The Components of Data Governance Framework for MOOC Providers in Indonesia

The COVID-19 pandemic had a universal and synchronous impact on all countries globally, leading to the implementation of the social distancing policy that mandated all activities take place within one's own house. One of the implementations is distance education. Since the pandemic, e-learning, which is the dominant use of information technology in the education sector, has gained widespread recognition and acceptance as a mainstream concept in contemporary society. An illustration of this is the growth of the Massive Open Online Course (MOOC), which has attracted the attention of both public and private universities, prompting them to adopt its implementation. The rise of MOOCs can be ascribed to the internet's ability to provide a more dynamic and flexible learning environment in comparison to traditional methods. The pandemic coincided with the establishment of digital campuses in schools and universities in recent decades. However, one important characteristic of these digital campuses is that they prioritize processes but overlook data and lack standards. Therefore, this study aims to identify important data governance components for MOOC providers in Indonesia from various past publications to construct a data governance framework. By examining the initial topic, the fundamental elements of data governance were ascertained. This study employed the Systematic Literature Review (SLR) methodology to address the research issue. The results derived from the SLR led to the initial derivation of six main components and 128 sub-components. Subsequently, interviews were carried out with 10 specialists representing 8 MOOC providers. The interview data were subsequently used to calculate the outcomes of the components using the fuzzy Delphi method. Based on statistical computation, six components, and 112 sub-components were deemed genuine and accepted by the eight MOOC providers in Indonesia. The subsequent phase of this study aims to construct a data governance system specifically tailored for MOOC providers in Indonesia.

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Publication Details

Journal
Journal of Computer Science
Published
2024-11-29
DOI
https://doi.org/10.3844/jcssp.2024.1636.1656
Citations
1
Primary Topic
Artificial Intelligence in Healthcare
Type
article
Field-Weighted Citation Impact
0.71

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The Components of Data Governance Framework for MOOC Providers in Indonesia

Harjanto Prabowo, Betty Purwandari, Yakob Utama Chandra, Ford Lumban Gaol
1 citations
Journal of Computer Science
Artificial Intelligence in Healthcare
0.71
article

The Components of Data Governance Framework for MOOC Providers in Indonesia

Harjanto Prabowo, Betty Purwandari, Yakob Utama Chandra, Ford Lumban Gaol
article en
1 citations

Abstract

The COVID-19 pandemic had a universal and synchronous impact on all countries globally, leading to the implementation of the social distancing policy that mandated all activities take place within one's own house. One of the implementations is distance education. Since the pandemic, e-learning, which is the dominant use of information technology in the education sector, has gained widespread recognition and acceptance as a mainstream concept in contemporary society. An illustration of this is the growth of the Massive Open Online Course (MOOC), which has attracted the attention of both public and private universities, prompting them to adopt its implementation. The rise of MOOCs can be ascribed to the internet's ability to provide a more dynamic and flexible learning environment in comparison to traditional methods. The pandemic coincided with the establishment of digital campuses in schools and universities in recent decades. However, one important characteristic of these digital campuses is that they prioritize processes but overlook data and lack standards. Therefore, this study aims to identify important data governance components for MOOC providers in Indonesia from various past publications to construct a data governance framework. By examining the initial topic, the fundamental elements of data governance were ascertained. This study employed the Systematic Literature Review (SLR) methodology to address the research issue. The results derived from the SLR led to the initial derivation of six main components and 128 sub-components. Subsequently, interviews were carried out with 10 specialists representing 8 MOOC providers. The interview data were subsequently used to calculate the outcomes of the components using the fuzzy Delphi method. Based on statistical computation, six components, and 112 sub-components were deemed genuine and accepted by the eight MOOC providers in Indonesia. The subsequent phase of this study aims to construct a data governance system specifically tailored for MOOC providers in Indonesia.

Journal of Computer ScienceVol. 20(12)
Binus University
Openalex Percentile: Top 20%
Artificial Intelligence in Healthcare
0.71
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