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Connecting performance to social structure and pedagogy as a pathway to scaling learning analytics in MOOCs: an exploratory study
Author(s) -
Goggins S.P.,
Galyen K.D.,
Petakovic E.,
Laffey J.M.
Publication year - 2016
Publication title -
journal of computer assisted learning
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.583
H-Index - 93
eISSN - 1365-2729
pISSN - 0266-4909
DOI - 10.1111/jcal.12129
Subject(s) - learning analytics , operationalization , construct (python library) , computer science , analytics , exploratory research , massive open online course , data science , knowledge management , psychology , mathematics education , world wide web , sociology , philosophy , epistemology , anthropology , programming language
This exploratory study focuses on the design and evaluation of teaching analytics that relate social learning structure with performance measures in a massive open online course (MOOC) prototype environment. Using reflexive analysis of online learning trace data and qualitative performance measures we present an exploratory empirical study that: (a) rigorously evaluates a novel, multi‐dimensional performance construct; (b) describes differences in small group dynamics and structure; and (c) draws a connection between learning performance and group structure. Performance is operationalized using a combination of knowledge construction measurement from discussion boards, rigorous analysis of student work products and several indicators of small group identity in the course examined. Interview and observational data are used to develop an approach for deriving and validating a model of the social structure of students in the course using traces of interaction data. The connection between performance and structure is developed at the small group unit of analysis. Implications for MOOC design, scaling MOOC analytics and a vision for developing social sensors in MOOC environments are presented in the discussion and conclusion.