English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Educational Data Mining (EDM) is a developing discipline, concerned withexpanding the classical Data Mining (DM) methods and developing new methods fordiscovering the data that originate from educational systems. Studentattendance in higher education has always been dealt with in a classical way,educators rely on counting the occurrence of attendance or absence buildingtheir knowledge about students as well as modules based on this count. Thismethod is neither credible nor does it necessarily provide a real indication ofa student performance. This study tries to formulate the extracted knowledge ina way that guarantees achieving accurate and credible results. Studentattendance data, gathered from the educational system, were first cleaned inorder to remove any randomness and noise, then various attributes were studiedso as to highlight the most significant ones that affect the real attendance ofstudents. The next step was to derive an equation that measures the StudentAttendance Credibility (SAC) considering the attributes chosen in the previousstep. The reliability of the newly developed measure was then evaluated inorder to examine its consistency. Finally, the J48 DM classification techniquewas utilized in order to classify modules based on the strength of their SACvalues. Results of this study were promising, and credibility values achievedusing the newly derived formula gave accurate, credible, and real indicators ofstudent attendance, as well as accurate classification of modules based on thecredibility of student attendance on those modules.

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