Download PDF by Ina Wechsung: An Evaluation Framework for Multimodal Interaction:

By Ina Wechsung

ISBN-10: 3319038095

ISBN-13: 9783319038094

ISBN-10: 3319038109

ISBN-13: 9783319038100

This booklet offers (1) an exhaustive and empirically tested taxonomy of caliber points of multimodal interplay in addition to respective dimension tools, (2) a tested questionnaire in particular adapted to the review of multimodal structures and masking lots of the taxonomy‘s caliber points, (3) insights on how the standard perceptions of multimodal structures relate to the standard perceptions of its person parts, (4) a suite of empirically proven elements which impression modality selection, and (5) versions concerning the dating of the perceived caliber of a modality and the particular utilization of a modality.

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Additional info for An Evaluation Framework for Multimodal Interaction: Determining Quality Aspects and Modality Choice

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Engelbrecht, Quade, Möller, 2009). , 1997) likely being the most widespread one. PARADISE uses linear regression to predict user satisfaction based on interaction parameters such as task success or task duration. Other approaches are the MeMo (Mental Models) workbench using a probabilistic model of user behaviour, which includes a rule engine derived from empirical data in order to predict user behaviour (Engelbrecht, 2012). But like cognitive models, the engineering models are rather of academic interest than a widespread usability evaluation method amongst practitioners.

If the user is “satisfied” with the system, and therefore rather utilizes top-down strategies. Here, user models are usually defined based on real user data and are not necessarily linked to cognitive theories (cf. Engelbrecht, Quade, Möller, 2009). , 1997) likely being the most widespread one. PARADISE uses linear regression to predict user satisfaction based on interaction parameters such as task success or task duration. Other approaches are the MeMo (Mental Models) workbench using a probabilistic model of user behaviour, which includes a rule engine derived from empirical data in order to predict user behaviour (Engelbrecht, 2012).

G. the way of interaction), and it is not specified how they should be assessed. Just recently (Kühnel, 2012) proposed a welldefined set of interaction parameters for multimodal interaction yielding reasonable prediction performance (>50% accuracy) for user judgements. Protocol Analyses and Thinking Aloud For Protocol Analyses, user behaviour is captured using video, audio and log-files. A prominent method is Thinking Aloud. Here participants are asked to verbalize and loudly utter their thoughts (Holzinger, 2005).

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An Evaluation Framework for Multimodal Interaction: Determining Quality Aspects and Modality Choice by Ina Wechsung


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