Data driven decision making in digital education : A case study from Finland and Russia
Khan, Arnob Islam (2019)
Diplomityö
Khan, Arnob Islam
2019
School of Engineering Science, Tuotantotalous
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2019072323186
https://urn.fi/URN:NBN:fi-fe2019072323186
Tiivistelmä
This Master’s thesis is contemplated as a part of CEPHEI project. One of the major goals of the project is to increase the digitalization the course contents of Industrial Innovation by standardization of its e-learning elements. The assessment of course design and students’ performance is one of the integral part of e-learning/digital course. However, the standards in learning assessment for digital courses remains vague. For teachers to find the proper evaluation method for online course has become a big challenge. It is crucial to determine the learning outcomes and check, whether the e-learning deliverable serve its purpose or not. Evaluation of eLearning course makes it possible to assess its quality and efficiency and, most importantly, to comprehend what modifications and improvements are needed.
The objective of the thesis is to evaluate a supervised learning based assessment methods for digital education. The method is evaluated based on the available data of “Systematic Creativity and TRIZ basics” course at Lappeenranta University of Technology, Finland and “Computer Science” course at Tomsk State University of Control Systems and Radioelectronics, Russia. This work is an attempt to illustrate the obtained results by the described evaluation methods and provide an initial speculation on the usability of learning analytics.
The objective of the thesis is to evaluate a supervised learning based assessment methods for digital education. The method is evaluated based on the available data of “Systematic Creativity and TRIZ basics” course at Lappeenranta University of Technology, Finland and “Computer Science” course at Tomsk State University of Control Systems and Radioelectronics, Russia. This work is an attempt to illustrate the obtained results by the described evaluation methods and provide an initial speculation on the usability of learning analytics.