First step is to install NoteScraper for Evernote. Once that’s done, I use the following steps:

1. Log into your Kindle account at http://kindle.amazon.com/ using Safari.
2. Click on the link to your books (https://kindle.amazon.com/your_reading).
3. Locate the book with notes you want to export in that list and click the title of it.
4. Scroll down to where notes start and choose “show your highlights only” (this also shows your notes).
5. Assuming NoteScraper for Evernote is correctly installed and the Apple global Script Menu is visible on the top menu bar, choose “Export Kindle notes to Evernote”.
6. You’ll be asked for some tags, a notebook to add it to (Kindle Notes), and whether you want each note to have its own note.
7. Done. It’s in Evernote now.

It can then be copied and pasted into DevonThink (the tool I use) or other electronic journal or writing tools you may use.

Don’t use a Mac? You can perform the first four steps and then manually copy the content from the web page to wherever you like, but formatting and appearance won’t likely be as nice.

I’ve quickly posted this based on my how-to in my own research journal in response to a Twitter question by Catulla. I’ll add some illustrative screenshots later.

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Image: I can just imagine this Blood Furnace boss exhorting people “L2P!” as he kills them over and over.

It is day two of the writing regime. Today’s plan is writing 750 words, writing CMA feedback, and working on the paper. I was musing last night about the approach to the paper, thinking that having an abstract or an introduction actually makes it easier to write because it provides a focus for the paper’s direction. I have heard other people say that it makes sense to leave the introduction to the last because then you know what you’ve written. I think the former approach might be more sensible for me. I can always go back and revise the introduction if it does not reflect what I end up doing. Focus, however, is priceless.

In addition to an introduction or an abstract, a title might also help. I was experimenting with variants of “L2P! Learn To Play Or…”. I thought that was clever, as it’s something you often see more experienced, impatient players saying to players who they think are not living up to their expectations in terms of expertise or speed. In the context of my work, however, it probably makes more sense to say “P2L! Play To Learn”, but I’m not sure how many people will get that. Nevertheless, a title is a starting point. I had both before I started my keynote writing and that turned out well. Perhaps I can incorporate the factoid into the abstract.

Abstract:

“L2P! L2P!” This is the exhortation you might encounter in massively multiplayer online games (MMOGs) when other players around you believe your skill or speed in playing is inadequate. It means “learn to play”. In this paper, we demonstrate how L2P has been turned on its axis to yoke the trials of play to the game of learning. 39 World of Warcraft players primarily playing in Europe submitted essays answering the question “Why do you play World of Warcraft?” in a 2010 study.

Using a grounded theory approach and discourse analysis, the essays were analyzed to ascertain the contributors’ motivations for playing and their reasons for persisting in playing. Yee’s player motivational framework subcomponents (Yee 2005; Yee 2006) were applied to each essay and contrasted with Bartle’s original player typology (Bartle 1996; Bartle 2003) in aggregate to determine overall, general motives these players had. While participants were not asked to write explicitly about learning and many did not provide any examples, several contributions are examined here as case studies of mundane and unusual examples, illustrating what these adults are playing to learn–a learning that goes beyond dungeons, dragons, and dwarves.

That does not seem too bad as a first go. I need to check on the discourse analysis; it might not be completely true. I also have no idea how I am going to write up the grounded theory bit appropriately, but at least that is accurate. I definitely followed that kind of approach in tagging the essays. I need to find some time to pore through the James Paul Gee’s book on discourse analysis. I just saw someone else in #phdchat mention it again yesterday. It keeps cropping up and I keep not reading it, even after I went to buy it and then realized I already had. That is trying to tell me something, if I would only listen. I also need to check on what to call Yee’s framework.

References:

Bartle, R. (1996) ‘Hearts, Clubs, Diamonds, Spades: Players Who Suit MUDs’, Journal of MUD Research, 1 (1). Also available from: http://www.mud.co.uk/richard/hcds.htm (Accessed April 22, 2011).

Bartle, R. (2003) Designing Virtual Worlds. New Riders Publishing.

