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Analysis of Students’ Performance in an Online Discussion Forum: A Social Network Approach

  • Arnob Islam Khan
  • Vasilii Kaliteevskii
  • Iuliia Shnai
  • Leonid ChechurinEmail author
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 95)

Abstract

In the new era of digitalization, the education sector is experiencing changes in terms of the learning design, teaching methods, engagement of the learners and integration of technology. Flexibility of online courses is attracting more and more students to the learning platforms every day. The learning activity of students on the online platforms generates enormous amount of data. Practically every click can be traced and described. Learners are viewing the video lectures and digital lessons as a passive form of learning and most of the active learning is taking place in the form of online discussions. Therefore, in order to measure the students’ active engagement in an online course, it is essential to evaluate the communication channels. However, the assessment methods for online discussion remain limited. The objective of the work is to provide a ranking of students, based on their participation in the online discussion forum. It provides an opportunity for the teachers to automatically assess students’ performance quantitatively based on systematic approach. In this paper, network centrality measures are utilized to rank the students based on their interactivity. Text analytics is applied in association with sentiment analysis to assess meaningfulness of each student’s communication. The method was tested on the online course data of “Systematic Creativity and TRIZ basics” at LUT University, Finland. This work was partially supported by CEPHEI project of the ERASMUS + EU framework.

Keywords

Social-network analysis Online-discussion forums Online learning Sentiment analysis Neuro-linguistic programming NLP 

Notes

Acknowledgments

This work was partially supported by CEPHEI project of ERASMUS+ EU framework which is an ongoing project focusing on digitalization of industrial innovation-related contents. The authors hope to conduct further research and extend this work in future with possible support from this project.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Arnob Islam Khan
    • 1
  • Vasilii Kaliteevskii
    • 1
  • Iuliia Shnai
    • 1
  • Leonid Chechurin
    • 1
    Email author
  1. 1.LUT UniversityLappeenrantaFinland

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