Crater Automated Essay Grader
How U of Michigan Built Automated Essay-Scoring Software to Fill ‘Feedback Gap’ for Student Writing
By Jessica Leigh Brown Jun 6, 2017
The University of Michigan’s M-Write program is built on the idea that students learn best when they write about what they’re studying, rather than taking multiple-choice tests. The university has created a way for automated software to give students in large STEM courses feedback on their writing in cases where professors don’t have time to grade hundreds of essays.
The M-Write program started in 2015 as a way to give more writing feedback to students by enlisting other students to serve as peer mentors to help with revisions. This fall, the program will add automated text analysis, or ATA, to its toolbox, primarily to identify students who need extra help.
Senior lecturer Brenda Gunderson teaches a statistics course that will be first to adopt the automated element of M-Write. “It’s a large gateway course with about 2,000 students enrolled every semester,” Gunderson says. “We always have written exams, but it never hurts to have students communicate more through writing.”
As part of the M-Write program, Gunderson introduced a series of writing prompts in the course last year. The prompts are targeted to elicit specific responses that clearly indicate how well students grasp the concepts covered in class. Students who chose to participate in the program completed the writing assignments, submitted them electronically, and received three of their peers’ assignments for review. “We also hired students who’d previously done well in the course as writing fellows,” Gunderson says. “Each fellow is assigned to a group of students and is available to help them with the revision process.”
Rising senior Brittany Tang has been a writing fellow in the M-Write program for the past three semesters. “Right now, I have 60 students in two lab sections,” she says. “After every semester, professors and fellows review every student submission from the class and score them based on a rubric.”
To build the automated system, a software development team used that data to create course-specific algorithms that can identify students who are struggling to understand concepts.
“In developing this ATA system, we needed to go through the pilot project and have students do the writing assignments to collect the data,” Gunderson says. “This fall, we’ll be ready to roll out the program to all the students in the course.” Gunderson is also incorporating eCoach, a personalized student messaging system developed by a research team at U-M, to provide students with targeted advice based on their performance.
When a student submits a writing assignment, the ATA system will generate a score. After a writing fellow quickly reviews it, the score gets delivered to the student via the eCoach system. The student then has an opportunity to revise and resubmit the piece based on the combination of feedback from the assigned writing fellow, the ATA system, and peer review.
Filling the Feedback Gap
The university’s launch of ATA is part of a growing nationwide trend in both K-12 and higher education classrooms, according to Joshua Wilson, assistant professor of education at the University of Delaware. Wilson researches the application of automated essay scoring. “I project the fastest adoption in the K-12 arena, and pretty quick adoption at community colleges, where it is helpful for remedial English courses,” Wilson says. “U-M presents a really interesting model of adoption. It has required them to build a content-specific system, but there’s really a demand for that among faculty who aren’t trained to teach writing.”
Wilson says ATA’s critics dislike the systems because they seem to remove the human element from essay grading—a traditionally personal act. But in reality, systems are being “taught” how to respond by their human programmers. “Systems are designed by looking closely at a large body of representative student work and the strengths and weaknesses of those papers,” he says. “Essentially, they provide a subset to the computer and they develop a model used to evaluate future papers.”
While a computer program will never give the same depth of feedback a professor can, Wilson says these systems could fill a growing gap in many K-12 and higher education classrooms. “I think people who outright reject these systems forget what the status quo is. Unfortunately, we know that instructors don’t give enough feedback, often because the teacher-student ratio is such that they don’t have time.”
In Wilson’s view, ATA feedback isn’t as good as human feedback, but it’s better than nothing—and the quality is improving all the time. “Obviously, a computer can’t understand language the same way we can, but it can identify lexical proxies that, combined with machine learning, can produce a score that’s very consistent with a score given by humans, even though humans are reading it in a different way.”
Title: Exploring Automated Essay Scoring for Nonnative English Speakers
(Submitted on 11 Jun 2017 (v1), last revised 29 Sep 2017 (this version, v3))
Abstract: Automated Essay Scoring (AES) has been quite popular and is being widely used. However, lack of appropriate methodology for rating nonnative English speakers' essays has meant a lopsided advancement in this field. In this paper, we report initial results of our experiments with nonnative AES that learns from manual evaluation of nonnative essays. For this purpose, we conducted an exercise in which essays written by nonnative English speakers in test environment were rated both manually and by the automated system designed for the experiment. In the process, we experimented with a few features to learn about nuances linked to nonnative evaluation. The proposed methodology of automated essay evaluation has yielded a correlation coefficient of 0.750 with the manual evaluation.
Submission historyFrom: Amber Nigam [view email]
[v1] Sun, 11 Jun 2017 10:18:46 GMT (428kb)
[v2] Wed, 14 Jun 2017 09:52:35 GMT (428kb)
[v3] Fri, 29 Sep 2017 07:08:50 GMT (652kb)
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