Data underlying the writing assessment study on depth perception representation during rating
doi: 10.4121/20364255
CC BY-NC 4.0
The research that the current dataset underlies proposes to use depth perception to represent raters’ decision when evaluating ESL essays, as an alternative medium to conventional form of numerical scores. The researchers verified the new method’s accuracy and inter/intra-rater reliability by inviting 24 ESL teachers to perform different representations when rating 60 essays written by Chinese ESL learners. During numerical representation, the raters expressed their evaluation in form of numbers, while with the DP-based method the raters express their evaluation by marking distances with nail tags on a wood ruler with hidden scale marks. Then the researchers translated the distance results into numbers, and compared the accuracy and inter/intra-rater consistency of the two approaches by referring to these essays’ criteria scores from 8 expert raters.
This data file contains rating results that are listed in separate columns, Ea for 30 random essays and Eb for the other 30 essays, Groups A for 12 raters using numer representation mathod and Group B for the other 12 raters using DP representation method. The scoring results by 8 experts are also included in this data file.
- 2022-07-25 first online, published, posted
DATA
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55582a1a6c7a9c5b1741fb476bb46992Lian's dataset for writing rating study.xlsx - download all files (zip)
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