Quality Assurance in Distance Education through Data Mining

Mustafa Tuncay Sarıtaş, Caner Börekci, Samet Demirel
547 269

Abstract


Learning Management Systems (LMS) are software applications that facilitate the management and monitoring of online teaching courses and/or training programs, workshops, webinars, forums, and other similar learning activities. The LMS provides learning and teaching benefits and possibilities for synchronous, asynchronous, and hybrid training. For instance, learning management systems (LMS) can store a wide variety of large-scale educational data. The stored data can be analyzed by employing educational data mining methods. Educational data mining (EDM) is a new discipline that deals with methods for exploring the unique and large-scale data generated by digital platforms to better understand students’ learning progress and the learning environment itself. In this study, the data stored in the LMS used by Balıkesir University during the fall semester of the 2021–2022 academic year were analyzed by using educational data mining methods in order to reveal the current status of distance education activities and make suggestions to improve the quality.

Keywords


Distance education, Data mining, Learning analytics, Quality improvement

Full Text:

PDF

References


Sarıtaş, M. T., Börekci, C., & Demirel, S. (2022). Quality assurance in distance education through data mining. International Journal of Technology in Education and Science (IJTES), 6(3), 443-457. https://doi.org/10.46328/ijtes.396




DOI: https://doi.org/10.46328/ijtes.396

Refbacks

  • There are currently no refbacks.


Copyright (c) 2022 International Journal of Technology in Education and Science

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Abstracting/Indexing

                     

                    

  

 

International Journal of Technology in Education & Science (IJTES)-ISSN: 2651-5369


 
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.