One of my favorite conferences, Interaction Design and Children (IDC) is going to be in NYC this year! Full papers are called for on January 22nd and I hope to see a lot of great stuff this year (definitely excited about the paper I will be submitting ). In the meantime, I wanted to reflect on some of my favorite IDC papers of all time. These are the ones that have directly influenced my own work and I wanted to share them with others who are interested in family communication technologies.
The first paper is about Mediated Intimacy in Families and it is a qualitative investigation of how parents and children build closeness. Dalsgaard et al. emphasize the importance of emotional and physical expressiveness in all relationships. But they also found differences: parents and children build closeness through play together and care provided by the parent for the child, rather than through reciprocal exchanges and setting of public and private boundaries (as in strong-tie relationship). These insights led quite directly to the work that I’ve done with the ShareTable, where my goal was supporting the kinds of care and play activities that might be done remotely.
Freed et al. used a doll house to investigate how children think about remote communication. This image is from their paper and belongs to the authors.
The second paper is about children connecting with peers through tangible characters in doll houses. Freed et al. built a pair of doll houses where toys could call each other, mail letters, and videochat with each other. This is a cool idea and an interesting way of investigating how children think about remote communication. The dollhouse approach turned out to be quite compelling to the kids, with most of them engaging in some level of shared play and finding the experience engaging. I had already been engaging with the idea of communicating through play, but seeing this work eventually inspired me to think about physical arrangements for videochat that could support narrative and pretend play.
Raffle et al. investigated asynchronous messaging with toddlers. This image is from their paper and belongs to the authors.
The last paper is about asynchronous messaging with preschoolers. If you had asked me before this paper was published whether I thought that asynchronous communication with toddlers would work, I would probably have been very dubious. It’s just not that easy to communicate remotely and it seems much easier to connect with a person than with a message! I have to say that this paper has changed my mind. It presents three prototypes for asynchronous contact that thoughtfully explore what it may look like to engage toddlers with remote relatives asynchronously. This hasn’t led me to do a new paper or a new project (yet), but it reminded me not to underestimate the power of creative ideas and the ability of children to adapt to new ways of connecting.
If you like these papers, you might thing about checking out IDC in NYC this year. I’ll definitely be there, so let me know if you want to meet up.
I was avoiding real work online and I stumbled across an interesting area of Google Scholar. Turns out they’ve calculated the h5-index of various publication by subfield and HCI is one of the sub-areas featured. It’s obviously not perfect, for example, CHI appears as two different conferences depending on how people cited it, but it’s still fascinating.
First, I’m especially grateful that this list has exposed me to a publication with which I wasn’t familiar but that I find quite relevant and fascinating: the journal of Computers in Human Behavior. Just a quick skim through the top 20 papers suggests that there might be a lot of good stuff that I’ve been overlooking in my searches. I do wonder why a lot of these papers haven’t appeared on my previous related work investigations (of say, video game addiction)? Am I not using the right search terms?
Second, I found it interesting that the results don’t seem to closely match another list gathered of average citations per paper. I tend to trust the Google results more as they have a more nuanced metric and the results seem more believable (at least in terms of CHI being very high on the list and the major conferences being represented). Also, the Google list only includes the last 5 years of publications rather than the whole corpus of each venue, so I think it gives a more accurate idea of the way things currently stand.
Lastly, I wanted to do a quick clustering of the top ten cited papers in CHI, CSCW, and UbiComp over the last 5 years to figure out what we’re citing the most. I picked these 3 conferences because I’ve actually been to these, so I feel a bit more comfortable doing this classification.
- CHI seems to care the most about social networking sites (3/10 papers). Otherwise, there is a lot of variety in the top cited papers, including: crowdsourcing, activity sensing to support fitness, surface computing, and end-user programming. There were else some reflection and vision papers on this list, which wasn’t the case for the other two conferences. I guess CHI especially likes talking about the big picture.
- CSCW seems to also mostly care about social networking sites (5/10 papers), especially Twitter (3 out of those 5). Disaster response, social search, crowds and wikipedia, and input devices for collaboration each have some representation on that list too. But, I’m most psyched that family videochat is on that list as well. It’s not my paper, but it’s nice to know that people care about my topic. Though CSCW has been exploring non-work domains, it is still the only conference with any top papers explicitly focusing on work environments (2/10 papers).
- Ubicomp is obsessed with sensing what people are doing and where they are doing it, with 3 papers on activity recognition, 2 on location sensing, and 1 on sensing events over power lines. But apparently, Ubicomp also cares about useful contexts for sensing with papers about sensing activities or location is order to support family awareness, sustainability, and health. This is confirming my earlier assertion that it should change its name to SensorComp.
I hope you find these lists as interesting as I did. Were you surprised by anything you saw (or didn’t see) there?
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.