This is an individual project to make your webpage more accessible. Learning goals include
Some of the basic rules for web accessibility
How to use an accessibility checker to assess whether a web page is accessible
How to fix accessibility problems
How to work within the constraints of end-user content editing tools and still make something accessible
This project has two phases.
Phase one: Assess problems
In phase one, you will assess problems with the web page you choose.
Picking a webpage
You can assess your own website, if you have one. If not, a next best option would be your personal social media site (such as your linked-in page). If you don’t have one, but use social media such as facebook and twitter you can assess your posts on one of those sites. Finally, if none of those are options, just pick any site you think makes sense.
Running an accessibility checker
Once you have selected a web page, you should run it through an accessibility checker. The WebAim accessibility checker, WAVE, is a great choice for many sites. However, if the site requires that you log in, you may need an alternative. A great choice is the Chrome plugin Axe.
What to bring to class from Phase one
You should not change anything about the website you selected before class. You should bring your accessibility checking results to class and have read them over. We will work together in class on addressing the problems you found.
Phase two: Fixing problems
In phase two, you will fix problems on the website you chose. We will talk about how to write alt text, set up proper header structures, simplify language, and what else is possible within the constraints of the technology you are using.
Taylor is a second-year PhD student in the Paul G Allen School of Computer Science and Engineering. She is advised by Professor Jennifer Mankoff. In 2017, she graduated from the University of California, Santa Cruz with bachelor’s degrees in Computer Engineering and Cognitive Science. She then earned her Masters in Human Computer Interaction from Rochester Institute of Technology in 2019.
Her research interests focus on trying to make fabrication more accessible for people with disabilities. Her prior research explored how to make the e-textile circuit development process more accessible for adults with intellectual disabilities. Her recent projects focus on understanding the kinds of difficulties that people with disabilities face while knitting, and developing technologies to help users overcome some of these difficulties.
The goal of your final project is to explore an accessibility issue in more depth than you’ve been able to do in our projects so far. In choosing this project, you may want to draw from personal expertise, literature, or user data should you have access to it.
Your final project will have three phases:
Proposal
Proposal: Your proposal be a slide deck with 5 slides that describe your
promise: How the world will be better based on your project
obstacle: Why we don’t have this already.
solution: How you will achieve the promise. This will most likely be primarily technical, such as a novel device.
related work: It should also include a related work section with at least 5 references showing some evidence for the importance of this problem.
timeline: Finally, it should include a timeline showing that this is feasible.
Development: We will check in on projects in part of class and/or office hours on a weekly basis to help provide guidance about progress on the milestones laid out in your timeline
Midterm Writeup
Midway through the project you will turn in a brief update to your project. This should included an up-to-date written version of your promise, obstacle and solution (1-3 paragraphs) and a related work section, also updated based on feedback (3-4 paragraphs). The total should be less than a page long.
In addition, you will participate in a poster session.
Poster
Your poster should cover the same basic items as your report, but in much less depth. It should have a section highlighting the key goals of the project, images of what you did and/or pictures that convey study results if you did one, and some explanation of how you accomplished things, as well as mentioning how a disability studies perspective informed your project.
It does not need a related work section, and you will want to put your names on it and a big title.
Written Document
The report should cover these main sections:
Introduction — 1-3 paragraphs: Present the promise/ obstacle/ solution for your project — what is the problem are you solving and why is it important to solve it? This can re-use text from your midterm report.
Related Work — 3-4 paragraphs: Talk about relevant work that closely connects with your project. This can re-use text from your midterm report.
Methodology — about 1 page: What did you do in your project – If you worked with participants: how many people, what did they do. If you implemented a system, or designed something, what did you design?
Disability Studies Perspective – 1 Paragraph: How did a disability studies perspective inform your project
Conclusions — 1-2 paragraphs: describe what you learnt and how can this be extended/built on in the future
Personal reflection — 1-2 paragraphs, individual and handed in separately: describe what you personally learned from this project, and what your individual contributions were to the team.
Important notes and considerations
Language: You will be expected to use best practices in language and presentation. Here is the SIGACCESS guide on this.
The things we have emphasized in this class, namely a disability studies perspective and physical building, should be featured in your project as much as possible.
With respect to disability studies, you should think critically about whether and how your project empowers and gives agency to people with disabilities, as well as the extent to which it expects/engages the larger structural issues around the problem you’re trying to solve
With respect to physical computing, this is not required, but you should get approval from the instructor if you go in a different direction, and have a rationale
If you don’t have personal experience justifying the choice of problem, it is important to find studies that involved people with disabilities that help justify the sense of your proposed work. It is not feasible to do a full iterative design cycle in this project (and not necessarily an ethical use of the time of people with disabilities), but equally important not to come in with a ‘hero complex’ and simply believe you know what people need.
