final edits to supplement

Author: paxtonfitzpatrick

paper/supplement.pdf

@@ -515,7 +515,7 @@ \section*{Supplementary results}
 The time series plots of individual questions' correlations also exhibit clear 
 ``peaks'' when computed using LDA embeddings, but not when computed using BERT 
 embeddings. This indicates that the LDA embeddings are capturing specific 
-``matches'' between individual quiz questions and relatively well defined time 
+``matches'' between individual quiz questions and relatively well-defined time 
 periods of lecture content, whereas the BERT embeddings appear to blur these 
 specific correspondences. Taken together, these comparisons suggest that our LDA 
 model trained specifically on the two lectures' contents is sensitive to subtle 
@@ -526,7 +526,7 @@ \section*{Supplementary results}
 Supp.~Tab.~\ref{tab:matches}). Meanwhile, BERT, whose embedding weights must be 
 able to represent a far broader range of concepts, consequently blurs these 
 subtle distinctions between concepts conveyed within the span of a single course 
-lecture, thereby affording a ``lower resolution'' depiction of its contents. 
+lecture, thereby affording a ``lower-resolution'' depiction of its contents. 
 
 We also compared the within-lecture question matches identified by our LDA-based 
 approach to analogous matches obtained via a simpler approach that did 
@@ -576,7 +576,7 @@ \section*{Supplementary results}
 content share more superficial similarities. The time series of Jaccard 
 similarities, by contrast, are less cleanly structured and do appear to reflect 
 such superficial content matches in the more scattered, brief, and weak 
-``peaks'' they exhibit. As an example, panel E displays text of the two quiz 
+``peaks'' they exhibit. As an example, panel E displays the text of the two quiz 
 questions highlighted in green and purple in panels A through D, along with the 
 text from the best-matching intervals from the lectures (identified based on
 topic vector correlation). Both questions include words that---given the subject