Julie Kientz Research Group
Can Smartphone Usage Predict Sleep Status? (Spring 2014)
Sleep is an important component of health. With long-term, behavioral sleep issues such as insomnia, monitoring the amount of sleep you get can be an important part of helping to find the causes of the problems and work on a solution. Current sleep sensors often require on-the-body sensors and that the user must remember to put it on or turn it on every night, which can lead to high user burden and unreliable data. These issues make it difficult for users to learn about their sleep behaviors over the long term.
In this directed research group, we will be working on a new way of identifying sleep behaviors by looking at how people use their smart phones. For example, many people have the habit of charging their phone while they sleep, or the phone may not move for several consecutive hours. The work involved will be collecting data via phone usage logs as well as “ground truth” data via commercial sleep sensors and manual sleep diaries, and then applying machine learning techniques (using the Weka toolkit) to determine whether we can build a model that predicts a user’s sleep status. We already have access to an Android-based logging tool and commercial sleep sensors, so part of the work for this group will be to recruit participants, set up the logging tool on their phone, give them the sleep sensors, and collect information from them and analyze it.
Students participating would benefit from having some familiarity with Android phones, basic programming skills (in case we need to do any modifications to the logging tool), and quantitative data analysis skills (or a willingness to learn).
Meeting times for Spring 2014 are tentatively set for Wednesdays from 3:00-4:30 PM (although if there are enough interested students who need a different time, I may be willing to adjust that). If you are interested, please send along your resume and a short paragraph indicating your interest in the project to Professor Kientz at firstname.lastname@example.org.
This reading group will bring together students and faculty to read and discuss research papers relevant to the design, evaluation, and theory of games and video games. Each week, there will be two students assigned to choose and lead the group a discussion on a single paper they select. This will be a 1-credit hour course for HCDE 596, with 2 hours per week of reading and 1 hour per week of group discussion. The group will meet on Thursdays from 3:30-4:30 p.m. (Room TBA). This group will be co-led by Professor Julie Kientz and Ph.D. student John Porter. If you are interested in registering, please contact John Porter at email@example.com.
Developing a Validated Measure of User Burden (Autumn 2013)
In any interactive technology, there is often some amount of burden placed on the user that can prevent its use and adoption. Burdens can include mental, physical, emotional, financial, time, privacy, or access. For example, a food journaling application may induce too significant of a mental, time, and emotional burden on the user that may prevent adoption. If we have a better way of assessing these different burdens, we can hopefully design better systems.
Currently, other than just asking participants, there is no way of assessing user burden in a systematic or comparable way. Researchers have adopted scales such as the NASA Task Load Index (NASA-TLX), the System Usability Scale (SUS), or the Technology Acceptance Model (TAM) to assess technologies, but there is not yet a scale that assesses user burden.
The goal of this research group will be to develop and validate a scale for assessing user burden. We will begin the quarter by reading papers about other similar scales and how they have been validated and used. We will then brainstorm questions, refine them, and validate them using quantitative statistical approaches. A previous class in statistics or quantitative methods would be helpful, but a willingness to learn would also work.
Weekly meetings will take place on Mondays from 3:00 to 4:00 P.M. in Sieg 420.
If you are interested in participating, please email Julie Kientz (firstname.lastname@example.org) with a copy of your resume/C.V. and a short statement expressing your interests in the group.
Designing Computing Technology for Tracking Children's Developmental Progress (Winter & Spring 2013)
For Winter and Spring 2013 quarters, my directed research group will be working on prototyping and running a study of a system that uses social media to encourage developmental milestone tracking by parents of young children. This work will build upon user research already conducted by the Baby Steps project, funded by the National Science Foundation. We will use Twitter and/or Facebook to proactively prompt parents to track and respond to their young child's milestones such as taking their first steps, responding to verbal communication, and making eye contact. After designing a system for supporting prompting and data collection, we will the conduct a research study evaluating its effectiveness.
Research activities for each quarter will include:
Spring 2013—Meetings on Thursdays from 2:30–4:00
- Design a mechanism for using Twitter and/or Facebook to prompt on developmental milestones and collect and store parent responses
- Develop a functional prototype of social media application
- Debug and do usability testing on a functional prototype
- Design a field study for evaluating the prototype
Spring 2013—Meetings on Wednesdays from 2:00–3:30
- Execute on the study designed during Winter 2013 quarter
- Recruit participants and conduct interviews/study procedures
- Collect and analyze data
- Write up study results and submit for publication
If you are interested in participating, please complete the Catalyst survey by December 3. I will notify participants and provide add codes by December 7. If there are more people interested in participating than we have room to accommodate, I will prioritize based on relevant skill sets (or a willingness to learn) and the ability to commit for both quarters. Students with Python, web programming, and/or database skills are especially needed for Winter 2013 quarter. Students with experience working with user populations, conducting user testing, and doing data analysis are especially needed for Spring 2013 quarter.