How Gamification Works for Me

Some of the tools I use to gamify my life

Gamification–rewarding everyday behaviors with fun, points, or virtual prizes–is kind of a big deal nowadays. I’m a big proponent of gamification and I wanted to share why it works for me.

The reason I like gamification is because I invented it. I mean, you might have invented it too, but that doesn’t make my personal act of creation any less true. When I was a kid on a long errand with my parents, I would count my steps. That was the first time I ever counted to 1000. I announced it proudly to my parents and they dispensed the appropriate praise (achievement unlocked!). In school, I gamified paying attention by setting and tracking goals, such as ask or answer a question in every class for a month. In life I gamified everyday stuff, like go as long as I can without missing a day of flossing (keep the date written on my bathroom mirror). Of course, all of the points were just in my head and nobody else cared about the games that make my life just a bit more fun and just a bit less tedious.

Today, I use a number of existing tools (and tools that I have appropriated outside of their original purpose) to gamify my life:

  • I check in on FourSquare when I go somewhere to get badges, mayorships, and points
  • I use EpicWin to get loot and level-up a character by completing real tasks on my to-do list
  • I level up my Pokemon by getting high step counts on my Pokewalker pedometer
  • At CHI this year, I used MissionRunner to complete missions, get badges, and get actual prizes while getting the most out of my conference experience
  • I get kudos for saving the greatest percent on groceries by posting my awesome savings on the fridge and also on SouthernSavers.com for their Friday Finals.
  • I have my own set of daily and weekly goals (based on The Happiness Project book) and I track them using Salud! There’s no better reward than seeing a “perfect” day or week (though, this is closer to the way I gamified things as a child — it’s all in my head).

There are four big ways that gamification makes my life better:

  • Gives me an excuse to do what I should — when the thing that I should be doing like exploring a new city, talking to a stranger, or walking 10000 steps a day feels more awkward than the thing that I’m currently doing (e.g., working in my hotel room, talking with people I already know, or sitting on the couch), sometimes the game gives me just enough of an extra push to do the right thing. It also sometimes makes a good excuse to give to others about your activity or changing activities.
  • Burst of energy when close to a milestone — when I get close to the next level, the next badge, or the next round number, I get a burst of energy that helps me get there. For example, yesterday I was this close to hitting level 10 on my EpicWin character, so I actually got some of today’s todos done last night just to get that level-up feeling.
  • Helps me act as the person that I want to be — this is particularly true of games where the achievements and status are shared with others in my social network. For example, I want to be the kind of person who explores new places and has fun during the week and on weekends. If when I look over the week’s checkins on foursquare all I see is “work” and “home,” I’m actually a bit more motivated to put aside the Internet and go exploring with friends, so that my checkins may actually be interesting to my friends and give off the impression that I want.
  • Gives me tracking and reflection for free — most games track your progress, making self-tracking into a fun credit-getting step instead of a boring chore. I track because I like to see my progress in the game, but when I need to, I now have a treasure trove of personal data that I can review for to gain insights about myself (e.g., the longer I plan to spend working, the less I get done), to remember specific details of past events (e.g., what was that restaurant we liked on the road trip in June?), and to see potential areas to improve (e.g., Really? It’s been 5 months since my last haircut?).

So, big kudos to everybody who does this kind of work. I’d love to see more stuff out there to help me make my life even more fun. I’d love to hear about other apps, tools, and strategies that you use to make your life more fun.

Lessons from Uber-Successful Games for Kids

Pokemon and Neopets are two incredibly successful games. What do they have in common and how are they different?

I’ve been thinking a lot about game elements that are common to successful games. Now, let me preface this by saying that I’m by no means a games researcher, just an observer. In this post, I want to examine two games for children that have been incredibly successful. By successful, I mean that these games have been around for more than 10 years and still command a great share of the market. This means that they are able to retain players as they grow up and/or attract new players.

