Open AccessComputer-Assisted Reading and Discovery for Student Generated Text in Massive Open Online Courses
Author(s) -
Justin Reich,
Dustin Tingley,
Jetson Leder-Luis,
Margaret E. Roberts,
Brandon Stewart
Publication year - 2014
Publication title -
journal of learning analytics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.084
H-Index - 7
ISSN - 1929-7750
DOI - 10.18608/jla.2015.21.8
Subject(s) - learning analytics , computer science , reading (process) , meaning (existential) , variation (astronomy) , analytics , data science , world wide web , semantics (computer science) , mathematics education , psychology , linguistics , philosophy , physics , astrophysics , psychotherapist , programming language
Dealing with the vast quantities of text that students generate in Massive Open Online Courses (MOOCs) and other large-scale online learning environments is a daunting challenge. Computational tools are needed to help instructional teams uncover themes and patterns as students write in forums, assignments, and surveys. This paper introduces to the learning analytics community the Structural Topic Model, an approach to language processing that can 1) nd syntactic patterns with semantic meaning in unstructured text, 2) identify variation in those patterns across covariates, and 3) uncover archetypal texts that exemplify the documents within a topical pattern. We show examples of computationally aided discovery and reading in three MOOC settings: mapping students’ self-reported motivations, identifying themes in discussion forums, and uncovering patterns of feedback in course evaluations.