Learning analytics in seamless learning environments: a systematic review

Jewoong Moon, Daeyeoul Lee, Gi Woong Choi, Jooyoung Seo, Jaewoo Do, Taehyeong Lim

Research output: Contribution to journalReview articlepeer-review

Abstract

We implemented a systematic literature review to investigate the trends and issues of learning analytics in seamless learning environments. We collected and analyzed a total of 27 empirical journal articles that study and discuss learning analytics design and implementation in seamless learning environments. In a recent decade, researchers have explored various seamless learning environments supporting students’ inquiry-based and experiential learning. Seamless learning aims to engage students to develop, elaborate, and apply knowledge to various inquiry-based learning contexts. With the significance of formative assessment in digital learning environments, integrating learning analytics into seamless learning environments increasingly evolved to trace and assess student learning. Despite various uses of learning analytics during seamless learning, there is a lack of comprehensive reviews that illustrate how learning analytics has been integrated into seamless learning. This literature synthesis decomposes the collected journal articles to understand learning analytics design and integrations inherent to seamless learning environments. Also, using qualitative thematic analysis, this systematic literature review demonstrates various design practices and future research directions of learning analytics in seamless learning environments.

Original languageEnglish (US)
JournalInteractive Learning Environments
DOIs
StateAccepted/In press - 2023

Keywords

  • Seamless learning
  • educational data mining
  • learning analytics
  • systematic literature review
  • thematic analysis

ASJC Scopus subject areas

  • Education
  • Computer Science Applications

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