The COVID-19 viral outbreak and other technological developments in recent years have profoundly altered educational norms. The quantity of online course materials is expanding rapidly. The trend towards online education means that more and more school records are being converted to digital format.
However, the processing and analysing massive amounts of educational data to extract benefits for realising learning still need improvement. With the help of big data technology, educators can quickly analyse vast amounts of data.
Educational process mining refers to identifying how students learn through studies based on log data. In its simplest form, Education Data Mining (EDM) is an approach to education that uses data mining and analysis to boost educational outcomes.
Education data mining EDM is an area of education science that uses methods and tools from related fields like statistics and computer science. Therefore, a collaboration between several disciplines is essential.
“The implementation of the mining method seeks to discover, monitor, analyse, and improve actual procedures by extracting data derived from event logs obtained from information systems,” said Lia Sadita, a young expert from the Research Center for Data and Information Science Research (PRSDI) BRIN. ” Boosting the use of data for online learning, with data preserved digitally, we can process the data and use it again for education.”
Data and scientific inquiry are prioritised in many spheres of society, not only academia and government. During the webinar “The Role of Data Science in the Learning and Research Process, Budi Prawara, Head of the Electronics Research Organisation and Informatics of the National Research and Innovation Agency (BRIN) said that data processing and data analysis can help our activities in everyday life and can improve work performance and efficiency.
Harry Budi Santoso, who runs the Digital Library and Distance Learning Lab at the University of Indonesia’s Faculty of Communication Sciences said big data in education is linked to Learning Analytics (LA) and Educational Data Mining (EDM), both of which are expected to lead to the creation of new knowledge.
Appropriate resources are needed to examine massive data sets efficiently and attempts to share resources are necessary. In addition, we don’t employ this information as-is; instead, we must make a concerted, continual effort to gather enough information that suits our immediate and future needs.
The event concluded with a presentation on biorepository design for a fish metabolite database, a systematic literature review, and benchmarking methods. Another young expert researcher, Ira Maryati remarked, “This research is an implementation of database design,” where the data is crucial for ongoing study and education, especially in fisheries. Therefore, this study is an example of database design in action.
He employs a metabolite database for a systematic literature review and benchmarking technique on fish metabolites. It was carried out so that a fish metabolite database could be planned out. The metabolite database must be developed separately because the specific data cannot be combined with other data. However, this step is essential for making the database accessible for research and educational purposes.
Head of PRSDI BRIN Esa Prakarsa says this gathering has the potential to become a collaborative venue for research in the future and that the work presented here will provide us with all new perspectives and knowledge.
In an earlier article, OpenGov Asia reported that Natural language processing (NLP) and machine learning (ML) strategies are being utilised by the Indonesian Ministry of Communication and Informatics (Kemenkominfo) to combat false news on the internet. The tools were developed in collaboration with the National Research and Innovation Agency’s (BRIN) Collaboration for the Acceleration of Indonesian Artificial Intelligence Innovation (Korika).
