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General Information
    • ISSN: 2010-3689
    • Frequency: Bimonthly (2011-2014); Monthly (Since 2015)
    • DOI: 10.18178/IJIET
    • Editor-in-Chief: Prof. Dr. Steve Thatcher
    • Executive Editor: Ms. Nancy Y. Liu
    • Abstracting/ Indexing: EI (INSPEC, IET), Electronic Journals Library, Google Scholar, Crossref and ProQuest
    • E-mail: ijiet@ejournal.net
Editor-in-chief
Prof. Dr. Steve Thatcher
University of South Australia, Australia
It is my honor to be the editor-in-chief of IJIET. The journal publishes good papers which focous on the advanced researches in the field of information and education technology. Hopefully, IJIET will become a recognized journal among the scholars in the filed of information and education technology.
IJIET 2018 Vol.8(2): 121-127 ISSN: 2010-3689
doi: 10.18178/ijiet.2018.8.2.1020

Measuring the Credibility of Student Attendance Data in Higher Education for Data Mining

Mohammed Alsuwaiket, Christian Dawson, and Firat Batmaz
Abstract—Educational Data Mining (EDM) is a developing discipline, concerned with expanding the classical Data Mining (DM) methods and developing new methods for discovering the data that originate from educational systems.
Student attendance in higher education has always been dealt with in a classical way, i.e. educators rely on counting the occurrence of attendance or absence building their knowledge about students as well as modules based on this count. This method is neither credible nor does it necessarily provide a real indication of a student’s performance.
This study tries to formulate the extracted knowledge in a way that guarantees achieving accurate and credible results. Student attendance data, gathered from the educational system, were first cleaned in order to remove any randomness and noise, then various attributes were studied so as to highlight the most significant ones that affect the real attendance of students. The next step was to derive an equation that measures the Student Attendance’s Credibility (SAC) considering the attributes chosen in the previous step. The reliability of the newly developed measure was then evaluated in order to examine its consistency. Finally, the J48 DM classification technique was utilized in order to classify modules based on the strength of their SAC values.
Results of this study were promising, and credibility values achieved using the newly derived formula gave accurate, credible, and real indicators of student attendance, as well as accurate classification of modules based on the credibility of student attendance on those modules.

Index Terms—EDM, credibility, reliability, student attendance, higher education.

The authors are with the Loughborough University, Epinal Way, Loughborough Leicestershire, UK (e-mail: m.alsuwaiket@lboro.ac.uk, c.w.dawson1@lboro.ac.uk, f.batmaz1@lboro.ac.uk).

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Cite: Mohammed Alsuwaiket, Christian Dawson, and Firat Batmaz, "Measuring the Credibility of Student Attendance Data in Higher Education for Data Mining," International Journal of Information and Education Technology vol. 8, no. 2, pp. 121-127, 2018.

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