Surf*Mind*Musings

I’m very excited to be participating in the Mozilla Education JetPack/AddOn Design Challenge as an instructor in Phase 2 of the challenge. Thankfully, we’ve got Brian King along for technical heavy lifting on the code side. I’ll be focused on helping challenge participants create positive user experiences for learning.

All of the basic rules of HCI apply: keep the user informed, match between system and real world, recognition not recall, speak the users language, provide shortcuts, etc. Then we get a whole slew of new concerns around facilitating learning.

I see great opportunities for browser addons to aid sense-making in the early stages of learning a space as well as in elaboration and chunking. Chunking is how humans get beyond the limits of short term memory (the common 7 plus or minus 2 rule) to create richer mental representations.

Even more so, learning ehancements could facilitate connections between people with shared learning objectives, enable novice-expert interactions, and create other social opportunities abound beyond the more obvious rehearsal tools (e.g. flashcards) and ease of information access utilities. Getting the right information at the right time is critically important for the integration of new knowledge into existing understanding.

The focus of the design challenge is on JetPacks but full firefox extensions can also be entered. While add-ons have a huge range of user interface options, the JetPack solution set of UI is more constrained. This should be a nice sandbox for UI patterns. I’ll be teaching a couple classes along the way to a finalist workshop preceding SXSW Interactive.

I’ve got one consumer ready JetPack out there for rotating tabs for a display kiosk but look forward to crafting many more! I’m even dabbling with a multi-touch enabled Firefox on a HP tx2 tablet w/ Windows 7.

Check out the official challenge website for more info. You’ve only got a month to make your submissions. I’ll see the winners in Austin right before SXSWi.

Continuing my Mozilla Test Pilot tabs data analysis informed by some official work…. I’ve started to work with time and the event sequencing of tab usage. This involved significant data transformations, see below.

Timing & Event Sequences

Here’s a look at what happens *after* a page loads in a firefox tab:


~83% of the time another page is loaded with an average view time of 6 seconds. Opening a new tab makes that view time shorter. Closing a window is a much more deliberative action than closing a tab, averaging ~60 seconds
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Timing & Sessions

An early study of Netscape 1.0 browser files determined that browsing episodes tend to fall within 20 minutes. This finding seems to hold true today as indicated by the graph inflection point and has technical bearing on in-memory cookie retention.

The graph is a zoom on the 80% of sessions under 90 minutes. We see 75% of session fall within half an hour. There’s a definite research opportunity to explore overall versus site versus task session durations, though this data can only inform on site and overall duration.

Getting to the Nitty Gritty

At some point in a complex data analysis it becomes useful to go down to the individual data and make sure you understand what’s happening. Here’s a view of a single user engaged in a very length 4 hour browsing session.

The table shows how this user compares to the average in terms of the frequency of top sequences. This user browses a bit more aggressively than the average user, loading more pages per tab. He or she also is more likely to load a new page when a switching to a tab. On the right is a visualization (click for all 4000px in height via image or view html) of the sequencing of these actions. Each column represents an open tab and the colored cell is the tab of current activity. Height increases beyond 20 seconds to a maximum of about 8 minutes in this viz.

It’s interesting that the visualization shows the user working orderly through tabs, largely moving a single tab at a time away from the current focus except when jumping to tab 1. I’m still working to fully understand what different types of transitions communicate around tab closes, open in new tab foreground and background, etc.

Here are a some static HTML views of sessions starting with a “tab addict” who’s carry around over a dozen tabs that don’t get visited in either this short or longer session. On the other hand, here’s an example of a quick 5 minute episode in which the user revisits all of his open tabs

Sharing

My R and MySQL code is on etherpad and I’ve made a 66mb MySQL database dump available with session tagging and sequenced data for the Vanilla 30 users.

There is a generic Vanilla 30% data table (vanilla30) and a table prepped for session grouping and self-joins for chaining (vanilla30seq). The live session visualizer and data-browser is not quite ready for sharing. I’ve also imported the TreeStyle data set.

The Mozilla Labs TestPilot project has just released it’s first round of data on tab usage. Getting started with a dataset begins with exploration, confirming basic hypotheses before getting fancy. Here’s an exploratory look at the “vanilla 30″ Test Pilot dataset with color coding on average tabs across a day done with GGobi.

high res version

Moving through this we can observe:

• **A long tail of average tabs, with an inflection around 20 tabs. 75% of users average under 8 tabs, the 50% mark is at 4.1 tabs. Note the color coding is explained by this first cell.
• **In the top right cell, we see a bimodal distribution, in which most users with high maxTabs have high average tabs, but a subset of “tab bingers” who more occasionally use large numbers of tabs. More below…
• **One puzzling aspect is the presence of 22 distinct values for day. Though the timespan of the study is one week, apparently the start and end times are not the same.

This dataset is pretty large, with 7749 points analyzed by day by user. I imported the data into mySQL and am using a hybrid of SQL and R to conduct exploration (see the code).

I moved to a more granular analysis of (the first 25,000 rows) of hourly averageTabs and maxTabs.Here we see the bimodal distribution more strongly. Some minority of users break with the general relationship between average tabs and max tabs. These are the “tab bingers” or alternatively speaking, the clean uppers, who go from few tabs to many and back to a few. Futher analyses will have to be done to identify the pattern here.

In fact, looking at the speed of change of open tabs for the 50% of heavy users with more than 4 average tabs open is one of the more intriguing opportunities discovered so far (see the # tabs per navigation action by Dubroy). This is possibly just an artifact of using average, as MySQL doesn’t have a median function, but it does suggest there may be spiky versus constant tab “junkies”.

My first tweet on this dataset said that 50% of users never go beyond 13 tabs. In fact, the number looks lower than that in this 50% subsample. Seven +-2 likely holds for number of tabs open for most users. There are two interesting exceptions to this rule, called out in the “projection pursuit” ggobi video below:

In the video, spiky tabbers are in grey and tab addicts are plotted with hollow squares:

It’s challenging to derive meaningful and concise conclusions from complex datasets like this one. To that end, I’m attempting with this analysis, like I did with the Places Stats project, to share my analysis code for open source collaboration. I’ve even done a video on using R + GGobi.

This first round of analysis barely touches the surface of the interesting aspects to this data set and suggests two tentative areas for further inquiry: Average tab users currently only use a handful of tabs, can we make life easier on them? Two types of users venture into the >10 tab world, habitual tabbers and spiky tabbers (e.g. addicts and bingers in my unPC terminology) — why?

Further work with this dataset is going to require more sequential analyses, walking the data to generate more granular metrics on growth and constriction of # of tabs, as well as looking at spawning methods and the role of windows. That doesn’t even begin to get into the folks who optimize tabs by using extensions! Avoiding averages is also critical in general and to connect to Dubroy’s thesis study. Does MySQL do UDFs?

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