The first is the Pokemon series of games, which has sold 220 million games so far and has been around since 1996. The core games are made for the mobile platform (Gameboy before, Nintendo DS now). This RPG game allows players to move through a storyline, while encountering, collecting, and battling animal-like creatures called Pokemon. The Pokemon universe was one of the first and most successful to combine video games, trading cards, TV series, and physical toys in a consistent campaign (with no one part as an afterthought to the other parts)  – a model later followed by a number of other franchises such as Yu-Gi-Oh and Digimon. The aspects that define the Pokemon games are:

  • Collecting Pokemon, by catching them in-game and by trading with friends
  • Creating teams and strategies for battles in-game, against friends, and in global tournaments
  • In-game, RPG-style world to explore while playing through a storyline
  • Willingness to explore new technologies as peripheral game components, such as incorporating physical activity, augmented reality, etc.
Though Pokemon has experimented with a variety of non-battle-based ideas such as dressing up the Pokemon, decorating a secret base, and participating in Beauty Contests to earn ribbons, none of the concepts have succeeded in the game (e.g. survived for more than one generation). Pokemon tends to be more popular with males (e.g., see GL stats).

The second is Neopets, which is a very popular online community launched in 1999 that reached 1 trillion page views in 2011. In Neopets, a player adopts imaginary pets and is responsible for feeding and training them. Playing a variety of simple flash games earns points to buy food, clothes, etc. for the pets. A number of more recent games have emulated Neopets including Subeta, Club Penguin, and Webkinz. Most important features of Neopets are:
  • Customizing the look of the pet to express personality and style
  • Earning trophies by participating in site events, submitting user-created content (e.g., drawings, stories), and earning high scores in Flash games
  • Socially interacting with other players through moderated forums or in-game email-like messages (when over 13 or with parental consent)
  • Creating and customizing homes for pets, shops, and galleries
  • Collecting avatars, stamps, and more
  • Battling pets event single-player challengers or friends in the Battledome

Though Neopets has tried to branch out onto the mobile platform with spin-off games like Lutari Island and Puzzle Adventure for DS, neither of these approaches were particularly successful. Currently, it is very difficult to participate in Neopets on a mobile device. One note of interest is that most Neopets players are female.

Overall, I’d say that Neopets is more focused on self-expression and social interaction, while Pokemon is more focused on strategy and competition, but there are four elements that these games share in common:

  • Focus on collecting — in Pokemon this is a key feature of the game; in Neopets it was not designed as a core feature but has become so over time with the introduction of stamps and avatars
  • Pets — something about the idea of caring for and training a pet that seems to be consistently engaging to children
  • Activities outside the game that help succeed in the game — in Pokemon, these activities take shape of attending tournaments and using pedometer feature; in Neopets, these activities take shape of creating drawings, sculptures, and stories for in-game contests.
  • Many ways to succeed — in Pokemon, you can focus on exploring, collecting, training, breeding, or battling Pokemon in underused, overused, and uber tiers; in Neopets, you can focus on exploring, customizing, collecting, trading, drawing, writing, and playing games. There are some general heuristics for what it means to be a successful player in either game, but its hard to directly compare two people. Each player is just different.

I don’t think any of these points are new, but I think that examining Neopets and Pokemon as two successful case studies can confirm that indeed these strategies work.

Review of Rethinking Statistical Analysis Methods for CHI

I’m starting to slog through the thick pile of papers that look interesting from CHI 2012. I wanted to start with the heavier stuff while I have the energy, so I began by looking at a paper discussing statistical methods for HCI. It helped that the first author was Maurits Kaptein, who is a great guy I met while studying abroad at TU/e in the Netherlands.

