Data Preparation in Predictive Learning Analytics (PLA) for Student Dropout
Nurmalitasari1, Zalizah Awang Long2, Mohammad Faizuddin Mohd Noor3
1Nurmalitasari, Malaysian Institute Information Technology, Universiti Kuala Lumpur, Kuala Lumpur, Malaysia.
2Zalizah Awang Long, Malaysian Institute Information Technology, Universiti Kuala Lumpur, Kuala Lumpur, Malaysia.
3Mohammad Faizuddin Mohd Noor, Malaysian Institute Information Technology, Universiti Kuala Lumpur, Kuala Lumpur, Malaysia.
Manuscript received on 09 January 2020 | Revised Manuscript received on 05 February 2020 | Manuscript Published on 20 February 2020 | PP: 116-120 | Volume-9 Issue-3S January 2020 | Retrieval Number: C10270193S20/2020©BEIESP | DOI: 10.35940/ijitee.C1027.0193S20
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Predictive learning analytics (PLA) are the current trend to support learning processes. One of the main issues in education particularly in higher education (HE) is high numbers of dropout. There are little evidences being identified the variables contributing toward dropout during study period. The dropout are the major challenges of educational institutions as it concerns in the education cost and policy-making communities. The paper presents a data preparation process for student dropout in Duta Bangsa University. The number of students dropout in Duta Bangsa University are in high alarm for both management and also educator in Duta Bangsa. Preventing educational dropout are the major challenges to Duta Bangsa University. Data preparation is an important step in PLA processes, the main objective is to reduce noise and increase the accuracy and consistency of data before PLA executed. The data preparation on this paper consist of four steps: (1) Data Cleaning, (2) Data Integration, (3) Data Reduction, and (4) Data Transformation. The results of this study are accurate and consistent historical dropout data Duta Bangsa University. Furthermore, this paper highlights open challenges for future research in the area of PLA student dropout.
Keywords: Data Preparation, Dropout, PLA, Duta Bangsa University.
Scope of the Article: Data Analytic