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Alex Jansen

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Publication statistics

Pub. period:2010-2011
Pub. count:5
Number of co-authors:8

Co-authors

Number of publications with 3 favourite co-authors:

Jacob O. Wobbrock:4
Kristen Shinohara:3
Leah Findlater:2

Productive colleagues

Alex Jansen's 3 most productive colleagues in number of publications:

Jacob O. Wobbrock:70
Leah Findlater:17
Morgan Dixon:9

Calendar

May 20

CTS 2013 - The 2013 Intl. Conf. on Collaboration Techn. and Systems

May 22

Knowing (by) Designing 2013

May 27

13th Intl. Conf. on New Interfaces for Musical Expression

May 19

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-- Paul Rand, 1997

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Alex Jansen

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Publications by Alex Jansen (bibliography)

2011

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Wobbrock, Jacob O., Shinohara, Kristen and Jansen, Alex (2011): The effects of task dimensionality, endpoint deviation, throughput calculation, and experiment design on pointing measures and models. In: Proceedings of ACM CHI 2011 Conference on Human Factors in Computing Systems 2011. pp. 1639-1648.

“Fitts' law (1954) characterizes pointing speed-accuracy performance as throughput, whose invariance to target distances (A) and sizes (W) is known. However, it is unknown whether throughput and Fitts' law models in general are invariant to task dimensionality (1-D vs. 2-D), whether univariate (SDx) or bivariate (SDx,y) endpoint deviation is used, whether throughput is calculated using the mean-of-means approach or the slope-inverse approach, or whether Guiard's (2009) Form -- Scale experiment design is used instead of fully crossed A-W factors. We empirically investigate the confluence of these issues, finding that Fitts' law is largely invariant across 1-D and 2-D, provided that univariate endpoint deviation (SDx) is used in both, but that for 2-D pointing data, bivariate endpoint deviation (SDx,y) results in better Fitts' law models. Also, the mean-of-means throughput calculation exhibits lower variance across subjects and dimensionalities than the slope-inverse calculation. In light of these and other findings, we offer recommendations for pointing evaluations, especially in 2-D. We also offer an evaluation tool called Fitts Study to facilitate comparisons.”

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Wobbrock, Jacob O., Jansen, Alex and Shinohara, Kristen (2011): Modeling and predicting pointing errors in two dimensions. In: Proceedings of ACM CHI 2011 Conference on Human Factors in Computing Systems 2011. pp. 1653-1656.

“Recently, Wobbrock et al. (2008) derived a predictive model of pointing accuracy to complement Fitts' law's predictive model of pointing speed. However, their model was based on one-dimensional (1-D) horizontal movement, while applications of such a model require two dimensions (2-D). In this paper, the pointing error model is investigated for 2-D pointing in a study of 21 participants performing a time-matching task on the ISO 9241-9 ring-of-circles layout. Results show that the pointing error model holds well in 2-D. If univariate endpoint deviation (SDx) is used, regressing on N=72 observed vs. predicted error rate points yields R{squared}=.953. If bivariate endpoint deviation (SDx,y) is used, regression yields R{squared}=.936. For both univariate and bivariate models, the magnitudes of observed and predicted error rates are comparable.”

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Jansen, Alex, Findlater, Leah and Wobbrock, Jacob O. (2011): From the lab to the world: lessons from extending a pointing technique for real-world use. In: Proceedings of ACM CHI 2011 Conference on Human Factors in Computing Systems 2011. pp. 1867-1872.

“We present the Pointing Magnifier as a case study for understanding the issues and challenges of deploying lab-validated pointing facilitation techniques into the real world. The Pointing Magnifier works by magnifying the contents of an area cursor to allow for selection in a magnified visual and motor space. The technique has been shown in prior lab studies to be effective at reducing the need for fine pointing for motor-impaired users. We highlight key design and technical challenges in bringing the technique, and such techniques in general, from the lab to the field.”

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Bauer, Jared S., Jansen, Alex and Cirimele, Jesse (2011): MoodMusic: a method for cooperative, generative music playlist creation. In: Proceedings of the 2011 ACM Symposium on User Interface Software and Technology 2011. pp. 85-86.

“Music is a major element of social gatherings. However, creating playlists that suit everyone's tastes and the mood of the group can require a large amount of manual effort. In this paper, we present MoodMusic, a method to dynamically generate contextually appropriate music playlists for groups of people. MoodMusic uses speaker pitch and intensity in the conversation to determine the current 'mood'. MoodMusic then queries the online music libraries of the speakers to choose songs appropriate for that mood. This allows groups to listen to music appropriate for their current mood without managing playlists. This work contributes a novel method for dynamically creating music playlists for groups based on their music preferences and current mood.”

2010

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Findlater, Leah, Jansen, Alex, Shinohara, Kristen, Dixon, Morgan, Kamb, Peter, Rakita, Joshua and Wobbrock, Jacob O. (2010): Enhanced area cursors: reducing fine pointing demands for people with motor impairments. In: Proceedings of the 2010 ACM Symposium on User Interface Software and Technology 2010. pp. 153-162.

“Computer users with motor impairments face major challenges with conventional mouse pointing. These challenges are mostly due to fine pointing corrections at the final stages of target acquisition. To reduce the need for correction-phase pointing and to lessen the effects of small target size on acquisition difficulty, we introduce four enhanced area cursors, two of which rely on magnification and two of which use goal crossing. In a study with motor-impaired and able-bodied users, we compared the new designs to the point and Bubble cursors, the latter of which had not been evaluated for users with motor impairments. Two enhanced area cursors, the Visual-Motor-Magnifier and Click-and-Cross, were the most successful new designs for users with motor”

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