Research Methodologies for Student Performance Evaluation Using Educational Analytics Tools and Approaches
Sri Laxmi Kuna1, A.V. Krishna Prasad2
1Sri Laxmi Kuna, Research Scholar, Assistant Professor, KLU & Sreenidhi Institute of Science and Technology College, (Telangana), India.
2Dr. A.V. Krishna Prasad, Professor, KLU & MVSR, College, (Telangana), India.
Manuscript received on 02 June 2019 | Revised Manuscript received on 27 June 2019 | Manuscript Published on 04 July 2019 | PP: 68-74 | Volume-8 Issue-1S4 June 2019 | Retrieval Number: A10130681S419/2019©BEIESP
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© 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: Educational Data Learning Analytics (EDLA) are about learning and is used to study and analyzing data from academic databases, Which is focuses on faculty teaching and student learning process, developing new tools and algorithms for discovering data patterns to improve student success. Recently Educational institutions are also using Learning analytics for the decision making to improve the student learning process. We can also develop new techniques from statistics for the analysis of educational data. It is also tests the learning theories and improves the teaching – learning process. One more important feature of EDLA is that it can focus on university level, institutional level, classroom level, session level and individual student answer level also. Educational Data Learning Analytics techniques are used for the benefit of all the stakeholders in the educational system such as student, faculty, engineering institutions and engineering education universities, which will help study, predict and improve a students’ academic performance. In this paper we define Educational Data Learning Analytics and discussed about existing tools, how the stack holders can make use of it for student success.
Keywords: Educational Data Learning Analytics (EDLA), Classification, Clustering, Outlier analysis, Association Rule Mining, Tools and Stack Holders.
Scope of the Article: Software Engineering Methodologies