Progressive escalation of online collaborative learning: Practice and evaluation

Research output: Contribution to conferencePaper

Abstract

Learning analytics has been identified as an emerging research discipline focused on the measurement, collection, analysis and reporting of data about learners and their contexts for understanding and optimizing learning and the learning environments. Technological approaches in the area of educational data mining are key elements in learning analytics. However, the ultimate goal of understanding and optimizing learning can hardly be achieved without related rigorous underpinning learning theories. In this connection, this paper aims to address the challenge of building strong connections between learning analytics and the learning sciences by the experience of a case study. To enlighten learners to proceed from surface to deep learning, scaffolding strategies were implemented to 172 per-service teachers who engaged in a series of online collaborative learning activities in a wiki environment. The Bloom’s Taxonomy framework, escalating from knowledge, comprehension, application, analysis, synthesis to evaluation, was adopted as an analytical tool to explore the levels of students’ cognitive advancement. Data recorded in the wiki platform was extracted for analysis to investigate the extent of deep learning provoked by corresponding scaffolding strategies. This study suggests a framework for analyzing the depth of cognitive development in online collaborative learning using the Bloom’s Taxonomy for future reference.
Original languageEnglish
Publication statusPublished - Jul 2015

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Citation

Ng, W. S. (2015, July). Progressive escalation of online collaborative learning: Practice and evaluation. Paper presented at The 22nd International Conference on Learning and the Learner knowledge community, Universidad San Pablo CEU, Madrid, Spain.

Keywords

  • Learning analytics
  • Assessment for learning
  • Online collaborative learning