Implicit Interaction Through Machine Learning: Challenges in Design, Accountability, and Privacy

McMillan, Donald

doi:10.1007/978-3-319-70284-1_27

Cited by 5 publications

(4 citation statements)

References 18 publications

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“…Building on this, future MM teacher dashboard design may look to the Implicit Human-Computer Interaction (iHCI) paradigm for inspiration. This is when a system implicitly infers a users preferences and needs based on their behaviour, rather than explicit communication [63], [64]. For example, extending MM teacher dashboards from awareness platforms to teacher feedback recommendation systems, by integrating MMD-based feedback actionables that address student learning states (e.g., offering positive reinforcement, or scaling the content difficulty), and are displayed to teachers via a MM teacher dashboard in real-time.…”

Section: A MM Teacher Dashboard Advantagesmentioning

confidence: 99%

Multimodal Teacher Dashboards: Challenges and Opportunities of Enhancing Teacher Insights Through a Case Study

Lee-Cultura

Sharma

Giannakos

2024

IEEE Trans. Learning Technol.

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Teacher dashboards provide insights on students' progress through visualisations and scores derived from data generated during teaching and learning activities (e.g., response times, task correctness) to improve teaching. Despite the potential usefulness of enhancing teacher dashboards, and the respective teaching practices, with rich information regarding students' cognitive and affective states (e.g., cognitive load), few studies on teacher dashboards have considered such information. In this study, we drew on contemporary developments of MultiModal Learning Analytics and designed a MultiModal (MM) teacher dashboard with notification system. The proposed system 1) receives data from various sensors, 2) computes relevant cognitive and affective measurements, 3) visualises the resulting measurements in a clean customisable interface, and 4) notifies instructors during moments of interest so they may determine an appropriate method to support struggling students. To evaluate our MM teacher dashboard, we first collected MultiModal Data (MMD), performance data, and video recordings of students' interactions during an in-situ study where 26 students engaged with a motion-based learning task. Then, we used our MM teacher dashboard to present the collected MMD and video recordings to 20 experienced teachers and educational researchers, and collected qualitative data regarding respondents' insights on the advantages and challenges of visualising students' MMD. Results showed that teachers found a MM teacher dashboard enhanced with notification system, useful to complement their pedagogical practices. We offer empirically founded guidelines for design and integration of a MM teacher dashboard with notification systems, aimed to enhance teachers' understanding of students' learning states (e.g., real-time awareness of students' stress).

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Section: A MM Teacher Dashboard Advantagesmentioning

confidence: 99%

Multimodal Teacher Dashboards: Challenges and Opportunities of Enhancing Teacher Insights Through a Case Study

Lee-Cultura

Sharma

Giannakos

2024

IEEE Trans. Learning Technol.

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“…Including privacy-preserving features is essential when designing proactive VAs. Previous work shows that giving users the option to examine the recordings and actions taken by the systems [25] as well as transparency on the recorded data are decisive factors for the acceptance of such proactive technologies [26]. But even with full control over what private data is shared or stored, the VA's active role and interference in the private sphere in domestic situations might be experienced as inappropriate, which needs further investigation.…”

Section: Privacy Concerns In Voice Assistant Usementioning

confidence: 99%

Understanding Circumstances for Desirable Proactive Behaviour of Voice Assistants: The Proactivity Dilemma

Zargham

Reicherts

Bonfert

et al. 2022

Proceedings of the 4th Conference on Conversational User Interfaces

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“…Besides design concerns, implicit interactions in intelligent environments raise privacy concerns. From a technical point of view, in order to support such interactions, it is required that the environment records contextual and user data (such as movement or speech), and carries out all the required analysis so as to respond accurately and appropriately (McMillan, 2017). A more detailed discussion of the privacy and ethics concerns, and the challenges entailed is presented in Section 4.…”

Section: Implicit Interactionsmentioning

confidence: 99%

Seven HCI Grand Challenges

Stephanidis

Salvendy

Antona

et al. 2019

International Journal of Human–Computer Interaction

362

111

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This article aims to investigate the Grand Challenges which arise in the current and emerging landscape of rapid technological evolution towards more intelligent interactive technologies, coupled with increased and widened societal needs, as well as individual and collective expectations that HCI, as a discipline, is called upon to address. A perspective oriented to humane and social values is adopted, formulating the challenges in terms of the impact of emerging intelligent interactive technologies on human life both at the individual and societal levels. Seven Grand Challenges are identified and presented in this article: Human-Technology Symbiosis; Human-Environment Interactions; Ethics, Privacy and Security; Well-being, Health and Eudaimonia; Accessibility and Universal Access; Learning and Creativity; and Social Organization and Democracy. Although not exhaustive, they summarize the views and research priorities of an international interdisciplinary group of experts, reflecting different scientific perspectives, methodological approaches and application domains. Each identified Grand Challenge is analyzed in terms of: concept and problem definition; main research issues involved and state of the art; and associated emerging requirements. BACKGROUNDThis article presents the results of the collective effort of a group of 32 experts involved in the community of the Human Computer Interaction International (HCII) Conference series. The group's collaboration started in early 2018 with the collection of opinions from all group members, each asked to independently list and describe five HCI grand challenges. During a one-day meeting held on the 20th July 2018 in the context of the HCI International 2018 Conference in Las Vegas, USA, the identified topics were debated and challenges were formulated in terms of the impact of emerging intelligent interactive technologies on human life both at the individual and societal levels. Further analysis and consolidation led to a set of seven Grand Challenges presented herein. This activity was organized and supported by the HCII Conference series.

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Implicit Interaction Through Machine Learning: Challenges in Design, Accountability, and Privacy

Cited by 5 publications

References 18 publications

Multimodal Teacher Dashboards: Challenges and Opportunities of Enhancing Teacher Insights Through a Case Study

Multimodal Teacher Dashboards: Challenges and Opportunities of Enhancing Teacher Insights Through a Case Study

Understanding Circumstances for Desirable Proactive Behaviour of Voice Assistants: The Proactivity Dilemma

Seven HCI Grand Challenges

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