Parallel Particle Swarm Optimization (PPSO) clustering for learning analytics

Kannan Govindarajan, David Boulanger, Vivekanandan Suresh Kumar, K. Kinshuk

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

11 Citations (Scopus)


Analytics is all about insights. Learning-oriented insights are the targets for Learning Analytics researchers. Insights could be detected, analysed, or created in the context of variables such as the quality of interactions with the content, study habits, engagement, competence growth, sentiments, learning efficiency, and instructional effectiveness. Clustering techniques offer an effective solution for grouping learners using observed patterns. For instance, learners could be clustered based on the effectiveness of learners' self-regulation initiatives in reaching the target learning outcomes. Each learner could belong to a number of clusters that target different types of insights. One could also analyse the distance between clusters as a means to guide learners towards better performance. Further, one could analyse the effectiveness of cohesive peer groups within and among clusters. Traditional clustering techniques only cope with numerical or categorical data and are not readily applicable in offering learning analytics solutions. In addressing this gap, this research aims to design a Parallel Particle Swarm Optimization (PPSO) algorithm for the purposes of learning analytics, where the arrival of data is continuous, the types of data is both structured and unstructured, and the volume of data can be significantly large. The research will also describe the application of the PPSO algorithm to detect, analyse, and generate learning-oriented insights.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015
EditorsFeng Luo, Kemafor Ogan, Mohammed J. Zaki, Laura Haas, Beng Chin Ooi, Vipin Kumar, Sudarsan Rachuri, Saumyadipta Pyne, Howard Ho, Xiaohua Hu, Shipeng Yu, Morris Hui-I Hsiao, Jian Li
Number of pages5
ISBN (Electronic)9781479999255
Publication statusPublished - 22 Dec. 2015
Event3rd IEEE International Conference on Big Data, IEEE Big Data 2015 - Santa Clara, United States
Duration: 29 Oct. 20151 Nov. 2015

Publication series

NameProceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015


Conference3rd IEEE International Conference on Big Data, IEEE Big Data 2015
Country/TerritoryUnited States
CitySanta Clara


  • Hadoop Distributed File System
  • clustering
  • learning analytics
  • parallel particle swarm optimization
  • parallel processing


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