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All Journal ELINVO (Electronics, Informatics, and Vocational Education)
Ahmad Chafid Alwi
Faculty of Economics and Business, Universitas Negeri Yogyakarta
Computer Science & IT Education Electrical & Electronics Engineering

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Development of Adaptive MOOCs to Support Personalized Learning: Mixed Method Analysis P. Priyanto; Ahmad Chafid Alwi; Siti Irene Astuti Dwiningrum; Amrih Setyo Raharjo; Akhsin Nurlayli
Elinvo (Electronics, Informatics, and Vocational Education) Vol 7, No 2 (2022): November 2022
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (636.42 KB) | DOI: 10.21831/elinvo.v7i2.55481

Abstract

This study aims to explain the development of adaptive MOOCs that support personalized learning. This study was designed with a mixed method design of sequential explanatory type at the association level. Quantitative analysis used confirmatory factor analysis (CFA) (n = 110) and was deepened with qualitative analysis of the Miles and Huberman model. Quantitatively measured domains include accessibility, learning curriculum, competence, motivation, satisfaction, efficacy, and self-study. The domain was used as a reference for qualitative data mining through focus group discussions (FGD) involving lecturers and doctoral students (n = 25). The analysis results show that the curriculum domain and one of the motivational indicators should be removed because it did not meet the requirements after bootstrapping. The second running algorithm showed all valid and reliable variables. Some domains that significantly affect MOOC user satisfaction are efficacy, competence, and motivation. R square results showed 37% influenced by motivation, accessibility, efficacy, and self-study, and the rest influenced by other variables. In the qualitative analysis, 19 subcodes were found that were included in the three main codes. In conclusion, there is new information in the accessibility domain that expands quantitative data, including information on MOOCs, marketing traps, regulation, and dropouts. Meanwhile, what strengthens and deepens quantitative data is found in the information on metacognitive and personalized coding that strengthens the domain of efficiency, the domain of competence, which is strengthened by content, mentoring collaboration, and motivation reinforced by coding the user's motivations and goals.
Co-Authors Akhsin Nurlayli Amrih Setyo Raharjo P. Priyanto Siti Irene Astuti Dwiningrum