Personal data protection and academia: GDPR issues and multi-modal data-collections "in the wild"

Siegert, Ingo; Silber-Varod, Vered; Carmi, Nehoray; Kamocki, Pawel

doi:10.36965/ojakm.2020.8(1)16-31

Cited by 6 publications

(6 citation statements)

References 22 publications

(29 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For more information on approaches to achieve so-called pseudonymization, we refer to Finck and Pallas (2020). In particular, as speech and video data are often of particular interest to NLP researchers, we also refer to Siegert et al (2020) for a discussion on anonymization techniques.…”

Section: Privacymentioning

confidence: 99%

How to do human evaluation: A brief introduction to user studies in NLP

Schuff

Vanderlyn²,

Adel³

et al. 2023

Nat. Lang. Eng.

View full text Add to dashboard Cite

Many research topics in natural language processing (NLP), such as explanation generation, dialog modeling, or machine translation, require evaluation that goes beyond standard metrics like accuracy or F1 score toward a more human-centered approach. Therefore, understanding how to design user studies becomes increasingly important. However, few comprehensive resources exist on planning, conducting, and evaluating user studies for NLP, making it hard to get started for researchers without prior experience in the field of human evaluation. In this paper, we summarize the most important aspects of user studies and their design and evaluation, providing direct links to NLP tasks and NLP-specific challenges where appropriate. We (i) outline general study design, ethical considerations, and factors to consider for crowdsourcing, (ii) discuss the particularities of user studies in NLP, and provide starting points to select questionnaires, experimental designs, and evaluation methods that are tailored to the specific NLP tasks. Additionally, we offer examples with accompanying statistical evaluation code, to bridge the gap between theoretical guidelines and practical applications.

show abstract

Section: Privacymentioning

confidence: 99%

How to do human evaluation: A brief introduction to user studies in NLP

Schuff

Vanderlyn²,

Adel³

et al. 2023

Nat. Lang. Eng.

View full text Add to dashboard Cite

show abstract

“…In addition, behavior, which provides sensitive data, is readily accessible through video research. Anonymization of multimodal data without compromising the quality of the data can be challenging (Siegert et al, 2020). Full anonymization is not even possible if personal physical identifiers, such as close-up images of the iris, constitute essential data.…”

Section: Management Of Complex Datamentioning

confidence: 99%

“…The GDPR prohibits collecting unnecessary personal data. However, the principle of minimization of data poses a challenge for multimodal research (Siegert et al, 2020). It is in the nature of video data that it is not predictable in the sense that one will know exactly what information participants end up disclosing.…”

Section: Management Of Complex Datamentioning

confidence: 99%

“…While transfer of data among parties is possible, transfer outside the European Union requires planning as GDPR compliance involves that research participants are informed of transfer in a privacy policy description. Furthermore, making video data openly available for all researchers further entails a number of questions at the crossroads of data management, legal compliance, and research ethics, often leading researchers not to open their data for others to explore (Siegert et al, 2020). While it may be possible and nowadays facilitated by dedicated journals, in the Finnish and Australian context opening video data requires extremely careful planning, informing and consent procedures-and opening may still not be feasible due to potential risk or harm to participants (see Rutanen et al, 2018).…”

Section: Management Of Complex Datamentioning

confidence: 99%

See 1 more Smart Citation

Advancing video research methodology to capture the processes of social interaction and multimodality

Hannula

Haataja

Löfström

et al. 2022

ZDM Mathematics Education

View full text Add to dashboard Cite

In this reflective methodological paper we focus on affordances and challenges of video data. We compare and analyze two research settings that use the latest video technology to capture classroom interactions in mathematics education, namely, The Social Unit of Learning (SUL) project of the University of Melbourne and the MathTrack project of the University of Helsinki. While using these two settings as examples, we have structured our reflections around themes pertinent to video research in general, namely, research methods, data management, and research ethics. SUL and MathTrack share an understanding of mathematics learning as social multimodal practice, and provide possibilities for zooming into the situational micro interactions that construct collaborative problem-solving learning. Both settings provide rich data for in-depth analyses of peer interactions and learning processes. The settings share special needs for technical support and data management, as well as attention to ethical aspects from the perspective of the participants’ security and discretion. SUL data are especially suitable for investigating interactions on a broad scope, addressing how multiple interactional processes intertwine. MathTrack, on the other hand, enables exploration of participants’ visual attention in detail and its role in learning. Both settings could provide tools for teachers’ professional development by showing them aspects of classroom interactions that would otherwise remain hidden.

show abstract

“…Through implementing the achievements of Industry 4.0 HEIs are in the position to acquire and handle at a very large-scale data on student attendance, assessment, and progress. In order to constantly comply with the legal requirements on personal data protection, universities should insure that students are constantly informed on the legal setting of using their data (based on a legal duty or personal consent) and on the means by which their personal data is stored, used, modified, accessed or shared with third parties (Siegert, Silber-Varod, Carmy, & Kamocki, 2020). In the same respect, in order to evaluate the load of data needed -each functional unit of the universities has to supply the KM function with the typology of data they need, in strict accordance with their organizational role, and with the calendar for storage period of the data they need (Siegert, Silber-Varod, Carmy, & Kamocki, 2020).…”

Section: Introductionmentioning

confidence: 99%

Impact of Knowledge Management Strategies on Academic Performance in the Context of the Transition to Education 4.0

Dovleac,

Cărămidaru

2023

REd

View full text Add to dashboard Cite

The concept of Education 4.0 emerged linked to trends regarding Industry 4.0 and Internet 4.0 and is based on the integration of emerging technologies to revolutionize the way education is delivered. The use of these emerging technologies has created a vast amount of data that requires careful and thorough management in order to obtain the most possible benefits such as developing personalized learning experiences, smart ways of tracking student progress and providing feedback to all parties involved in the educational process. Therefore, knowledge management tools and strategies are gaining more importance, especially in the context of higher education institutions which have been identified as important sources of knowledge creation and sharing. The implementation of specific knowledge management strategies can furthermore help facilitate the transition to Education 4.0 by embracing key aspects of Industry 4.0 and Internet 4.0 such as promoting a culture of innovation, collaboration, continuous learning, providing a support system for streamlining administrative processes, cost reduction and improving the decision-making process. In the current paper, the authors analyse the current trends regarding knowledge management strategies deployed in Education 4.0 and propose a set of possible approaches which take into account knowledge management practices for an efficient transition to Education 4.0. The set of possible approaches and Industry 4.0 tools that could facilitate the transition have been organized in a structured, easy to follow model based on a four-stage process that relies on the feedback loops from both the process inputs and outputs. Using the proposed model facilitates the transition to Education 4.0 and helps practitioners understand the main aspects of each stage and select the Industry 4.0 tools that can be used in their respective context in order to yield most benefits.

show abstract

Personal data protection and academia: GDPR issues and multi-modal data-collections "in the wild"

Cited by 6 publications

References 22 publications

How to do human evaluation: A brief introduction to user studies in NLP

How to do human evaluation: A brief introduction to user studies in NLP

Advancing video research methodology to capture the processes of social interaction and multimodality

Impact of Knowledge Management Strategies on Academic Performance in the Context of the Transition to Education 4.0

Contact Info

Product

Resources

About