scholarly journals Extract hidden patterns in students' academic information to improve the curriculum by using data mining

2021 ◽  
Author(s):  
Saeide Amerioon ◽  
Mohammad Mehdi Hosseini ◽  
Mahshid Moradi
Keyword(s):  
Data Mining ◽  
Academic Success ◽  
Success Factors ◽  
Radial Basis Function Network ◽  
Educational Data Mining ◽  
Educational Background ◽  
Data Mining Approach ◽  
Academic Information ◽  
Using Data ◽  
Research Students

AbstractEducational data mining is an emerging exquisite field that has been successfully implemented in higher education. One of the best ways to increase the efficiency of this emerging phenomenon is to select efficient professors and effective teaching methods. This study is intended to show academic success factors to have better management in student curriculum, contextualizing the progress and to prevent unfavorable conditions for students. In this research, students of Shahrood University of Technology were studied. Initially, 3,765 samples of students' educational background were considered. Then, pre-processing was performed to make the data normalized. Next, several predictive models were developed using a supervised data mining approach. Next, five algorithms by the best result were selected. Comparing the results of algorithms applied to data, the two algorithms, radial basis function network and the Naïve Bayes with respectively value F-measure 0.929 and 0.942 showed more accurate results. Finally, the most effective feature was selected, the attributes ‘maximum semester’ and ‘the total number of units dropped’ were ranked an the most important, and the three attributes of ‘the basic courses average’, ‘the number of units of main courses’ and ‘the total average’, were placed in the next ranks.

A Data Mining Approach to Forming Generic Bills of Materials in Support of Variant Design Activities

2004 ◽  
Vol 4 (4) ◽  
pp. 316-328 ◽  
Author(s):  
Carol J. Romanowski , ◽  
Rakesh Nagi
Keyword(s):  
Data Mining ◽  
Product Family ◽  
Research Direction ◽  
Design Constraints ◽  
Variant Design ◽  
Data Mining Approach ◽  
Design Activities ◽  
Using Data ◽  
New Research

In variant design, the proliferation of bills of materials makes it difficult for designers to find previous designs that would aid in completing a new design task. This research presents a novel, data mining approach to forming generic bills of materials (GBOMs), entities that represent the different variants in a product family and facilitate the search for similar designs and configuration of new variants. The technical difficulties include: (i) developing families or categories for products, assemblies, and component parts; (ii) generalizing purchased parts and quantifying their similarity; (iii) performing tree union; and (iv) establishing design constraints. These challenges are met through data mining methods such as text and tree mining, a new tree union procedure, and embodying the GBOM and design constraints in constrained XML. The paper concludes with a case study, using data from a manufacturer of nurse call devices, and identifies a new research direction for data mining motivated by the domains of engineering design and information.

scholarly journals ANALYSIS OF THESIS ADVISING PATTERN IN MASTER PROGRAM ON FACULTY OF AGRICULTURE IPB USING DATA MINING APPROACH

Edulib ◽  
2018 ◽  
Vol 8 (2) ◽  
pp. 194
Author(s):  
Lilis Syarifah ◽  
Imas Sukaesih Sitanggang ◽  
Pudji Muljono
Keyword(s):  
Data Mining ◽  
Upland Rice ◽  
Future Research ◽  
Rule Mining ◽  
Related Item ◽  
Text Documents ◽  
Master Program ◽  
R Language ◽  
Data Mining Approach ◽  
Using Data

The thesis is student study report which is accomplished as a requirement of graduation for Master program. Selecting study’s topic and advisors influence implementation of the study. Therefore, study’s topic is able to improve academic institution quality, however a large number of thesis documents on the repository cause difficulty to get information related to advisor’s expertness and the frequent or rare topic is former studied. Association rule mining can be used to mine information on the related item. This study aims to analyze advising patterns system in Master program on Agriculture based on supervisors and their topic research on metadata thesis of IPB repository and text documents of summary using data mining approach. The datas were collected from the repository of Bogor Agricultural University website and processed using R language programming. Pattern result of the reseach were that the most popular association on supervisor was occurred at support value of 0.00793 or equivalent to 7 theses and four popular topics were Botanical insecticide, Global warming, Upland Rice, and Land Use Change. The analysis result could be useful information to be reference or suggest future research or appropriate supervisor among agricultural.

scholarly journals Using Data Mining for Improving Education of University Students – A Survey

2018 ◽  
Vol 6 (9) ◽  
pp. 78-83 ◽  
Author(s):  
K. P. S. D. Kumarapathirana
Keyword(s):  
Data Mining ◽  
Academic Success ◽  
Student Retention ◽  
Student Performance ◽  
Poor Performance ◽  
Academic Behavior ◽  
System Logs ◽  
Using Data ◽  
Visualization Techniques ◽  
The University

Data mining combines machine learning, statistical and visualization techniques to discover and extract knowledge. Student retention is an indicator of academic performance and enrolment management of the university. Poor student retention could reflect badly on the university. Universities are facing the immense and quick growth of the volume of educational data stored in different types of databases and system logs. Moreover, the academic success of students is another major issue for the management in all professional institutes. So the early prediction to improve the student performance through counseling and extra coaching will help the management to take timely action for decrease the percentage of poor performance by the students. Data mining can be used to find relationships and patterns that exist but are hidden among the vast amount of educational data. This survey conducts a literature survey to identify data mining technologies to monitor student, analyze student academic behavior and provide a basis for efficient intervention strategies. The results can be used to develop a decision support system and help the authorities to timely actions on weak students.

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