LEARNING ANALYTICS AND EDUCATIONAL DATA MINING IN INDONESIA: A LITERATURE REVIEW
University of Michigan (UNITED STATES)
About this paper:
Conference name: 16th annual International Conference of Education, Research and Innovation
Dates: 13-15 November, 2023
Location: Seville, Spain
Abstract:
Indonesia’s education technology state landscape presents unprecedented potential for research in learning analytics (LA) and educational data mining (EDM). The recent growth of the technology industry, coupled with the impact of the COVID-19 pandemic, has driven advancements in education technology innovation. Notably, major education technology startups have flourished, providing supplementary educational materials and college entrance exam preparation, serving millions of students nationally. Additionally, GovTech Edu, a team working in collaboration with the Ministry of Education, Culture, Research, and Technology of The Republic of Indonesia develops education technology solutions for multiple stakeholders within the national education system, including students and teachers. Consequently, this setting offers an infrastructure capable of collecting vast amounts of data. This paper conducts a review of 23 research studies on LA and EDM in Indonesia to gauge the extent to which this opportunity has been utilized. Consistent with global trends, the majority of these studies focus on higher education, primarily on predicting academic performance. There is a limited number of studies conducted within K-12 settings; however, they predominantly utilize administrative or publicly available data, and miss out on leveraging this data from education technology products. This paper aims to address the critical gap and opportunity for LA and EDM research in K-12 settings, specifically targeting one of the most pressing issues in the Indonesian education system–the geographic disparity in education quality. In general, LA studies serve various purposes, including understanding, explaining, predicting, and optimizing learning. This newly available data holds immense value as it can complement existing research conducted outside LA and EDM, in understanding the education disparities across different regions in Indonesia and formulating the strategies to tackle them.Keywords:
Learning analytics, educational data mining, education technology, Indonesia.