Yee, N. (2005) A Model of Player Motivations, [online] Daedalus Project. Available from: http://www.nickyee.com/daedalus/archives/001298.php?page=1 (Accessed March 31, 2011).

Yee, N. (2006) ‘Motivations for Play in Online Games’, CyberPsychology & Behavior, 9 (6), pp:772-775. Also available from: http://www.liebertonline.com/doi/abs/10.1089/cpb.2006.9.772 (Accessed March 31, 2011).

Image: Photograph of street near Elephant and Castle on a rainy day in London through rain-streaked window

I’m about halfway through my initial coding of the motivation essays collected last April.  I should have been done this months ago, but I’ve somehow been scared to do it.  I think the big reason behind that is I’m afraid that I’m doing it or will do it incorrectly.  As I am going through and creating codes, I cannot help but feel that I am not always focussing on the motivation issue, which is the primary question. I am generally coding for content or themes I see appearing in the essays.  As an example, an essay may express that the author is more likely to assist someone else if they feel that other person has put some effort and thought into their character.  That is not their motivation for playing, but I have still created a code for it as “assist others”.  When I get to the end and review the list, I will not be able to tell which ones refer to motivation.  Some probably are where a participant has expressed it as a motivation, but other instances, even of the same code, might just be a theme that was raised.

At the moment, I have the following free nodes in NVivo:

Feeling a little insecure, I thought it might be time to consult a book I bought late last year but had yet to open: The Coding Manual for Qualitative Researchers by Johnny Saldaña (2009).  While I have many books now on research methods and specifically on qualitative analysis, I have found it difficult to get a grasp on the mechanics of coding.  I am somewhat reassured to read in the first chapter that “Rarely will anyone get coding right the first time” (p.10).

Saldaña differentiates between themes and codes, based on work of Rossman & Rallis: “think of a category as a word or phrase describing some segment of your data that is explicit, whereas a theme is a phrase or sentence describing some more subtle and tacit processes.” (Saldaña 2009, p. 13, his emphasis).  He goes on to say that “SECURITY can be a code, but A FALSE SENSE OF SECURITY can be a theme.”   He recommends avoiding coding thematically initially and to instead note potential themes down in an analytic memo.

In examining my list, aren’t most of my existing codes themes rather than categories, even if they’re a single word?  Maybe not necessarily.  If an essay’s author says they play World of Warcraft as stress relief, “stress relief” is an explicit thing.  That’s a category?   I am still unsure.  For the moment, I think I will continue on as I am.  This is only the first iteration and I can always improve it later.  However, I think I should start explicit coding some passages as “motivation” to delineate it from other points of interest that may also arise within a given essay and then go back and do the same for essays prior to case S1-028.

I suspected I was deviating from the main goals of the survey while doing my coding.  Saldaña addresses this by supporting the recommendation of Auerbach & Silverstein to make a one-page  summary of your research concerns, central research question, theoretical framework, goals of the study, and any other major issues (Saldaña 2009, p.18).  Then, keep that in front of you to aid you in maintaining your focus during coding.  Some questions were suggested as being applicable to coding field notes for all research by Emerson, Fretz, & Shaw (quoted in Saldaña 2009, p. 18):

1. What are people doing?
2. How, exactly, do they do this?  What specific means and/or strategies do they use?
3. How do members talk about, characterize, and understand what is going on?
4. What assumptions are they making?
5. What do I see going on here?  What did I learn from these notes?
6. What did I include them?

I have trouble seeing the applicability of those questions to my current task.  I do, however, agree with Saldaña’s addition of “What strikes you?”, suggested by Creswell (Saldaña, 2009, p.18).  I suspect it is that question that helps save all my existing work from having been useless, even if I did forget the purpose behind the study at times.

