What I Learned Today

James Paul Gee, a professor of Reading, decided to explore the world of video games. He picked up a copy of “The New Adventures of the Time Machine” (inspired by the work of H. G. Wells) and was surprised by its difficulty. “Lots of young people pay lots of money to engage in an activity that is hard, long, and complex […] and yet enjoy it,” he wrote in a paper titled “Good video games and good learning”. As an educator, he wondered what elements of these video games could be used to improve learning in a more conventional school setting.

Gee identified 16 learning principles that good (effective, successful) games use. I won’t go into all 16 here, but some are interesting to consider, especially in terms of how they might be adopted in schools or universities.

He posits that good games require the player to take on a new identity, inspiring “an extended commitment of self.” The game is a world that you at least partially inhabit. It’s personal. Learning in a class can be the same way, asking you to commit to see the world through the eyes of a physicist or a francophone or a mathematician. Personal investment changes your experience radically from memorizing facts to actively seeing the world in a new way.

Another important principle of effective games is that they give players the opportunity to experiment, and possibly fail, with a relatively low cost. Even if your player dies, you can restart the game. In traditional school environments, failure is often much more public and much more costly: feedback may come in the form of “that’s wrong” instead of “try again.”

Gee also posits that good games are “pleasantly frustrating” in that they keep you within, but right at the edge of, your “regime of competence.” Tasks are doable but challenging. I think this is my favorite regime in which to live, period: challenged but able to make some progress!

A final principle that caught my eye was what Gee calls “well-ordered problems.” Good games give you a series of problems to solve that provide a learning progression: easier tasks first that lead to more difficult ones. In the field of machine learning, some researchers have been studying the best way to present examples to a learner (human or machine). One theory that matches with Gee’s observation is called “curriculum learning”: start with easy examples and progress to more subtle or nuanced ones. Humans tend to use this kind of approach, which perplexes some machine learning researchers since it is provably better to first show the hardest or most ambiguous examples first, because they give you the most information. For example, if I wanted to teach you how to tell whether a child is tall enough to ride a roller coaster, I might point to a boy who is 35 inches tall and say “he’s too short,” and then point to a girl who is 36 inches tall and say “she’s tall enough.” Curriculum learning instead would give you examples like “that man who is 6’4″ is tall enough” and “the 21-inch infant is too short” and only gradually work their way to the harder examples closer to the threshold. However, many learning problems aren’t easy to map to a linear scale in which you just want to pinpoint a threshold, and in those cases, curriculum learning seems to be more natural and more effective.

What else can we learn about learning from how games are designed? Do game designers know something that educators don’t? Not necessarily — but their incentive structure is different, which may lead them to create new kinds of playing, and learning, environments that educators can borrow from.

All games are not created equal, and neither are all gamers. Ito and Bittanti, in chapter 5 of “Hanging Out, Messing Around, and Geeking Out: Kids Living and Learning with New Media,” identify five different “genres of game playing” that describe different motivations and modes of play. They are:

1. Killing time: playing a game while you wait or because you’re bored or want to be distracted (e.g., crossword puzzles, solitaire, Minesweeper)
2. Hanging out: playing a game to connect with other people socially (e.g., party games, board games, Rock Band, bridge)
3. Recreational gaming: playing a game for the sake of the game (e.g., first-person shooters or really anything you get immersed in)
4. Organizing and mobilizing: playing a game that’s grown into a formal structure (e.g., being the dungeonmaster for a D&D game or a guild leader in MMORPGs)
5. Augmented game play: playing a game and creating additional “paratexts” around the game, such as fan sites, hacks, walk-throughs, cheat codes, or a focus on the creative element of the game (player customization, campaign design, etc.).

Player investment in (and passion for) the game increases along the list, from killing time up to augmented game play.

While some games seem to associate directly with a particular game playing genre, there isn’t a strict mapping. For example, players can enjoy World of Warcraft in any of these modes, depending on their interest in the game, their technical prowess, and their current mood. Bridge can be played socially, with ample table talk, or in a cutthroat competitive mode in which silence reigns outside of the bids. A player’s current genre might even change during the course of a game playing session.

