Since I started using Google Classroom for writing classes a few years back, I’ve noticed a pattern in the emails Google sends you whenever a student clears a comment you left. A few times, I’ve been able to tell when a student was still working on a paper past the deadline or if they got enough sleep the night before (emails at 3:20 AM are a bad sign). Most often though, you just find that a lot of students are making edits the morning that a paper is due, as your first email check of the morning features 30+ emails all saying “Bob resolved a comment in Final Essay”.

There exists a tool called Draftback (introduced to me, as with many edtech tools, by Brent Warner), a browser extension for Chrome, that lets you replay the history, letter by letter, of any Google Doc that you have edit access on. Its most obvious utility is as a tool for detecting academic dishonesty that plagiarism checkers like Turnitin miss (like copy/pasted translations, which show up in the editing history as whole sentences or paragraphs appearing all at once as opposed to letter by letter). It also has the benefit of showing you the exact times that edits were made in a document, which you can use to track how quickly students started responding to feedback, how many revisions they made (grouped helpfully into sessions of edits made less than 5 minutes apart), and whether these revisions were all made in the 10 minutes the student said he was just running to the library to print it. Draftback is the kind of tool that you hope not to need most of the time, but is hard to imagine life without when you need it.

With the pattern in my email inbox fresh in my mind (a term just having ended here), I thought I’d use Draftback to see whether this flurry of last-minute editing had some bearing on grades. To be specific, I used Draftback to help me answer these questions:

- Do numbers of edits correlate with scores on final drafts (FD) on papers?
- Does the timing of edits correlate with FD scores?
- Do either of these correlate with any other numbers of interest?

This required quite a bit of work. First, I copied and pasted rough draft (RD) and FD scores for each one of my students’ essays for the past 3 terms, totalling 6 essays, into a big Google Sheet, adding one more column for change in grade from the RD to the FD (for example, 56% on the RD and 92.5% on the FD yields a change of 65.18%). Then, I generated a replay of the history of each essay separately. Because each essay is typed into the same Google Doc, this gives me the entire history of the essay, from outline to final product. After each replay was generated (they take a few minutes each), I hit the “document graphs and statistics” button in the top right to see times and numbers of edits in easier-to-read form. I manually added up and typed the timing and number of the edits into the Google Sheet above. Last, I thought of some values culled from that data I might like to see correlated with other values. Extra last, I performed a few t-tests to see if the patterns I was seeing were meaningful.

(The luxury of a paragraph about how annoying the data was to compile is part of the reason I put these on my blog instead of writing them up for journals.)

The values that I thought might say something interesting were:

- % of edits (out of all edits) that occurred on a class day
- I’m curious whether students who edit on days when they don’t actually see my face do better – i.e., if students who edit on the weekends write better. Eliminating class days also helpfully eliminates lab days, the two class days a week when all students are basically forced to make edits. Incidentally, our classes meet Mon-Thu and final drafts are always due on the first day of the week. The average across all the essays was 63%, with a standard deviation of 38%.

- % of edits that occurred on the due date
- Specifically, before 1 PM – all my final drafts are due at the beginning of class, and all my classes have started at 1 PM this year. My assumption is that a high % of edits on the due date is a sign of poor work habits. The average was 21% with a standard deviation of 31%.

- total # of edits
- One would hope that the essay gets better with each edit. This number ranged from near 0 to more than 6000, with both an average and standard deviation of about 1700. Obviously, if you calculate this number yourself, it will depend on the length of the essay – mine were all between 3 and 5 pages.

- maximum # of edits per day
- I’m interested in whether a high number of edits per day predicts final grades more than a high number of edits total. That is, I want to know if cram-editing benefits more than slow-and-steady editing. The average and standard deviation for this were both about 1200.

- # of days with at least 1 edit
- Same as the above – I want to know if students who edit more often do better than ones who edit in marathon sessions on 1 or 2 days. The average was 3.25 days with a standard deviation of about 1 day.

