Archives

Automatically Tracking and Executing Green Actions

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We believe that self-reporting is a limiting factor in the original vision of StepGreen.org, and this component of our research has begun to explore alternatives. For example, we showed that financial data can be used to extract footprint information [1], and in collaboration with researchers at Intel and University of Washington, we used a mobile device to track and visualize green transportation behavior in the Ubigreen project (published at CHI 2009 [2]). We have also worked on algorithms to predict the indoor location of work and home arrival time of residential building occupants so as to automatically minimize thermostat use [3, 4]. Finally, we moved away from individual behavioral remedies to structural remedies by exploring tools that could help tenants to pick greener apartments [5]

[1] J. Schwartz, J. Mankoff, H. Scott Matthews. Reflections of everyday activity in spending data. In Proceedings of CHI 2009.  (Note). (pdf)

[2] J. Froehlich, T. Dillahunt, P. Klasnja, J. Mankoff, S. Consolvo, B. Harrison, J. A. Landay, UbiGreen: Investigating a Mobile Tool for Tracking and Supporting Green Transportation Habits. In Proceedings of CHI 2009. (Full paper) (pdf)

[3] Indoor-ALPS: an adaptive indoor location prediction system Christian Koehler, Nikola Banovic, Ian Oakley, Jennifer Mankoff, Anind K. Dey
UbiComp ’14 Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2014

[4] TherML: occupancy prediction for thermostat control Christian Koehler, Brian D. Ziebart, Jennifer Mankoff, Anind K. Dey UbiComp ’13 Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing, 2013

[5] Jennifer Mankoff, Dimeji Onafuwa, Kirstin Early, Nidhi Vyas, Vikram Kamath Cannanure: Understanding the Needs of Prospective Tenants. COMPASS 2018: 36:1-36:10

EDigs

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eDigs logoJennifer Mankoff, Dimeji Onafuwa, Kirstin Early, Nidhi Vyas, Vikram Kamath:
Understanding the Needs of Prospective Tenants. COMPASS 2018: 36:1-36:10

EDigs is a research project group in Carnegie Mellon University working on sustainability. Our research is focused on helping people find a perfect rental through machine learning and user research.

We sometimes study how our members use EDigs in order to learn how to build software support for successful social communities.

eDigs websiteScreenshot of edigs.org showing a mobile app, facebook and twitter feeds, and information about it.

Dynamic question ordering

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In recent years, surveys have been shifting online, offering the possibility for adaptive questions, where later questions depend on responses to earlier questions. We present a general framework for dynamically ordering questions, based on previous responses, to engage respondents, improving survey completion and imputation of unknown items. Our work considers two scenarios for data collection from survey-takers. In the first, we want to maximize survey completion (and the quality of necessary imputations) and so we focus on ordering questions to engage the respondent and collect hopefully all the information we seek, or at least the information that most characterizes the respondent so imputed values will be accurate. In the second scenario, our goal is to give the respondent a personalized prediction, based on information they provide. Since it is possible to give a reasonable prediction with only a subset of questions, we are not concerned with motivating the user to answer all questions. Instead, we want to order questions so that the user provides information that most reduces the uncertainty of our prediction, while not being too burdensome to answer.

Publications
Kirstin Early, Stephen E. Fienberg, Jennifer Mankoff. (2016). Test time feature ordering with FOCUS: Interactive predictions with minimal user burden. In Proceedings of 2016 ACM Conference on Pervasive and Ubiquitous Computing. Honorable Mention: Top 5% of submissions. Talk slides.