One thing I know I have not done is be rigorous about the codebook or code list.  MacQueen (quoted in Saldaña 2009, p. 21) recommends that a codebook entry should contain “the code, a brief definition, a full definition, guidelines for when to use the code, guidelines for when not to use the code, and examples.”  As I have created codes, I usually have not done any of that, although the odd one here or there has a brief description.  I have a plan to go back and “clean up” the codes.  For example, some codes need to be merged, like “exploration” and “exploring”.  Perhaps I can review how the codes have been used and write up descriptions for them at that point as well.

At the moment, I feel very much like the person looking through a rain-streaked window: everything is distorted and unclear. If I persevere, the hope is eventually the rain will stop and the streaks will fade away.

References:

Saldaña, J. (2009) The Coding Manual for Qualitative Researchers, London, United Kingdom, Sage Publications Ltd.

The requirements to complete the “Insane in the Membrane” achievement.
Image from WoWWiki (2009)

I want to say something about the kinds of motivations people have for playing World of Warcraft. Specifically, I want to enumerate factors that motivate players to persist in the game even when it involves tasks that are repetitive, boring, or seemingly impossibly long.

For example, there’s an achievement in World of Warcraft called “Insane in the Membrane” that gives the completer a reward of an in-game title of “The Insane.” This achievement requires you to raise your reputation points with different game factions to exalted, the highest level. Generally, you need about 21,000 points to reach exalted. Points are gained by completing quests, collecting and turning in items, or sometimes killing certain types of things. If you only had to gain exalted reputation with one or two factions, this would not be difficult. However, you need to do this with eight different factions, most of which are not factions you would be accruing large amounts of reputation with during the normal course of play.

To increase the difficulty, several of the factions involved have rival factions. With those factions, as you gain reputation with one, you lose reputation points with the rival faction, making the process of completing this achievement complex in addition to time-consuming. The WoWWiki (2009) page describes some strategies for completing this achievement and the complexities of the faction-rival relationships.

Most tasks players undertake are not going to be as complex, time-consuming, or mind-numbing to complete as the aptly-named “Insane in the Membrane”. There are, however, many smaller day-to-day activities necessary for successful raiding or to get some particular piece of gear, such as doing daily quests to earn gold, or harvesting materials for potions or enchantments, or completing instance and after instance to get badge rewards or reputation rewards. I’m making it sound like getting achievements or gear is the be-all, end-all, but I think the situation is more complex than that. It’s that hypothesis I want to verify.

Other things I would like to be able to comment on include the relationships between gender and motivation, or motivation and age, or possibly even motivation and nationality. I do not necessarily believe there will be a relationship between motivation and nationality necessarily, but how can you definitively say if you do not look for the correlation? That gives me the following questions I want answered:

1. What motivates people to play World of Warcraft?
2. What motivates people to persist in very boring or difficult tasks?
3. Is there a relationship between gender and stated motivations? If so, what is it?
4. Is there a relationship between age and stated motivations? If so, what is it?
5. Is there a relationship between nationality and stated motivations? If so, what is it?
6. Is there a relationship between character roles and classes and motivation?

With those six questions in mind and the original study idea of determining motivation via analysis of free-form essays about motivation, I can now go ahead and develop the specific survey questions that will help elicit data to answer those questions.

Going back to considering my approach-whether I should start with planning versus start with survey question-it was not as clearcut as I expected. By starting with some potential survey questions and then thinking about the answers I would get from them, I gained a better idea about what answers I wanted, a kind of iterative development process. Sometimes putting the horse first helps you know where and how to put the cart!

References:

WoWWiki. (2009) Insane in the Membrane, [online] WoWWiki. Available from http://www.wowwiki.com/Insane_in_The_Membrane (Accessed November 6, 2009).

Doc1	Doc2	Doc3	Doc4
Doc1	100	76	18	91
Doc2	76	100	22	35
Doc3	18	22	100	65
Doc4	91	34	65	100

This is not too dissimilar from the labelled diagram, part of the MATLAB documentation on data importing. Except that, if you look at the table below it, which describes which functions to use, they don’t have a function with a similar example to their labelled diagram. Early on I thought I should be able to use dlmread, which allows you specify rows/columns for starting points or a range. My idea was just to have a range which excluded the non-numeric troublesome labels. No matter what I did, though, I could not get it to work. It was frustrating, because I could paste the data into the Import Wizard and that could handle the data fine. I wrote people, I researched on the web, and I tried all sorts of things.

