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merging PCF changes into this fork
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paper/changes.pdf

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paper/changes.tex

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\documentclass[10pt]{article}
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%DIF LATEXDIFF DIFFERENCE FILE
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%DIF DEL old.tex Mon Feb 19 07:49:49 2024
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%DIF ADD main.tex Thu Feb 22 00:08:08 2024
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%DIF ADD main.tex Thu Feb 22 00:13:45 2024
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%DIF 2a2
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\usepackage{amsmath} %DIF >
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%DIF -------
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general, or about Quiz~1 performance in particular. This would be expected, for
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example, if participants were guessing about the answers to the Quiz~1
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questions (prior to having watched either lecture). When we repeated this
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analysis for Quizzes~2 and~3, we found that }\textit{\DIFadd{higher}} \DIFadd{estimated knowledge
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for a given question predicted a greater likelihood of answering it correctly
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analysis for Quizzes~2 and~3, we found that higher estimated knowledge
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for a given question predicted a }\textit{\DIFadd{greater}} \DIFadd{likelihood of answering it correctly
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(Quiz~2: $OR = 2.905,\ \lambda_{LR} = 17.333,\ 95\%\ \textnormal{CI} =
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[14.966,\ 29.309],\ p = 0.001$; Quiz~3: $OR = 3.238,\ \lambda_{LR} = 6.882,\
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95\%\ \textnormal{CI} = [6.228,\ 8.184],\ p = 0.016$). Taken together, these

paper/main.pdf

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paper/main.tex

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@@ -665,8 +665,8 @@ \section*{Results}
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general, or about Quiz~1 performance in particular. This would be expected, for
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example, if participants were guessing about the answers to the Quiz~1
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questions (prior to having watched either lecture). When we repeated this
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analysis for Quizzes~2 and~3, we found that \textit{higher} estimated knowledge
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for a given question predicted a greater likelihood of answering it correctly
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analysis for Quizzes~2 and~3, we found that higher estimated knowledge
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for a given question predicted a \textit{greater} likelihood of answering it correctly
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(Quiz~2: $OR = 2.905,\ \lambda_{LR} = 17.333,\ 95\%\ \textnormal{CI} =
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[14.966,\ 29.309],\ p = 0.001$; Quiz~3: $OR = 3.238,\ \lambda_{LR} = 6.882,\
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95\%\ \textnormal{CI} = [6.228,\ 8.184],\ p = 0.016$). Taken together, these

paper/supplement.pdf

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