Dynamic Learning Path Prediction - A Learning Analytics Solution

Kannan Govindarajan, Vivekanandan Suresh Kumar, Kinshuk

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

21 Citations (Scopus)

Abstract

In the course of the last few years, many educational and research communities have been deeply invested in the development of learning analytics. Learning analytics measures the effectiveness and efficiency of learning environments, in order to understand the needs of learners and to improve the teaching process. The research presented in this paper uses Parallel Particle Swarm Optimization (PPSO) mechanism to analyze and predict a dynamic learning path for learners based on competence and meta-competence values observed in a learning environment. The proposed system is able to auto-configure and auto-customize itself to offer personalized and individualized instruction, and calculate an optimal learning pathway for learners. Furthermore, it provides on-demand and adaptive support for learners based on their needs. Experimental evaluations - carried out within a Java Programming course - demonstrate the effectiveness of the proposed system.

Original languageEnglish
Title of host publicationProceedings - IEEE 8th International Conference on Technology for Education, T4E 2016
EditorsViraj Kumar, Kinshuk, Sahana Murthy
Pages188-193
Number of pages6
ISBN (Electronic)9781509061150
DOIs
Publication statusPublished - 11 Jan. 2017
Event8th IEEE International Conference on Technology for Education, T4E 2016 - Mumbai, Maharashtra, India
Duration: 2 Dec. 20164 Dec. 2016

Publication series

NameProceedings - IEEE 8th International Conference on Technology for Education, T4E 2016

Conference

Conference8th IEEE International Conference on Technology for Education, T4E 2016
Country/TerritoryIndia
CityMumbai, Maharashtra
Period2/12/164/12/16

Keywords

  • Artificial Intelligence
  • Dynamic Learning Path
  • Learning Analytics
  • Prediction

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