All of the above were computed from the due date of the last RD to the due date of the FD, up to a maximum of 1 week (my classes last for 6 weeks, and there is very little time between drafts – read more about the writing process in my classes here). When I was done, after several hours of just copying numbers and then making giant correlation tables, I had hints of what to look into more deeply:

As you can see in cells C9-H14 (or duplicated in I3-N8), students didn’t necessarily use the same revision strategies from essay to essay. A student who had a ton of edits on one day for essay 1 might have fewer edits spread out over more days for essay 2, as evidenced by the not-terribly-strong correlations in the statistics between essay 1 and essay 2. To take one example, “days with > 0 edits” on essay 1 was correlated with “days with > 0 edits” on essay 2 at just 0.21 (cell M7). Some of these differences were still statistically significant at p=0.05 (a good enough p for a blog, imo):

- Students who did > 2000 total edits on essay 1 had an average of 3428 total edits on essay 2. Students who did <= 2000 total edits on essay 1 had an average of 1650 total edits on essay 2.
- Students who did > 50% of their edits for essay 1 on the due date did an average of 45% of their edits for essay 2 on the due date. Students who did <= 50% of edits on essay 1 on the due date did an average of 17% of their edits for essay 2 on the due date.

Anyway, because it seemed prudent to consider the strategies used on each essay rather than the strategies used by each student, I made a second spreadsheet where the individual essays rather than the students (who each wrote 2 essays) are the subject of comparison, resulting in this much-easier-to-read correlations table:

Columns I and J (or rows 9 and 10) are probably the most interesting to other writing teachers: those hold the correlations between statistics derived from Draftback data and I) final draft scores and J) change in score between the rough draft and final draft. In plain English, the correlations here suggest:

- As expected, % of edits on class days and % of edits on the due date are negatively correlated with the final grade for the essay. That is, people who did a lot of their edits in class or right before turning in the essay seemed to do worse (but not by much-neither produces statistically significant differences in FD grades or in improvement between RD and FD).
- Total # of edits and max edits per day are both positively correlated with final grades (and with each other). Editing more tends to produce better essays.
- Everything that is true for the final scores is also true for the change in scores between RD and FD. The fact that RDs were even more negatively correlated with % edits on class days and % edits on the due date than those values were with FDs mean that the changes appear to be positively correlated, but I take it as meaning that those strategies with an improvement from very bad RD scores to mildly bad FD scores.

To give a bit more detail, these were some statistically significant differences (p=0.05):

- Students who did > 2000 total edits had an average grade of 86.8% on the FD. Students who did <= 2000 total edits had an average grade of 78.7% on the FD.
- Students who did > 3000 total edits had an average grade improvement of 17.8% between the two drafts. Students who did <= 3000 total edits had an average grade improvement of 4.9%.
- Students who did edits on > 3 days had an average grade of 84.8% on the FD. Students who did edits on <= 3 days had an average grade of 78.9%.
- Students who did edits on > 5 days (that is, almost every day) had an average grade improvement of 33.6% between the two drafts. Students who did edits on <= 5 days had an average grade improvement of 5.8%.

The data suggests a fairly uncontroversial model of a good writing student – one who edits often, both in terms of sheer numbers of changes and in terms of frequency of editing sessions. In fact, “model student” rather than “model essay” may be what the data is really pointing at – the amount and timing of the work that went into a particular essay seems sometimes to show more about the student’s other work than it does about the quality of that essay.

For example, it’s not clear why data derived from the time period between RD and FD would be correlated with RD scores (in fact, you would expect some of the correlations to be negative, as high RD scores might tell a student that there is less need for editing), but perhaps the fact that the same data points that are correlated with FD scores are correlated in the same ways with RD and final course grades indicates that the data shows something durable about the students who display them (my caveat earlier notwithstanding). It is feasible that the poor work habits evidenced by editing a major paper a few hours before turning it in might affect students’ other grades more than that paper itself.

In fact, this seems to be the major lesson of this little research project. One t-test on % edits on due date was statistically significant – one that compared students’ final course grades. To be precise, students who did > 20% of their total edits on the due date had average course grades of 84.5%. Those who did <= 20% of their total edits on the due date had average course grades of 88.8%.

Just to pursue a hint where it appeared, I went back into my stat sheets for each class for the last year and copied the # of assignments with grade 0 (found on the “other stats” sheet) for each student into my big Google Sheet. Indeed, there was a statistically significant difference. That is, students who made > 20% of edits made on the day an essay was due got a score of 0 on 5% of assignments across the term, and students who made <= 20% of edits made on the day an essay was due got a score of 0 on 3.2% of assignments across the term.

Like many characteristics of “good students”, from growth mindset to integrative motivation, whether a pattern of behavior correlates with success and whether it is teachable are two almost unrelated questions. It doesn’t necessarily follow from this research that I should require evidence of editing every day or that I should move due dates forward or back. It does suggest that successful students are successful in many ways, and that editing essays often is one of those ways.

I might just want to tell my students that I really love the Google Docs “cleared comment” emails that I get on Monday morning and I wish I got them all weekend, too.