The basic gist of the paper is that HCI researchers frequently get 3 things wrong:

  1. We wrongly interpret the p-value as the probability of the null-hypothesis being true
  2. Our studies often lack statistical power to begin with, making it impossible to draw meaningful conclusions about rejecting the alt hypothesis
  3. We confuse statistical significance (e.g., p-values) with practical significance (i.e., actual difference meaningful to the real world)

Good stuff and I’m sure I’ve been guilty of both 1 and 2 frequently (I am usually fairly careful with #3). The authors don’t just point out the problem, but also give 7 suggestions for addressing this issue. The main point of this post is to critique these suggestions and perhaps give a few resources:

  1. Make bolder predictions of direction and magnitude of effects AND
  2. Predict the size of the effect likely to be found — both of these are easily said, but the problem is that HCI frequently bridges into areas uncharted by previous studies. We frequently just don’t know what the effects might be. Piloting is always useful, but frequently leads to results that are different from the deployment as most pilots are done with confederates (e.g. lab mates) to moderate the costs. Even small differences in how a study is set up or positioned could lead to HUGE changes in a field deployment (for examples, see the “Into the Wild” paper from last year)
  3. Calculate the number of participants required ahead of time — every time I have done this, the number has WAY exceeded what I actually had resources to do, but Maurits predicted this objection and suggests…
  4. Team up with other researchers to do multi-site experiments and pool results — I agree with this suggestion, though I wonder how to structure such collaborations in a community like CHI which values novelty over rigor (in my humble opinion). Maurits also suggests that we use valid and appropriate measurement instruments so that we can build on each others’ work. I agree with this SO hard that I’ve actually gone through the process of validating a questionnaire to use in evaluating the emotional aspects of communication technologies. It’s called the ABCCT and it is available freely (the final publication for this is still under review, but I could provide upon request).
  5. Use Bayesian analysis if you need to calculate the probability of the hypothesis given the data — this is great and it’s definitely new to me! To help others who are trying to learn this new way of doing stats, here are a few resources I’ve found online: (1) the section on Bayesian methods in statspages (a great resource in its own right) (2) a Bayesian t-test tutorial for R and (3) an online calculator for Bayes Factor. I still need to figure out how to put all this stuff together for the actual work that I do… What do I do with non-parametric data, for example? If somebody would write a step-by-step online tutorial for HCI researchers, I would give major kudos!
  6. Encourage researchers, reviewers, etc. to raise the standard of reporting statistical results — my translation is “reject papers that get it wrong” which is depressing. I think this would be a lot easier to do in the new CSCW-ish model of reviewing where you have a revise cycle. That way you can actually encourage people to learn it rather than just take their (otherwise interesting work) elsewhere
  7. Interpret the non-standardized sizes of the estimated effect — with this I agree unequivocally and I’d actually like to add one more point to this idea of “considering if saving 1 minute actually significant to anybody.” As HCI researchers, we are usually the ones designing the intervention so we have a fairly good idea of how difficult it would be to incorporate into existing practices. For example, fiddling slightly with the rankings produced by a search algorithm or changing the layout of a site or adding a new widget to an existing system is all fairly low effort, so even if the effect size of the produced outcome is small, it may be worth adopting. Changing your company to a new email system, changing the work flow of an existing organization, or making new hardware get adopted is really high effort, so it’s really only worth considering if the effect size of the produced outcome is quite large.

All in all, I really like this paper and its suggestions, but just to cause some intrigue I would like to point to a slightly different discussion issue. The main goal of this paper seems to be to lead HCI researchers to doing better science. But, is that really what we do? Do all HCI researchers consider themselves scientists? I know that for me it is not the most important part of my identity. I run studies as a designer. The goal for me is not to convince others that A is better than B (A and B so frequently shift and evolve that this is usually a meaningless comparison 2 years after the study is run). Rather, I run studies to understand what aspects of A may make it better than B in what situations and what future directions may be promising (and unpromising) for design. To me, the study is just an expensive design method. The consequences of “getting it wrong,” in the worst case, is spending time exploring a design direction which in the end turns out to be less interesting. There’s rarely an actual optimal design to be found. It’s all just me poking at single points in a large 3D space of possibilities. Should you reject my paper because I didn’t get the number of participants right (which I never will) even if it can inspire others to move towards promising new designs? Just because I didn’t prove it, doesn’t mean that there isn’t something interesting there anyway. Maybe, a large proportion of HCI studies are meant to be sketches rather than masterpieces.