all AI news
Subgroup Discovery in MOOCs: A Big Data Application for Describing Different Types of Learners
March 12, 2024, 4:42 a.m. | J. M. Luna, H. M. Fardoun, F. Padillo, C. Romero, S. Ventura
cs.LG updates on arXiv.org arxiv.org
Abstract: The aim of this paper is to categorize and describe different types of learners in massive open online courses (MOOCs) by means of a subgroup discovery approach based on MapReduce. The final objective is to discover IF-THEN rules that appear in different MOOCs. The proposed subgroup discovery approach, which is an extension of the well-known FP-Growth algorithm, considers emerging parallel methodologies like MapReduce to be able to cope with extremely large datasets. As an additional …
abstract aim application arxiv big big data courses cs.cy cs.db cs.lg data discovery mapreduce massive paper rules type types
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne
Senior Machine Learning Engineer (MLOps)
@ Promaton | Remote, Europe
Senior Principal, Product Strategy Operations, Cloud Data Analytics
@ Google | Sunnyvale, CA, USA; Austin, TX, USA
Data Scientist - HR BU
@ ServiceNow | Hyderabad, India