Review

Learning Analytics and Potential Usage Areas in Education

Volume: 6 Number: 2 July 2, 2021
EN

Learning Analytics and Potential Usage Areas in Education

Abstract

The purpose of this study is to define learning analytics, to introduce concepts related to learning analytics and to introduce potential study topics related to learning analytics. Today’s education model has changed with evolving social and economic conditions over time. This change in education has created such new situations as individualized learning, determination of student behavior and the use of alternative assessment tools. One of the learning tools that can be used is to learning analytics. Learning analytics is defined as measuring, collecting and reporting data related to learners and learning environments to understand and improve learning and the surrounding environment. The use of learning analytics creates opportunities for individualized learning, to determine the student behaviors associated with success by examining the student behaviors affecting success, it serves as an alternative assessment tool. The main subject of the learning analytics is to obtain meaningful results from the virtual learning environments to improve student outcomes in online learning environments.

Keywords

References

  1. Agudo-Peregrina, Á. F., Iglesias-Pradas, S., Conde-González, M. Á., & Hernández-García, Á. (2014). Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning. Computers in Human Behavior, 31, 542-550.
  2. Aldowah, H., Al-Samarraie, H., & Fauzy, W. M. (2019). Educational data mining and learning analytics for 21st century higher education: A review and synthesis. Telematics and Informatics, 37, 13-49. doi:10.1016/j.tele.2019.01.007
  3. AoIR, A. o. I. R. (2012). Ethical Decision-Making and Internet Research:Recommendations from the AoIR Ethics Working Committee (Version 2.0). Retrieved from https://aoir.org/reports/ethics2.pdf
  4. Baghaei, N., Mitrovic, A., & Irwin, W. (2007). Supporting collaborative learning and problem-solving in a constraint-based CSCL environment for UML class diagrams. International Journal of Computer-Supported Collaborative Learning, 2(2-3), 159-190.
  5. Baker, R. S., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. JEDM| Journal of Educational Data Mining, 1(1), 3-17.
  6. Bienkowski, M., Feng, M., & Means, B. (2012). Enhancing teaching and learning through educational data mining and learning analytics: An issue brief. US Department of Education, Office of Educational Technology, 1, 1-57.
  7. Blikstein, P., & Worsley, M. (2016). Multimodal learning analytics and education data mining: Using computational technologies to measure complex learning tasks. Journal of Learning Analytics, 3(2), 220-238.
  8. Bloom, B. S. (1968). Learning for Mastery. Instruction and Curriculum. Regional Education Laboratory for the Carolinas and Virginia, Topical Papers and Reprints, Number 1. Evaluation comment, 1(2), n2.

Details

Primary Language

English

Subjects

Other Fields of Education

Journal Section

Review

Publication Date

July 2, 2021

Submission Date

August 8, 2020

Acceptance Date

January 19, 2021

Published in Issue

Year 1970 Volume: 6 Number: 2

APA
Yılmaz, F., & Çakır, H. (2021). Learning Analytics and Potential Usage Areas in Education. Journal of Learning and Teaching in Digital Age, 6(2), 81-89. https://izlik.org/JA78RT24JM

Journal of Learning and Teaching in Digital Age 2023. This is an Open Access journal distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. 19195

Journal of Learning and Teaching in Digital Age. Open Access Journal, 2023. ISSN:2458-8350