Discovering vital patterns from UST students data by applying data mining techniques

Asma A Al-shargabi; Ali N Nusari

doi:10.1109/ICCAE.2010.5451653

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Conference proceeding

Discovering vital patterns from UST students data by applying data mining techniques

Asma A Al-shargabi and Ali N Nusari

2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE), Vol.2, pp.547-551

02/2010

DOI: https://doi.org/10.1109/ICCAE.2010.5451653

Abstract

Association rules

Clustering

Clustering algorithms

Computer science

Computer science education

Data engineering

Data mining

Data Mining (DM)

Decision trees

Demography

Educational institutions

Knowledge Discovery

Predictive models

This paper presents an applied study in data mining and knowledge discovery. It aims at discovering patterns within historical students' academic and financial data at UST (University of Science and Technology) from the year 1993 to 2005 in order to contribute improving academic performance at UST. Results show that these rules concentrate on three main issues, students' academic achievements (successes and failures), students' drop out, and students' financial behavior. Clustering (by K-means algorithm), association rules (by Apriori algorithm) and decision trees by (J48 and Id3 algorithms) techniques have been used to build the data model. Results have been discussed and analyzed comprehensively and then well evaluated by experts in terms of some criteria such as validity, reality, utility, and originality. In addition, practical evaluation using SQL queries have been applied to test the accuracy of produced model (rules).

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Title: Discovering vital patterns from UST students data by applying data mining techniques
Creators - without role: Asma A Al-shargabi - Sana'a University
Ali N Nusari - Sana'a University
Publication Details: 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE), Vol.2, pp.547-551
Publisher: IEEE
Identifiers: 9928612108331
Academic Unit: Qassim University
Language: English
Resource Type: Conference proceeding