eRevise: Using Natural Language Processing to Provide Formative Feedback on Text Evidence Usage in Student Writing
Table 5: Lookup table for feedback selection.
['[BOLD] Feature  [ITALIC] NPE', '[BOLD] Value 0', '[BOLD] Value 0', '[BOLD] Value 0', '[BOLD] Value 1', '[BOLD] Value 2', '[BOLD] Value 3', '[BOLD] Value 4', '[BOLD] Value 1', '[BOLD] Value 1', '[BOLD] Value 2', '[BOLD] Value 2', '[BOLD] Value 3', '[BOLD] Value 4', '[BOLD] Value 3', '[BOLD] Value 4']
SPCAWE=3, and SPClmh =M. We are about to begin the next deployment of eRevise, which will extend our work in two ways. First, to better determine the benefit of using AES to adaptively guide revision, we have added a control condition where eRevise will display the same generic feedback message to all students: “MAKE YOUR ESSAY MORE CONVINCING - Help readers understand why you believe the fight against poverty is/isn’t achievable in our lifetime.” This is in contrast to the existing eRevise adaptive feedback, where students receive different messages based on AES. Second, students will use eRevise for two different forms of the RTA (i.e., RTAspace in addition to RTAMVP). While we have already trained a SG model for scoring RTAspace We are also exploring adding CO-ATTN scores to the lookup table.