Reflecting on my own game playing, I am not sure I have a particular preferred genre. The time I spend playing games today is limited, due to other demands on my time, and therefore limited to the “hanging out” genre (occasional board games or video games with friends). But during my first year in college, I discovered online communal role-playing games (the text-based predecessors of today’s MMORPGs), and that experience ranged over most of the five genres.

I was quickly captivated by the Pern-based games in which you could create a character who had the chance to be chosen as a dragonrider — every Anne McCaffrey fan’s dream. I spent hours developing my character and role-playing with other people on the game. I was bowled over by the idea of a bunch of people getting together to effectively write a collaborative novel in realtime (!).

Rather than progressing from a social to technical to creative motivation (as suggested if you view the genre list as a progression), it was the creative element that drew me in first (augmented game play). My interest in programming inspired me to learn how to create custom interactive in-game objects. As I developed friendships with other players, the social aspect (hanging out) became more present; sometimes the role-playing would taper off while the players engaged in “out of character” discussions on communication channels that weren’t part of the in-game play but were still social. As my investment and expertise grew, I became more involved in the organization part of the game: helping run large-scale events (such as dragon egg Hatchings) and creating names and descriptions for the next batch of dragons. Eventually, real life constraints placed limits on how much time I could invest in the game, and I moved on to other things.

I would not be surprised if most people find their engagement with any particular game to move between genres as I’ve described here. Over time, what interests you most about a game (and keeps you coming back) may change, due to your own changes in expertise, or a simple desire for variety.

How would you categorize the way you play your favorite game?

I’m reading a book called Hanging Out, Messing Around, and Geeking Out for my library school class on makerspaces. The book is a compilation of studies of how today’s youth navigate, use, play, and grow up in virtual spaces in tandem with their interactions in physical space.

Early on, the book refers to “media ecology,” a new term for me. After some online investigation, I determined that it seems to refer to the study of how [communication] media facilitate interactions between people (an “ecology”). The wikipedia article on media ecology goes into far more depth on this, noting that the term was coined by Neil Postman in 1970. He wrote:

“Media ecology looks into the matter of how media of communication affect human perception, understanding, feeling, and value; and how our interaction with media facilitates or impedes our chances of survival.”

How does your use of Facebook facilitate friendships? Does Twitter trigger new ties? Does email elucidate and edify? But it’s not just about communication and reflection; Postman used the word “survival.” Well, consider: if people use new media to form new relationships, those networks can indeed play a role in survival: friends, romantic partners, support networks.

McLuhan, another media ecologist, proposed the media tetrad as a framework in which any medium (or technology) might be analyzed. It asks you to brainstorm about what the medium enhances or improves, what it renders obsolete, what it retrieves (brings back from the past), and what it does in reversal (bad effects, in the limit). I found that rather abstract; it makes much more sense if you consider some examples.

I practiced by making my own tetrad about in-car navigation systems, which

• generally improve the speed with which you reach your destination,
• render paper maps (and back-seat navigators) obsolete,
• retrieve confidence in finding places and not getting lost, and
• with frequent use may reverse into a degraded personal sense of direction and location.

But going back to the media ecology concept, the main idea is that of connections and interactions. I thought this observation from the book was particularly astute:

“One of the important outcomes of youth participation in many online practices is that they have an opportunity to interact with adults who are outside of their usual circle of family and school-based adult relationships” (Ito et al., 2010, p. 7)

That is, it’s not just about other youth they might connect with, but adults as well, who might become mentors, instructors, friends, or protectors. I wonder how many youth make online connections of this kind (yes, not the creepy kind). How might my teenage years have been different, with the Internet to hand? There’s a tetrad to be sketched, right there.

Imagine a public library, and what comes to mind? Perhaps: a collection of books neatly arranged on shelves, wrapped in silence. Of course, most libraries offer far more than just books (CDs, DVDs, audiobooks, magazines, etc.) and services far beyond just access to physical volumes (storytime, literacy tutoring, lectures, book signings, Internet access, reference services, etc.). As a canonical stereotype, however, the static archive of (possibly dusty) books persists.

But now there are efforts to turn libraries into places where you can create, not just consume. One example is the Idea Box at Oak Park Public Library. This is a glassed-in space with a different design each month that lures visitors in to make their own mark. For National Poetry Month, the room was coated with magnetic paint and patrons used magnetic word tiles to assemble their own poems. For National Novel Writing Month, patrons were invited to write short stories about Oak Park and pin them to a map based on where they took place. Another month, patrons were given a book that had been covered in blank paper and invited to decorate it as if it told the story of their lives.

