Automatically generating personalized user interfaces with Supple
Download 5.78 Mb. View original pdf
|
| Fig. 36. Participant error rates. Both motor-impaired and able-bodied participants made fewest errors with the ability-based interfaces. The baseline interfaces resulted inmost errors. Error bars show standard error. Significant pairwise differences are indicated with a star (As illustrated in Fig. 35 (left, participants with motor-impairments saw significant gains a 10% improvement for preference- based and a 28% improvement for ability-based interfaces (F 2 , 438 = 112 . 17, p < . 0001). Able-bodied participants saw a relatively smaller, though still significant, benefit of the personalized interfaces a 4% improvement for preference-based and for ability-based interfaces (F 2 , 220 = 49 . 36, p < . 0001). The differences in performance can be explained by a significant 5 main effect of Interface variant on total manipulation time, that is, the time spent actually manipulating the widgets ( χ 2 ( 2 , N = 763 ) = 359, p < . 0001). With baseline interfaces, participants spent on averages per trial set manipulating the individual widgets. With preference-based interfaces, this number was 5.76 s, while for ability-based interfaces, it was only 0.84 s, constituting a nearly 90% reduction compared to baseline interfaces. For all results reported so far, the pairwise differences between individual interface variants were statistically significant as well. We additionally observed a significant main effect of Interface variant on the total navigation time (F 2 , 674 = 7 . 76, p < . 001), explained by the significant difference between baseline and ability-based interfaces (z = − 3180, p < . 01). Baseline interfaces required the least amount of navigation time on averages) while preference- and ability-based interfaces required a little longer to navigates and 20.5 s, respectively. While statistically significant, these differences were very small—on the order of and were offset by the much larger differences in total manipulation time. There was a significant interaction between Impairment and Interface variant with respect to the total navigation time (F 2 , 674 = 9 . 20, p < . 0001): for able-bodied participants, navigation time was longer for both of the personalized interfaces (F 2 , 220 = 17 . 18, p < . 0001; all pairwise differences were significant as well, while for motor-impaired participants the effect was opposite, though smaller in magnitude and not significant. 8.6.3. Error rates There was a significant main effect of Interface variant on error rate ( χ 2 ( 5 , N = 153 ) = 55 . 46, p < . 0001): while the average error rate for baseline interfaces was 3.96%, it dropped to 2.57% for preference-based interfaces and to 0.93% for ability-based interfaces. This means that participants were both significantly faster and more accurate with the ability-based interfaces. There was no significant interaction between Impairment and Interface variant and the effects were similar and significant for both groups individually ( χ 2 ( 2 , N = 54 ) = 23 . 66, p < . 0001 for able-bodied and χ 2 ( 2 , N = 99 ) = 11 . 00, p < . 01 for motor-impaired; see Fig. All pairwise differences between individual interface variants for the results reported here are statistically significant, with the exception of the difference between the baseline and preference-based condition for participants with motor impairments. 8.6.4. Subjective results On a Not Easy (1)–Easy (7) scale for ease of use, motor-impaired participants rated ability-based interfaces easiest (6.00), preference-based next (5.64), and baseline most difficult (4.18). Similarly for able-bodied participants 5.29 for ability-based, 5 The manipulation time data had bi-modal distribution because for many task sets the total manipulation time was 0. We therefore used anon- parametric Wilcoxon Rank Sum test [88] to analyze these data. K.Z. Gajos et al. / Artificial Intelligence 174 (2010) 910–950 945 Download 5.78 Mb. Share with your friends:
|