InPath forum: a real-time learning analytics and performance ranking forum system

Adeline Chew Yao Yi, Tey Kai Ying, Siang Jo Yee, Wee Mee Chin, Ting Tin Tin

Research output: Contribution to journalArticleResearchpeer-review

8 Citations (Scopus)

Abstract

Numerous studies have been conducted on the influence of peers on students’ learning processes and their participation in online forums. However, these studies are limited in terms of system functionality and lack real-time analysis. In this study, we present InPath, a real-time analytics forum system, to rank and provide feedback on students’ online participation performance. We leveraged other students’ online discussion forum performance as an effective reference point to inspire forum participation. A set of learning metrics was generated to analyze students’ contributions to online forums. The K-means clustering method was used to classify students into three broad levels: Hall of Fame, All Star, and Rookies. The results showed that students with higher badge levels were more likely to spend more time on forums. In summary, this study highlights the implications of this state-of-the-art system for learning analytics on online forums, including supporting instructors and students in determining overall and individual performance on the forum.

Original languageEnglish
Pages (from-to)128536-128542
Number of pages7
JournalIEEE Access
Volume10
DOIs
Publication statusPublished - 2022

Keywords

  • Clustering algorithms
  • Discussion forums
  • Feedback
  • feedback system
  • Machine learning algorithms
  • Measurement
  • online discussion forums
  • Ranking (statistics)
  • ranking system
  • real-time learning analytics
  • Real-time systems

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