Eventually, I came full-circle back to dlmread and experimented by making a small data file with unrelated data in it. That worked fine. So I then copied half of one of my data tables into the test file and tried that. That also worked fine. I copied the whole data table into the test file and used dlmread on it. It worked fine! What was the difference between the two identical data files other than their filenames? When I uncovered the answer to that, I kicked myself. My data files were generated years ago and stored on my Mac OS 9-based laptop. My laptop and the data have since migrated to Apple’s swoopy BSD-based UNIX goodness and that’s the environment that MATLAB runs under. So… Have you guessed the problem? Yes, it was linefeeds! The data files had original Mac linefeeds and MATLAB wanted UNIX linefeeds. D’oh! It just goes to reaffirm that the things you don’t see can really hurt you.

Once that was solved, work proceded rapidly apace as I was now able to finish automating the whole comparison process from start to finish.

function  [Anal1Raw, Anal2Raw, Anal1MDS, Anal2MDS, fit] =
processEinCiteData(firstFile, secondFile, runName, labels)
% Read in the similarity matrices from the two data files
Anal1Raw = dlmread(firstFile, '\t', 1, 1);
Anal2Raw = dlmread(secondFile, '\t', 1, 1);
% Set up default document name labels if we didn't get any
if nargin < 4
labels = {'g4c', 'pp1', 'pp2', 'msc', 'pl1', 'pl2', 'pl3', 'sp1', 'sp2', 'ac1', 'ac2', 'bws'};
if nargin < 3
runName = '';
end
end
% Set up labels for the filenames
fileName1 = regexprep(firstFile, '\..*$', '');
fileName2 = regexprep(secondFile, '\..*$', '');
% Convert the similarity data to numbers below 1 for use in MDS
Anal1Raw = abs(100 - Anal1Raw)
Anal2Raw = abs(100 - Anal2Raw)
% Calculate the MDS and prepare a diagram showing the
% clusterings for the first document
[Anal1MDS, eigvals] = cmdscale(Anal1Raw);
figure(1);
plot(1:length(eigvals),eigvals,'bo-');
graph2d.constantline(0,'LineStyle',':','Color',[.7 .7 .7]);
axis([1,length(eigvals),min(eigvals),max(eigvals)*1.1]);
xlabel('Eigenvalue number');
ylabel('Eigenvalue');
plot(Anal1MDS(:,1),Anal1MDS(:,2),'bo', 'MarkerFaceColor', 'b', 'MarkerSize', 10);
axis(max(max(abs(Anal1MDS))) * [-1.1,1.1,-1.1,1.1]); axis('square');
text(Anal1MDS(:,1)+1.5,Anal1MDS(:,2),labels,'HorizontalAlignment','left');
hx = graph2d.constantline(0,'LineStyle','-','Color',[.7 .7 .7]);
hx = changedependvar(hx,'x');
hy = graph2d.constantline(0,'LineStyle','-','Color',[.7 .7 .7]);
hy = changedependvar(hy,'y');
title(['\fontname{lucida}\fontsize{18}' fileName1 ' MDS']);
xlabel(['\fontname{lucida}\fontsize{14}' runName ' on ' date], 'FontWeight', 'bold');
% Calculate the MDS and prepare a diagram showing the
% clusterings for the second document
[Anal2MDS, eigvals] = cmdscale(Anal2Raw);
figure(2);
plot(1:length(eigvals),eigvals,'rd-');
graph2d.constantline(0,'LineStyle',':','Color',[.7 .7 .7]);
axis([1,length(eigvals),min(eigvals),max(eigvals)*1.1]);
xlabel('Eigenvalue number');
ylabel('Eigenvalue');
plot(Anal2MDS(:,1),Anal2MDS(:,2),'rd', 'MarkerFaceColor', 'r', 'MarkerSize', 10);
axis(max(max(abs(Anal2MDS))) * [-1.1,1.1,-1.1,1.1]); axis('square');
text(Anal2MDS(:,1)+1.5,Anal2MDS(:,2),labels,'HorizontalAlignment','left');
hx = graph2d.constantline(0,'LineStyle','-','Color',[.7 .7 .7]);
hx = changedependvar(hx,'x');
hy = graph2d.constantline(0,'LineStyle','-','Color',[.7 .7 .7]);
hy = changedependvar(hy,'y');
title(['\fontname{lucida}\fontsize{18}' fileName2 ' MDS']);
xlabel(['\fontname{lucida}\fontsize{14}' runName ' on ' date], 'FontWeight', 'bold');
% Apply Procrustes to the two MDS results to map them
% into the same vector space and prepare a plot of the
% result
[fit, Z, transform] = procrustes(Anal1MDS, Anal2MDS);
figure(3);
plot(Anal1MDS(:,1), Anal1MDS(:,2), 'bo','MarkerFaceColor', 'b', 'MarkerSize', 10);
hold on
plot(Z(:,1), Z(:,2), 'rd', 'MarkerFaceColor', 'r', 'MarkerSize', 10);
hold off
text(Anal1MDS(:,1)+1.5,Anal1MDS(:,2), labels, 'Color', 'b');
text(Z(:,1)+1.5,Z(:,2),labels, 'Color', 'r');
xlabel(['\fontname{lucida}\fontsize{14}' runName ' on ' date], 'FontWeight', 'bold');
ylabel(['\fontname{lucida}\fontsize{14}' 'fit = ' num2str(fit, '%2.4f')], 'FontWeight', 'bold');
titleStr = ['\fontname{lucida}\fontsize{18}' fileName1 ...
' compared to ' fileName2];
title(titleStr, 'HorizontalAlignment', 'center', ...
'VerticalAlignment', 'bottom');
legend({firstFile, secondFile}, 4);

