Continuous clustering in big data learning analytics

Kannan Govindarajan, Thamarai Selvi Somasundaram, Vivekanandan S. Kumar, Kinshuk

Research output: Chapter in Book/Report/Conference proceedingPublished Conference contributionpeer-review

20 Citations (Scopus)

Abstract

Learners' attainment of academic knowledge in postsecondary institutions is predominantly expressed by summative or formative assessment approaches. Recent advances in educational technology has hinted at a means to measure learning efficiency, in terms of personalization of learner competency and capacity in terms of adaptability of observed practices, using raw data observed from study experiences of learners as individuals and as contributors in social networks. While accurate computational models that embody learning efficiency remain a distant and elusive goal, big data learning analytics approaches this goal by recognizing competency growth of learners, at various levels of granularity, using a combination of continuous, formative and summative assessments. This study discusses a method to continuously capture data from students' learning interactions. Then, it analyzes and clusters the data based on their individual performances in terms of accuracy, efficiency and quality by employing Particle Swarm Optimization (PSO) algorithm.

Original languageEnglish
Title of host publicationProceedings - 2013 IEEE 5th International Conference on Technology for Education, T4E 2013
Pages61-64
Number of pages4
DOIs
Publication statusPublished - 2013
Event2013 IEEE 5th International Conference on Technology for Education, T4E 2013 - Kharagpur, West Bengal, India
Duration: 18 Dec. 201320 Dec. 2013

Publication series

NameProceedings - 2013 IEEE 5th International Conference on Technology for Education, T4E 2013

Conference

Conference2013 IEEE 5th International Conference on Technology for Education, T4E 2013
Country/TerritoryIndia
CityKharagpur, West Bengal
Period18/12/1320/12/13

Keywords

  • Big Data
  • Hadoop
  • K-Means Clustering
  • Learning Analytics
  • Particle Swarm Optimization (PSO)-based Clustering

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