What an opportunistic take on creativity! Many of us may think that writing poetry or short stories or creating art is something that you must set aside a specific time for, and that kind of planned-out creative time may never actually happen. But, drawn in by a moment’s curiosity, you may discover that, given the chance to play with words or chalk or golf tees, an author or artist or composer lives inside you, too. The barrier to entry is so low that you unwittingly step over it as you enter the room. How many patrons, in creating such an ephemeral work of art, walk away with a memory that will linger well beyond the next renovation of the room?

My favorite Idea Box to date is one in which you are encouraged to create your own constellation. Brilliant!

Libraries exist to facilitate learning. Reading is one way to gain knowledge, and active, creative play is another. I’m delighted to see libraries experimenting with a broader view that engages people and enriches their experience of learning.

I am taking a class titled “Information Retrieval” this semester. It covers general topics about how to organize information so that it can be easily searched, retrieved, and used later. Much of the content overlaps with previous coursework I’ve had on databases and machine learning, but with a different emphasis.

I’m really enjoying the assignments in the class so far. In the first one, we were given a collection of scanned postcards to analyze. We looked at them in batches of three (randomly selected), and then were asked to come up with an “attribute” that was true for two of the postcards but false for the third. After doing this 20 times, we each had a list of 20 descriptive postcard attributes; this process was referred to as “attribute elicitation.”

I was delighted! The representation question is at the heart of machine learning, too, but we rarely (if ever) are given the chance to MAKE UP THE ATTRIBUTES ourselves. (Unless, of course, it’s a data set that we’re creating, which is also rare.) I felt such freedom. At the same time, I realized that the objective wasn’t quite the same. In machine learning, you want a representation that maximizes your later ability to classify, cluster, or otherwise analyze the data. In library science, you want a representation that maximizes your later ability to find particular items that satisfy a query. This perhaps boils down to discriminability vs. findability.

This goes deeper than it may seem at first. Often the machine learning (ML) task at hand is one of classification, in which case the universe of classes of interest is known in advance. Each item can be assigned to one of those classes. The representation can be (and sometimes is) optimized to maximize performance in classifying the known classes of interest. One of the latest trends in ML is to use “deep learning” to manufacture the representation automatically.

For information retrieval, in the sense used by library science folks, the classes of interest are not known, nor is the goal to craft an automated classifier for future data. Instead, the system (and representation) should support a potentially unlimited variety of future user (human) queries about any of the items in the collection. Success is measured not by classification or clustering accuracy, but by how many queries successfully locate the desired item or items, and how easily this is achieved (from the user perspective).

Has anyone tried to apply deep learning to library collections? Would it be useful here?

There is a terminology shift between the fields, too. The process of deciding on a representation is called “attribute elicitation” in library science, not to be confused with “feature extraction” in ML, which means the (automated) calculating of feature *values*. That process (assigning attribute values to items), in turn, is called “indexing.” (After creating 20 attributes, we then indexed 10 postcards by filling in their values for each of the attributes.) In ML we generally don’t get to do that, either, and especially not in a manual fashion. It was fun!

Going through the attribute elicitation and item indexing process raised other questions for me. It quickly becomes obvious that some attributes are easier or faster to “compute” than others, even for humans doing the task. “Color image” vs. “not a color image” is an easy decision, but “picture of a French location” can be far more difficult, relying as it does on deeper domain knowledge and deeper analysis of the image.

Should we prefer those that are easier to compute, all other things being equal? If you assume human indexers, then it seems you’d also prefer attributes that are most likely to be consistently computed by different people. We talked briefly about the “indexing rules” (crafted by humans) that go along with any such representation, to help with consistency. However, there was no discussion about informativeness, discriminability, or other properties that would guide you in selecting the best attributes to use. Perhaps we’ll get to that later.

Our next task is a group exercise in creating a database catalog of any objects we like, other than books. My group has chosen candles, and we’re now discussing what the most useful attributes might be; what might one like to search on, when in need of a candle? Or candles?

We’re only required to input five (five!) items into the final database. If we manage to get a few more in there, I’m tempted to do a clustering or PCA analysis and examine the distribution of candles that we end up with. :)