Link Analysis in Employment Data Set to Improve Learning Outcomes for IT Programmes


  • Kadhim B. Swadi Al-Janabi Department of Mathematics, Faculty of Computer Science and Mathematics, University of Kufa



Data Mining, Classification, Link Analysis


This paper presents an approach for analyzing data of the Information Technology graduate according to the employability knowledge areas in order to predict feedback recommendations to
improve the IT programmes teaching and learning resources and processes towards,the improvement of the programme learning outcomes. The approach is based on features (knowledge areas) extracted
from logged data for employment and university graduates. Link analysis is an efficient approach to study the correlation and relationships between different attributes that highly affect jobs in IT
market, including different skills areas in both the market and the programme curriculum, and it gives good weighted evaluation for these knowledge areas. The link analysis shows great relationship and
associations between these attributes (Student Performance in Bachelor degree, analytical and development skills, Programming skills (Java, C++, C#, etc), practical skills communication skills, and
training and certificates) and the market demands. Data set from IT market and university records is used to create and test the model. WEKA was used as a software for mining tasks.


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IEEE/ACM Computing Curricula,

Information Technology, Volume, Version:

October 2005.

Quality Assurance Agency for Higher


Jiawei Han and Micheline Kamber

Data Mining: Concepts and Techniques

nd ed., Morgan Kaufmann, 2006.

AlJanabi Kadhim, Mining Employment

Data Set to Improve Teachning and

Learning Resources and Processes in IT

Programmes, International Conference for

Applied Sciences, Kufa University, 2008.

B. Barros and M. F. Verdejo, Analysing

student interaction processes in order to

improve collaboration: the degree approach ,

International Journal of Artificial Intelligence in

Education, 11, 221 241, (2000).

Elena Gaudioso and Luis Talavera , Mining

Student Data To Characterize Similar Behavior

Groups In Unstructured Collaboration Spaces,

ECAI04 workshop, 2004.

Rahel Bekele Wolfgang Menzel A Bayesian

Approach To Predict Performance Of A Student.

Artificial Intelligence and Applications ~AIA

~, Innsbruck, Austria

Kortemeyer G.,.Minaei-Bidgoli, B., Punch,

W.F., Association Analysis for an Online

Education System , IEEE International

Conference on Information Reuse and

Integration (IRI-2004), Las Vegas, Nevada, Nov




How to Cite

Al-Janabi, K. B. S. (2010). Link Analysis in Employment Data Set to Improve Learning Outcomes for IT Programmes . Journal of Kufa for Mathematics and Computer, 1(2), 128–135.

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