NDLI: An overview of using academic analytics to predict and improve students' achievement: A proposed proactive intelligent intervention

Content Provider	IEEE Xplore Digital Library
Author	bin Mat, U. Buniyamin, N. Arsad, P.M. Kassim, R.
Copyright Year	2013
Description	Author affiliation: Fac. of Electr. Eng., Univ. Teknol. MARA, Shah Alam, Malaysia (bin Mat, U.; Buniyamin, N.; Arsad, P.M.; Kassim, R.)
Abstract	This paper presents a literature review on the use of a large array of data about students and courses that was collected by institutions and learning analytics to improve students success and retention. Academic analytics is getting notable attention, because it assists educational institutions in improving student achievement and success, increasing student retention, and reduce the load of liability and accountability. The purpose of this paper is to provide a brief overview of how academic analytics has been used in educational institutions, what tools are available, and how institution can predict student performance and achievement. In addition, the study will discuss its applications, goals, examples, and why instructors want to make use of academic analytics. Finally, this paper will propose an intelligent recommendation intervention to improve students' achievement that will be based on two outcomes; performance as measured by final grade, and students' information data such as attendance, prerequisite subject, English and Mathematics marks, and suggests the use of Artificial Neural Network and Decision Tree for predictive modeling.
Starting Page	126
Ending Page	130
File Size	208645
Page Count	5
File Format	PDF
e-ISBN	9781479923328
DOI	10.1109/ICEED.2013.6908316
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-04
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Educational data mining Neural Network Decision Tree Conferences Students achievement Predictive models Performance prediction Educational institutions Data mining Decision trees Engineering education Academic analytics
Content Type	Text
Resource Type	Article

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