Unfolding learning analytics for big data

Jeremie Seanosky, David Boulanger, Vivekanandan Kumar, Kinshuk

Research output: Contribution to journalJournal Articlepeer-review

4 Citations (Scopus)


Educational applications, in general, treat disparate study threads as a singular entity, bundle pedagogical intervention and other student support services at a coarser level, and summatively assess final products of assessments. In this research, we propose an analytics framework where we closely monitor individual threads of study habits and assess study threads in an individual fashion to trace learning processes leading into assessment products. We developed customized intervention to target specific skills and nurture optimal study habits. The framework has been implemented in a system called SCALE (Smart Causal Analytics on LEarning). SCALE enables the tracking of students’ individual study threads towards multiple final study products. The large volume, multiple variety, and incessant flow of data classifies our work in the realms of big data analytics. We conducted a preliminary study using SCALE. The results show the ability of the system to track the evolution of competencies. We propose that explicitly supporting the development of a targeted set of competencies is one of the key tenets of Smart Learning Environments.

Original languageEnglish
Pages (from-to)377-384
Number of pages8
JournalLecture Notes in Educational Technology
Issue number9783662441879
Publication statusPublished - 2015


  • Bigdata
  • Coding competency
  • Learning analytics
  • Learning management systems
  • Learning traces
  • Programming
  • Training


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