Your project can include designing and piloting a study, but only if you have significant experience already in this domain since we haven’t really taught that aspect of accessibility in this class. Better to spend time on skills you learned here! In addition, given the number of weeks available, be careful not to overcommit (e.g. creating a significant novel device and a lengthy study!)
Cura is the software yo ushould use. It has built in slicing, runs on macs and windows, and has pre-configured options for all Ultimaker models in the add-a-printer dialogue (instructions for adding a printer).
You will need to first export your model as an STL from OpenSCAD: First you render, not just preview, then you 3D print (the menu option just under Render in the image at right. You may need to debug your model. The result will be an STL file.
When you load an STL file into Cura, you then prepare your print. There are MANY options to consider, which are documented in detail on the ‘Mastering Cura’ webpage. Keep an eye on predicted print time
You saw in class how to start a print. First, save to GCODE from CURA. Then bring it to the Ultimaker. The Ultimaker resources I am linking to are part of a series (look for the arrows at bottom right and left of each page) that walks you through everything you need to make that first print. I’d recommend trying this out with something really small from the essential calibration set in our drive such as the thin wall box. It should be something that prints in 20 mins or less. You can also experiment with settings such as rafts and brims in that small format.
Vivian Genaro Motti, Assistant Professor, Information Sciences and Technology. Photo by: Ron Aira/Creative Services/George Mason University
I am an Assistant Professor on Human Computer Interaction at George Mason University where I lead the Human-Centric Design Lab. In the Fall 2019, I am a visiting scholar at the University of Washington’s Paul G. Allen School of Computer Science and Engineering. My research interests involve the design and evaluation of smartwatch applications to assist young adults with neurodiverse conditions. More specifically, I focus on how wearable applications can assist neurodiverse individuals with self-regulation, executive functions and activities of daily living.
I am also interested on usable privacy for smart home devices, wearables, accessibility and mHealth.
Avery is a Phd Student in the Paul G. Allen School of Computer Science and Engineering at the University of Washington. They are advised by Prof. Jennifer Mankoff. They completed their bachelors in Computer Science at the University of Illinois at Urbana-Champaign in 2019, where Prof. Aditya Parameswaran and Prof. Karrie Karahalios advised them. They are an NSF Fellow and an ARCS Scholar.
Their research focuses on applying computer science to create or improve technologies that serve people with disabilities. Their current work focuses on 1) representation of people with disabilities in digital technologies like avatars and generative AI tools, and 2) how to support people with fluctuating access needs like neurodiverse people and people with chronic or mental health conditions.
Feelings of loneliness are associated with poor physical and mental health. Detection of loneliness through passive sensing on personal devices can lead to the development of interventions aimed at decreasing rates of loneliness.
Objective: The aim of this study was to explore the potential of using passive sensing to infer levels of loneliness and to identify the corresponding behavioral patterns.
Methods: Data were collected from smartphones and Fitbits (Flex 2) of 160 college students over a semester. The participants completed the University of California, Los Angeles (UCLA) loneliness questionnaire at the beginning and end of the semester. For a classification purpose, the scores were categorized into high (questionnaire score>40) and low (≤40) levels of loneliness. Daily features were extracted from both devices to capture activity and mobility, communication and phone usage, and sleep behaviors. The features were then averaged to generate semester-level features. We used 3 analytic methods: (1) statistical analysis to provide an overview of loneliness in college students, (2) data mining using the Apriori algorithm to extract behavior patterns associated with loneliness, and (3) machine learning classification to infer the level of loneliness and the change in levels of loneliness using an ensemble of gradient boosting and logistic regression algorithms with feature selection in a leave-one-student-out cross-validation manner.
Results: The average loneliness score from the presurveys and postsurveys was above 43 (presurvey SD 9.4 and postsurvey SD 10.4), and the majority of participants fell into the high loneliness category (scores above 40) with 63.8% (102/160) in the presurvey and 58.8% (94/160) in the postsurvey. Scores greater than 1 standard deviation above the mean were observed in 12.5% (20/160) of the participants in both pre- and postsurvey scores. The majority of scores, however, fell between 1 standard deviation below and above the mean (pre=66.9% [107/160] and post=73.1% [117/160]).
Our machine learning pipeline achieved an accuracy of 80.2% in detecting the binary level of loneliness and an 88.4% accuracy in detecting change in the loneliness level. The mining of associations between classifier-selected behavioral features and loneliness indicated that compared with students with low loneliness, students with high levels of loneliness were spending less time outside of campus during evening hours on weekends and spending less time in places for social events in the evening on weekdays (support=17% and confidence=92%). The analysis also indicated that more activity and less sedentary behavior, especially in the evening, was associated with a decrease in levels of loneliness from the beginning of the semester to the end of it (support=31% and confidence=92%).
