Data Mining K-Means Algorithm for Performance Analysis
TL;DR: In this increasingly competitive world of technology, there needs to be efforts to improve the quality of educational institutions as mentioned in this paper , however, it is often constrained by data processing that runs less than maximum and is less explored further.
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Abstract: In this increasingly competitive world of technology, there needs to be efforts to improve the quality of educational institutions. However, it is often constrained by data processing that runs less than maximum and is less explored further. Therefore, Educational Data Mining as one of the clumps in data mining is tasked with uncovering student characteristics and behaviors hidden in a form of data that needs to be analyzed first. Based on these techniques, efforts to create a quality academic life by reducing the risk of student failure can be realized through a pedagogical plan of learning in the future. Through the following techniques, efforts to find association rules, group and classify data comparisons that have the best ability and value in the data mining process can be realized by implementing various tools such as WEKA, R, and Orange.
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References
Educational data mining and learning analytics for 21st century higher education: A review and synthesis
TL;DR: Applying EDM and LA in higher education can be useful in developing a student-focused strategy and providing the required tools that institutions will be able to use for the purposes of continuous improvement.
496
Educational data mining: Predictive analysis of academic performance of public school students in the capital of Brazil
Eduardo Fernandes,Maristela Holanda,Marcio de Carvalho Victorino,Vinicius R. P. Borges,Rommel N. Carvalho,Gustavo Cordeiro Galvão Van Erven +5 more
TL;DR: In this article, a predictive analysis of the academic performance of students in public schools of the Federal District of Brazil during the school terms of 2015 and 2016 was presented, where two datasets were obtained: the first dataset contains variables obtained prior to the start of the school year and the second included academic variables collected two months after the semester began.
291
Challenges for the Future of Educational Data Mining: The Baker Learning Analytics Prizes
Ryan S. Baker
- 16 Jun 2019
TL;DR: A vision for some directions the field should go is presented: towards greater interpretability, generalizability, transferability, applicability, and with clearer evidence for effectiveness: the Baker Learning Analytics Prizes (BLAP).