Adaptive Interfaces and Agents

Jameson, Anthony

doi:10.1201/9781420088861.ch6

Cited by 60 publications

(59 citation statements)

References 41 publications

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“…As Jameson (2003) points out, users have a need for control that is in conflict with automatic user-adaptivity. If a system's criteria are transparent, ways should be provided to correct them if they appear unsuitable to the user.…”

Section: User Freedom and Controlmentioning

confidence: 99%

“…In spite of these potential problems, previous authors have emphasised the importance of transparency (Jameson 2003;Höök et al 1996). Sinha and Swearingen (2002) for instance, evaluated transparency and recommendations for five music recommender systems.…”

Section: Transparency Of User-adaptive Systems and Study Hypothesesmentioning

confidence: 99%

“…However, apart from the advantages such adaptivity might bring, user-adaptivity also provides new challenges for interaction design. Jameson (2003) provides an overview of issues related to user-adaptivity, such as controllability, privacy, obtrusiveness, breadth of the user's experience, predictability and transparency. User-adaptive systems take decisions on behalf of the user; this influences controllability of a system and might conflict with the user's need to control a system's actions directly.…”

Section: Interaction With User-adaptive Systemsmentioning

confidence: 99%

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The effects of transparency on trust in and acceptance of a content-based art recommender

Cramer

Evers

Ramlal

et al. 2008

User Model User-Adap Inter

340

207

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The increasing availability of (digital) cultural heritage artefacts offers great potential for increased access to art content, but also necessitates tools to help users deal with such abundance of information. User-adaptive art recommender systems aim to present their users with art content tailored to their interests. These systems try to adapt to the user based on feedback from the user on which artworks he or she finds interesting. Users need to be able to depend on the system to competently adapt to their feedback and find the artworks that are most interesting to them. This paper investigates the influence of transparency on user trust in and acceptance of content-based recommender systems. A between-subject experiment (N = 60) evaluated interaction with three versions of a content-based art recommender in the cultural heritage domain. This recommender system provides users with artworks that are of interest to them, based on their ratings of other artworks. Version 1 was not transparent, version 2 explained to the user why a recommendation had been made and version 3 showed a rating of how certain the system was that a recommendation would be of interest to the user. Results show that explaining to the user why a recommendation was made increased acceptance of the recommendations. Trust in the system itself was not improved by transparency. Showing how certain the system was of a recommendation did not influence trust and acceptance. A number of guidelines for design of recommender systems in the cultural heritage domain have been derived from the study's results.

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Section: User Freedom and Controlmentioning

confidence: 99%

Section: Transparency Of User-adaptive Systems and Study Hypothesesmentioning

confidence: 99%

Section: Interaction With User-adaptive Systemsmentioning

confidence: 99%

See 1 more Smart Citation

The effects of transparency on trust in and acceptance of a content-based art recommender

Cramer

Evers

Ramlal

et al. 2008

User Model User-Adap Inter

340

207

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“…Following previous studies focusing on adaptive technologies [40,56], we predict that adaptive item sequencing will result in higher learning outcomes and a higher level of motivation.…”

Section: Methodsmentioning

confidence: 99%

When Easy Becomes Boring and Difficult Becomes Frustrating: Disentangling the Effects of Item Difficulty Level and Person Proficiency on Learning and Motivation

Moeyaert

Wauters²,

Desmet

et al. 2016

Systems

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Abstract:The research on electronic learning environments has evolved towards creating adaptive learning environments. In this study, the focus is on adaptive curriculum sequencing, in particular, the efficacy of an adaptive curriculum sequencing algorithm based on matching the item difficulty level to the learner's proficiency level. We therefore explored the effect of the relative difficulty level on learning outcome and motivation. Results indicate that, for learning environments consisting of questions focusing on just one dimension and with knowledge of correct response, it does not matter whether we present easy, moderate or difficult items or whether we present the items with a random mix of difficulty levels, regarding both learning and motivation.

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“…This response is not always positive when it concerns systems that autonomously adapt to the user or the environment. Especially when these systems become more sophisticated, they perform actions that users never experienced from similar systems before [18], and this makes these systems to be perceived as unpredictable and unreliable [19,20].…”

Section: Introductionmentioning

confidence: 99%

How elderly users of a socially interactive robot experience adaptiveness, adaptability and user control

Heerink

2011

2011 IEEE 12th International Symposium on Computational Intelligence and Informatics (CINTI)

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Adaptive Interfaces and Agents

Cited by 60 publications

References 41 publications

The effects of transparency on trust in and acceptance of a content-based art recommender

The effects of transparency on trust in and acceptance of a content-based art recommender

When Easy Becomes Boring and Difficult Becomes Frustrating: Disentangling the Effects of Item Difficulty Level and Person Proficiency on Learning and Motivation

How elderly users of a socially interactive robot experience adaptiveness, adaptability and user control

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