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Once all data for each student (clicker responses, attendance, and exam grades) were gathered in Excel, student names were removed from the spreadsheet to ensure anonymity.
Peak flow and FEV1 data for each student was expressed as a Z-score relative to the student's mean score over the period of the study.
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The following data were available for each student: self-mark, tutor-mark and the peer-mark.
We learned that we would not be able to rely solely on activity log data for assessing student understanding.
Analysis for the ATHCTS was missing data for one student and analysis for the TSS was missing data for two students due to insufficient valid responses being provided.
In addition, the response rate for year 2 students was lower than anticipated, and in the analysis there were some missing data for the student characteristics variables.
Using these data, researchers manually calculated three rates for each student, as explained above.
Using this data, researchers manually calculated three rates for each student, as explained below.
These data resulted in 4 reflection scores for each student (2 cases with each being scored twice), which were used in a generalizability study to analyze intra-rater and case specificity as possible sources of variance in reflection scores.
We report student performance data for each assessment item as the percentage of correct responses for a given item.
Field sites may lack resources to support training (including involving students in field research and/or preparing data for student use) and must ensure that trainee datasets protect confidentiality of human subjects.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com