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|Title: ||Measuring the credibility of student attendance data in Higher Education for data mining|
|Authors: ||Dawson, Christian W.|
|Issue Date: ||2016|
|Citation: ||DAWSON, C.W., ALSUWAIKET, M. and BATMAZ, F., 2016. Measuring the credibility of student attendance data in Higher Education for data mining. International Journal of Information and Education Technology, [in press]|
|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. It aims to use those methods to achieve a logical understanding of students, and the educational environment they should have for better learning.
These data are characterized by their large size and randomness and this can make it difficult for educators to extract knowledge from these data. Additionally, knowledge extracted from data by means of counting the occurrence of certain events is not always reliable, since the counting process sometimes does not take into consideration other factors and parameters that could affect the extracted knowledge.
As a case example of the above problem, 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 explores the above problem and 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.|
|Description: ||This paper is closed access until publication. It was presented at the 5th International Conference on Knowledge and Education Technology (ICKET 2016) and subsequently published in the International Journal of Information and Education Technology.|
|Version: ||Accepted for publication|
|Publisher Link: ||http://www.ijiet.org/|
|Appears in Collections:||Closed Access (Computer Science)|
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