Getting a paper accepted into CHI can really be a pain if you’re just starting out. I’ve been there and I sympathize. I try to be positive and constructive in my reviews, but I frequently find myself pointing out the same issues over and over again. I’m going through 7 reviews for CHI right now and there is a lot of good stuff in there, but also a couple of papers making rookie mistakes that make it hard to give the “4” or “5” score. Obviously, other people might be looking for something else, but if you do all of the things below, I would really have no reason to reject your paper.
- Introduction: keep it brief and to the point, but most importantly, don’t overreach in saying what you’ve done. If you say that your system actually helped kids learn something, I’m going to expect to see an evaluation that supports that claim and I’m not going to be able to accept a paper that only actually measured engagement or preference. So, frame your introduction in a way that sets the expectations right.
- Related Work: give me an overview of what has been done by others, how your work builds on that, and why what you did is different. At the end of this section, I need to have a good idea of the open problem you are working on, gap you are addressing, or improvement you are making. I actually find it helpful to draft this section before I start the study. If somebody has already addressed the same problem, it’s nice to know about it before you start rather than when you’re writing up your results (or worse, from your reviewers). On a final unrelated note, if I see a paper that only references work from one lab or one conference, I get suspicious about it potentially missing a lot of relevant work. I rarely reject a paper based on that, but it makes me much more cautious when reading the rest and I sometimes do my own mini lit-review if I suspect that there is big stuff missing.
- Methods: give me enough information to actually evaluate your study design, otherwise I have to assume the worst. For example, if you don’t say that you counterbalanced for order effects, I will assume that you didn’t. If you don’t say how you recruited participants, I will assume that they are all your friends from your lab. If you don’t say how you analyzed your qualitative data, I will assume that you just cherry-picked quotes. The rule of thumb is: can another researcher replicate the study from your description? I will never reject a paper for small mistakes (e.g., losing one participant’s video data, using a slightly inappropriate stat test, limitations of sampling, etc.) as long as it’s honest about what happened and how that affects the findings, but I have said “no” if I just can’t tell what the investigators did.
- Results: I basically want to see that the results fulfill the promises made in the intro, contribute to the problem/gap outlined in the related work, and are reported in a way that is appropriate to the methods. I’m not looking for the results to be “surprising” (I know other CHI reviewers do, however), but I do expect them to be rigorously supported by the data you present. The only other note on the results section is that I’m not looking for a data dump, I probably don’t need to see the answer to every question you asked and every measure you collected — stick to the stuff that actually contributes to the argument you are making (both confirming and disconfirming) and the problem at hand.
- Discussion: this is the section where I feel a lot of papers fall short. I won’t usually reject for this reason alone if the rest of the paper is solid, but a good discussion can lead me to checking that “best paper nomination” check box. Basically, tell me why the community should care about this work? If you have interesting implications for design, that’s good, but it’s not necessary and there’s nothing worse than implications for design that just restate the findings (e.g., finding: “Privacy is Important,” implication: “Consider Privacy”). When I look at an implication for design (as a designer), I want to have an idea of how I should apply your findings, not just that I should do so. But alternatively, I would like to hear about how your investigation contributes something to the ongoing threads of work within this community. Did you find out something new about privacy in this context that might be interesting or might be a new perspective of thinking about privacy in HCI? Does this work bring out some interesting new research directions and considerations (not just, “in the future, we will build this system”)? If you used an interesting/new/unusual method in your work, that could be another thing to reflect on in the discussion, because your approach could be useful to other investigators.
Okay, I have given away all of my reviewing secrets, because I don’t like rating good work down when the paper fails to present it with enough detail, consistency, or reflection. I hope that this is helpful to somebody out there! I say “yes” to a lot of papers already, but I’d like to be able to accept even more.