scholarly journals Academic Decision Support System for Choosing Information Systems Sub Majors Programs using Decision Tree Algorithm

2019 ◽  
Vol 5 (1) ◽  
pp. 57
Author(s):  
Cut Fiarni ◽  
Evasaria M. Sipayung ◽  
Prischilia B.T. Tumundo
Keyword(s):  
Data Mining ◽  
Factor Analysis ◽  
Decision Support ◽  
Decision Tree ◽  
Decision Support System ◽  
Support System ◽  
Educational Data Mining ◽  
Educational Process ◽  
Student Interest ◽  
The Impact

Background: Educational data mining is an emerging trend, especially in today Big Data Era. Numerous method and technique already been implemented in order  to improve its process to gain better understanding of the educational process and to extract knowledge from various related data, but the implementation of these methods into Decision support system (DSS) application still limited, especially regarding help to choose university sub majors .Objective: To design an academic decision support system (DSS) by adopting Theory of Reasoned Action (TRA) concept and using Data Mining as a factor analytic apporach to extract rules for its knowledge model.Methods: We implemented factor analysis method and decision tree method  of C.45 to produce rules of the impact course of the sub- majors and the job interest as the basic rules of the DSS.Results: The proposed academic decision support system able to give sub majors recommendations in accordance with student interest and competence, with 79.03% of precision and 61.11% of recall. Moreover, the system also has a dashboard feature that shows the information about the statistic of students in each sub majors.Conclusion: C.45 algorithm and factor analysis are suitable to build a knowledge model for Academic Decision Support System for Choosing Information System Sub Majors Bachelor Programs. This system could also help the academic adviser on monitoring and make decision accordance with that academic information

scholarly journals Development of Prediction Models for the Dengue Survivability Prediction: An Integration of Data Mining and Decision Support System

2019 ◽  
Vol 8 (10) ◽  
pp. 2199-2205
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Decision Tree ◽  
Decision Support System ◽  
Support System ◽  
Prediction Models ◽  
Viral Disease ◽  
Rule Based ◽  
Medical Practitioners ◽  
Tree Models

Dengue is a viral disease that has been feared by people globally. Due to its rapid prevalence and increasing threat, this study explored on the use of data mining techniques together with decision support system to develop prediction models of dengue survivability. This study was focused on three important points namely: identify significant predictor attributes to dengue survivability prediction, development of a rule-based and decision tree models for dengue survivability prediction, and the development of a dengue survivability platform for prediction purposes. The developed rule-based and decision tree models were compared according to accuracy and they underwent the 10-fold cross validation procedure and were integrated in the system to provide a platform to predict the survivability of a patient given the input medical data using a client-server configuration via the Internet. The result of the prediction for the dengue survivability may be used as an intervention by medical practitioners in the general management of dengue cases.

Research of data mining and web technology in university discipline construction decision support system based on MVC model

2019 ◽  
Vol 38 (3) ◽  
pp. 610-624
Author(s):  
Zhaokun Huang ◽  
Yufang Liang
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Decision Tree ◽  
Decision Support System ◽  
Support System ◽  
Colleges And Universities ◽  
Bayesian Theory ◽  
Data Mining Algorithm ◽  
Content Type ◽  
Mvc Model

Purpose Taking the discipline construction in colleges and universities as the application background, based on the research on data mining technology and decision support system technology, the data generated by university management information system are effectively utilized. The paper aims to discuss these issues. Design/methodology/approach Based on the Beijing Key Discipline Information Platform as the data source, the decision tree algorithm of data mining is studied. On the basis of decision tree C4.5, the Bayesian theory is applied to the post-pruning operation of the decision tree. Findings A decision tree post-pruning algorithm based on the Bayesian theory is studied and put forward in order to simplify the decision tree, which improves the generalization ability of the whole algorithm. Finally, the algorithm is used to build the prediction model of key disciplines. Combined with the decision support system architecture, data warehouse and the data mining algorithm constructed by university discipline, based on J2EE standard enterprise system specification, MVC model is applied. Moreover, a prototype system of decision support system for discipline construction in colleges and universities with browser/server (B/S) structure is completed and implemented. Originality/value A decision tree post-pruning algorithm based on the Bayesian theory is studied and put forward in order to simplify the decision tree, which improves the generalization ability of the whole algorithm. Finally, the algorithm is used to build the prediction model of key disciplines. Combined with the decision support system architecture, data warehouse and the data mining algorithm constructed by university discipline, based on J2EE standard enterprise system specification, MVC model is applied. Moreover, a prototype system of decision support system for discipline construction in colleges and universities with B/S structure is completed and implemented.

