Learning linkages: Integrating data streams of multiple modalities and timescales

Ran Liu, John Stamper, Jodi Davenport, Scott Crossley, Danielle McNamara, Kalonji Nzinga, Bruce Sherin

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

Increasingly, student work is being conducted on computers and online, producing vast amounts of learning-related data. The educational analytics fields have produced many insights about learning based solely on tutoring systems' automatically logged data, or “log data.” But log data leave out important contextual information about the learning experience. For example, a student working at a computer might be working independently with few outside influences. Alternatively, he or she might be in a lively classroom, with other students around, talking and offering suggestions. Tools that capture these other experiences have potential to augment and complement log data. However, the collection of rich, multimodal data streams and the increased complexity and heterogeneity in the resulting data pose many challenges to researchers. Here, we present two empirical studies that take advantage of multimodal data sources to enrich our understanding of student learning. We leverage and extend quantitative models of student learning to incorporate insights derived jointly from data collected in multiple modalities (log data, video, and high-fidelity audio) and contexts (individual vs. collaborative classroom learning). We discuss the unique benefits of multimodal data and present methods that take advantage of such benefits while easing the burden on researchers' time and effort.

Original languageEnglish (US)
JournalJournal of Computer Assisted Learning
DOIs
Publication statusAccepted/In press - Jan 1 2018

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Keywords

  • collaborative learning
  • intelligent tutoring systems
  • log data
  • multi-modal data analytics
  • natural language processing
  • STEM education

ASJC Scopus subject areas

  • Education
  • Computer Science Applications

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