experiments was carried out to contrast the behavior of the neural networks regarding some specific statistics in the data of the sample. An overall effectiveness in prediction superior to 78% for the case of the first subject was achieved, while for the second one effectiveness superior to 75% was reached. [ABSTRACT FROM AUTHOR]

Every day organizations have more information because their systems produce a large amount of daily operations which are stored in transactional databases. In order to analyze this historical information, an interesting alternative is to implement a Data Warehouse. In the other hand, Data Warehouses are not able to perform predictive analysis for themselves, but machine learning techniques can be used to classify, grouping and predict historical information in order to improve the quality of analysis. This paper depicts architecture of a Data Warehouse useful to perform an analysis of students' academic performance. The Data Warehouse is used as input of a Neural Network in order to analyze historical information and forecast. The results show the viability of using Data Warehouse for academic performance analysis and the feasibility of predicting the number of approved courses for students using only their own historical information. [ABSTRACT FROM AUTHOR]