Conclusions: Passive sensing has the potential for detecting loneliness in college students and identifying the associated behavioral patterns. These findings highlight intervention opportunities through mobile technology to reduce the impact of loneliness on individuals’ health and well-being.
The goal of this assignment is for you to develop basic familiarity with OpenSCAD. Your goal is to create a model of something that makes something more accessible for you or someone else. To keep this problem within reason for a first assignment, you should focus on things that are fairly simple to model. You should work in pairs on this assignment.
Examples would be a tactile label for something (such as a luggage tag), a guide (to make moving something along a path easier) or a lever (to make rotating something easier.
Your solution should be correctly sized (i.e. try to measure the thing you are modifying and to make sure that your printed object is appropriately sized).
You should use a simple method to attach things such as a zip-tie (simply requires small holes), or glue.
Your object should be small (be printable in 20 minutes to 2 hours)
You should create a Thingiverse “thing” which represents your object with a picture of your final object in use, your OpenSCAD file, and a picture of your model, along with a brief explanation of what problem it solves, how to correctly size it, how it attaches to or interacts with the real world. If you remixed something else on Thingiverse be sure to correctly attribute it (by creating a remix).
You should submit the link to your Thingiverse “thing” on Canvas.
The grading rubric for this assignment is as follows. When points are 1 or 0, this is pass fail (no nuance). When points are 0-3, use the following scale: 0 – Not done; 1 – Short shallow solution; 2 – Good solution; 3 – Outstanding solution.
Points
Description
Comments (by grader)
0-3
Create a 3D model that solves a problem
1 or 0
Learn how to correctly size a model
1 or 0
Apply an appropriate attachment method
1 or 0
Learn the pipeline: Create a 3D printed object from your model
Setting up your bluefruit is fairly straightforward, but there are a couple of things you will need to do. They are (almost) all documented on the AdaFruit website BlueFruit page. Some things you will need to do:
Open Preferences and put ‘https://adafruit.github.io/arduino-board-index/package_adafruit_index.json’ in the Additional Boards Manager URL
Open Tools>Board…>Board Manager
Click on Adafruit nRF52 and click ‘Install’
Quit and re-open the Arduino IDE
Check if you have succeeded. You should be able to select the Bluefruit board from the Boards menu, select the correct port from Tools>Port and upload a sketch!
OS-specific Install Instructions
If you are on a mac, you will additionally need to install the USB to UART bridge drivers provided by Silabs. Be sure (within 30 minutes of install) to approve it in the Security and Privacy settings for your mac (you’ll see a button for this below “Allow apps downloaded from…”).
If you are Windows, you may need to install a driver (I did not have to on the windows machine in our classroom).
Check if you have succeeded. You should see a USB port in your Arduino Ports menu
You’ll want the Bluefruit libraries and sample code. Go to Tools>Manage Libraries and search for bluefruit. Install the Adafruit BluefruitLE nRF51 suite
Sketches used in class
You can download the sketches used in our class from our class google drive, arduino folder.
Improving mobile keyboard typing speed increases in value as more tasks move to a mobile setting. Autocorrect is a powerful way to reduce the time it takes to manually fix typing errors, which results in typing speed increase. However, recent user studies of autocorrect uncovered an unexplored side-effect: participants’ aversion to typing errors despite autocorrect. We present the first computational model of typing on keyboards with autocorrect, which enables precise study of expert typists’ aversion to typing errors on such keyboards. Unlike empirical typing studies that last days, our model evaluates the effects of typists’ aversion to typing errors for any autocorrect accuracy in seconds. We show that typists’ aversion to typing errors adds a self-imposed limit on upper bound typing speeds, which decreases the value of highly accurate autocorrect. Our findings motivate future designs of keyboards with autocorrect that reduce typists’ aversion to typing errors to increase typing speeds.
The Limits of Expert Text Entry Speed on Mobile Keyboards with Autocorrect Nikola Banovic, Ticha Sethapakdi, Yasasvi Hari, Anind K. Dey, Jennifer Mankoff. Mobile HCI 2019.
An example mobile device with a soft keyboard: A) text entry area, which in our study contained study progress, the current phrase to transcribe, and an area for transcribed characters, B) automatically suggested words, and C) a miniQWERTY soft keyboard with autocorrect.
Our model estimated expected mean typing speeds (lines) for different levels of typing error rate aversion (e) compared to mean empirical typing speed with automatic correction and suggestion (bar plot) in WPM across Accuracy. Error bars represent 95% confidence intervals.Median empirical error rates across Accuracy in session 3 with automated correction and suggestion. Error bars represent minimum and maximum error rate values, and dots represent outliers