At the end, I had a quantitative number, the degree of fit, between two diagrams after applying the Procrustes Rotation to them. Finally! On a whim, I fed in the same data table as both arguments to my comparison program. That is, I compared the same data file to itself. My hypothesis was that the resultant degree of fit should be either 0 or 1 (depending on which the fitness was measured). Much to my surprise, no matter which data file I used, the result was never 0 or 1. My previous Procrustes Analysis code was taken from some sample code in the MATLAB documentation and looked like: [D,Z] = procrustes(Anal1aMDS, Anal2aMDS(:,1:2)); That last bit in () is some kind of MATLAB scaling, which, being a novice to MATLAB, I didn’t realize. So, in fact, my two diagrams weren’t the same which is why I wasn’t getting a 100% degree of fit. I do not want to say how long it took me to narrow that down. Once I did, though, it looked like I was basically set and I was able to quickly produce some comparisons between my “weird” half-baked metric and the cosine normalization one. One small step for EinKind.

This is a delayed entry from May 12th, 2004.

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Everywhere I look now, the signs of the University’s decision to rearrange the school so that there no longer is a School of Cognitive and Computing Science are in obvious fruition. Even my building, previously known as COGs, is now just relegated to the not-so-glamorous Pevensey II. Printers have moved, the wireless network is finally launched to the public, and I cannot find the departmental support pages to save my life. Irrespective of the changes, it’s good to see that some things never change: Darren is still “almost done” his Ph.D., like he’s been for the last two years; and Rob is still pursuing his Ph.D. slowly but surely. I’m in good company, I figure.
I ran into Beate in the hallway and she seemed glad to see me. She’s also working part-time on her Ph.D., but she started several years after me. Nevertheless, I was quick to assure her that she would probably finish first. Shortly thereafter, I ran into Darren, too. He also seemed pleased I was back. It’s nice to be welcomed and assured that there was a slight bit of worry that I might have “slid in the depths of Brighton’s underbelly.” All in all, it’s good to be back.

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