Pipeline for expediting learning analytics and student support from data in social learning

Yohan Jo, Gaurav Tomar, Oliver Ferschke, Carolyn Penstein Rosé, Dragan Gaševíc

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

8 Citations (Scopus)


An important research problem in learning analytics is to expedite the cycle of data leading to the analysis of student progress and the improvement of student support. For this goal in the context of social learning, we propose a pipeline that includes data infrastructure, learning analytics, and intervention, along with computational models for individual components. Next, we describe an example of applying this pipeline to real data in a case study, whose goal is to investigate the positive effects that goal-setting students have on their peers, which suggests ways in which we might foster these social benefits through intervention.

Original languageEnglish
Title of host publicationLAK '16 Conference Proceedings
Subtitle of host publicationThe Sixth International Learning Analytics & Knowledge Conference: Enhancing Impact: Convergence of Communities for Grounding, Implementation, and Validation
EditorsShane Dawson, Hendrik Drachsler, Carolyn Penstein Rosé
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages2
ISBN (Electronic)9781450341905
Publication statusPublished - 2016
Externally publishedYes
EventInternational Learning Analytics & Knowledge Conference 2016 - University of Edinburgh, Edinburgh, United Kingdom
Duration: 25 Apr 201629 Apr 2016
Conference number: 6th


ConferenceInternational Learning Analytics & Knowledge Conference 2016
Abbreviated titleLAK 2016
CountryUnited Kingdom
Internet address


  • Learning analytics
  • Social learning

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