Learning analytics for communities of inquiry

Vitomir Kovanović, Dragan Gašević, Marek Hatala

Research output: Contribution to journalArticleOtherpeer-review

Abstract

This paper describes doctoral research that focuses on the development of a learning analytics framework for inquiry-based digital learning. Building on the Community of Inquiry model (CoI) — a foundation commonly used in the research and practice of digital learning and teaching — this research builds on the existing body of knowledge in two important ways. First, given that the CoI model requires substantial manual coding of student discourse, its potential for guiding pedagogical interventions are limited. Thus, the first contribution is the development of a learning analytics system that automates this coding process by means of a novel text-classification algorithm that takes into the account the process nature of inquiry-based learning and the specifics of communication through asynchronous discussions. Furthermore, it is equally important to investigate how learning processes unfold over time through student interactions with information, technology, and other course participants. With this in mind, the second contribution of this research focuses on the development of analytical models that provide insight into these important aspects of inquiry-based learning.
Original languageEnglish
Pages (from-to)195-198
Number of pages4
JournalJournal of Learning Analytics
Volume1
Issue number3
DOIs
Publication statusPublished - 2014
Externally publishedYes

Keywords

  • learning analytics
  • community of inquiry
  • quantitative content anlysis
  • social network analysis
  • trace data clustering

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