Collection and especially analysis of open-ended survey responses are relatively rare in the discipline and when conductedare almost exclusively done through human coding. We present an alternative, semiautomated approach, the structural topic model (STM) survey questions. Collection and especially analysis of open-ended data are relatively rare in the discipline and when conducted are almost exclusively done through human coding. We present an alternative, semiautomated approach, the structural topic model (STM) , that draws

We use Maimonides’ rule as an instrument for class size in large Israeli samples from 2002–2011. In contrast with Angrist and Lavy (1999), newer estimates show no evidence of class size effects. The new data also reveal enrollment manipulation near Maimonides cutoffs. A modified rule that uses birthdays to impute enrollment circumvents manipulation while still generating precisely estimated zeros. In both old and new data, Maimonides’ rule is unrelated to socioeconomic characteristics conditional on a few controls. Enrollment manipulation therefore appears to be innocuous. We briefly discuss possible explanations for the disappearance of Israeli class size effects since the early 1990s. (JEL C38, H52, I21, I28)

ABSTRACT: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) find 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.

Special thanks go to the Israeli Ministry of Educations for use of their secure research lab and especially to Eliad Trefler for his help with data in the lab. Angrist thanks the Arnold Foundation and The Spencer Foundation for financial support. Lavy acknowledges financial support from the European Research Council through ERC Advanced Grant 323439. Shany thanks the Israel Institute for financial support. The views expressed here are those of the authors alone and do not necessarily reflect those of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

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