scholarly journals Model of decision support system in educational process of a university on the basis of learning analytics

2020 ◽  
pp. 42-52
Author(s):  
Alexander Alexandrovich Buldaev ◽  
Larisa Vladimirovna Naykhanova ◽  
Inga Sergeevna Evdokimova
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Decision Support System ◽  
Conceptual Model ◽  
Support System ◽  
Learning Analytics ◽  
Educational Institution ◽  
Educational Environment ◽  
Educational Process ◽  
Digital Information

In recent decades, the potential of analytics and data mining – the methodologies that extract valuable information from big data, transformed multiple fields of scientific research. Analytics has become a trend. With regards to education, these methodologies are called the learning analytics (LA) and educational data mining (EDM). Latterly, the use of learning analytics has proliferated due to four main factors: a significant increase in data quantity, improved data formats, achievements in the area of computer science, and higher complexity of available analytical tools. This article is dedicated to the description of building the model of decision support system (DSS) of a university based on educational data acquired from digital information and educational environment. The subject of this research is the development of DSS with application of learning analytics methods. The article provides a conceptual model of decision-making system in the educational process, as well as a conceptual model of the components of DSS component – forecasting subsystem. The peculiarity of forecasting subsystem model implies usage of learning analytics methods with regards to data sets of a higher educational institution, which contain the results of work of the digital information and educational environment, and include the characteristics of student activity. The main results of the conducted research is the examined and selected methods of clusterization and classification (KNN), the testing of which demonstrated palatable results. The author examined various methods of clusterization, among which k-prototypes method showed best results. The conclusion is made on favorable potential of application of the methods of learning analytics in Russian universities.

scholarly journals Decision Support System for an Art University

2021 ◽  
Vol 1 (2) ◽  
pp. 8-12
Author(s):  
Svetlana E. Vecherskaya
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Special Needs ◽  
Support System ◽  
Learning Process ◽  
Performance Indicators ◽  
Educational Process ◽  
Learning Plan ◽  
Specific Performance ◽  
Talented Students

A prototype of an automated decision support system for creative universities has been developed, which will allow assessing the achievements particularly talented students and identifying the needs in the learning process in order to help organize the educational process in accordance with identified capabilities. Use of a decision support system based on the Bayesian classifier is suggested which will allow to evaluate factors contributing to the progress in teaching students particular techniques, and in perspective to assess the possible resources that will be required to make changes to the learning. The list of specific performance indicators is given. The system should contribute to the formation of the learning plan, taking into account the capabilities of both a group art workshop as a whole, and special needs of an individual to develop, if necessary an individual approach.

scholarly journals Methodological Approaches to Regional Socio-Economic System Management

2020 ◽  
Vol 23 (6) ◽  
pp. 148-160
Author(s):  
E. A. Averchenkova
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Economic System ◽  
Support System ◽  
Feedback System ◽  
Management Theory ◽  
Practical Implementation ◽  
System Management ◽  
Management Decisions ◽  
The Impact

Purpose of research. This paper is a description of the methodology for regional socio-economic system management based on the principles and concepts of management theory. Methods. A methodology for regional socio-economic system managing has been developed, taking into account the impact of National projects and the influence of the external environment. The methodology consists of six stages and fourteen techniques that allow describing the regional socio-economic system management in terms and tools of the management theory: the region itself is considered as an object of management experiencing a controlling action formed under some affecting influence. The methodology also assumes the formalization of a negative feedback system and a control system in the developed model of regional socio-economic system management. Results. The methodology of managing the regional socio-economic system can be used in the management process. Those who make management decisions at the regional level usually rely on their own professional skills, past experience, and intuition. However, the heuristic approach to regional management can be extended by the capabilities of the developed methodology, the practical implementation of which can be presented as a decision support system. This will allow regional governments to improve the effectiveness of management decisions based on monitoring the state of socio-economic systems. Conclusion. The methodology for managing the regional socio-economic system provides a complete management cycle: from the formalization of basic concepts to the description of the control and feedback system. The information implementation of the methodology is presented in the form of an automated product – a decision support system - that can be used in the formation of an automated workplace for civil servants. 

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