Yes-Yes-Yes: Proactive Data Collection for ACL Rolling Review and Beyond

Dycke, Nils; Kuznetsov, Ilia; Gurevych, Iryna

doi:10.18653/v1/2022.findings-emnlp.23

Cited by 1 publication

(2 citation statements)

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“…In addition, when applying NLP to peer-feedback, the ethical and legal challenges related to learners' personal data management and data licensing must be addressed. While responsible research is already gaining traction in NLP (Dycke et al, 2022(Dycke et al, , 2023Rogers et al, 2021), we envision that cross-collaborations between the fields would greatly enrich the overall data and participant handling practice in NLP. 7.…”

Section: Current Challenges and A Rese Arch Agendamentioning

confidence: 98%

“…Even the most advanced NLP systems suffer from substantial performance decrease when applied to previously unseen languages and domains. The application of NLP to peer-feedback thus demands advancements in domain and language adaptation technology (Chronopoulou et al, 2022;Pfeiffer et al, 2020), as well as quantifying and collecting data from pre-existing peer-feedback workflows (Dycke et al, 2022(Dycke et al, , 2023. Simultaneously, peer-feedback has the potential to generate great amounts of diverse textual data for NLP research and to provide excellent testing grounds for the study of language-and domain-adaptation in NLP.…”

Section: Current Challenges and A Rese Arch Agendamentioning

confidence: 99%

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Using natural language processing to support peer‐feedback in the age of artificial intelligence: A cross‐disciplinary framework and a research agenda

Bauer

Greisel

Kuznetsov

et al. 2023

Brit J Educational Tech

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Advancements in artificial intelligence are rapidly increasing. The new‐generation large language models, such as ChatGPT and GPT‐4, bear the potential to transform educational approaches, such as peer‐feedback. To investigate peer‐feedback at the intersection of natural language processing (NLP) and educational research, this paper suggests a cross‐disciplinary framework that aims to facilitate the development of NLP‐based adaptive measures for supporting peer‐feedback processes in digital learning environments. To conceptualize this process, we introduce a peer‐feedback process model, which describes learners' activities and textual products. Further, we introduce a terminological and procedural scheme that facilitates systematically deriving measures to foster the peer‐feedback process and how NLP may enhance the adaptivity of such learning support. Building on prior research on education and NLP, we apply this scheme to all learner activities of the peer‐feedback process model to exemplify a range of NLP‐based adaptive support measures. We also discuss the current challenges and suggest directions for future cross‐disciplinary research on the effectiveness and other dimensions of NLP‐based adaptive support for peer‐feedback. Building on our suggested framework, future research and collaborations at the intersection of education and NLP can innovate peer‐feedback in digital learning environments. Practitioner notesWhat is already known about this topic There is considerable research in educational science on peer‐feedback processes. Natural language processing facilitates the analysis of students' textual data. There is a lack of systematic orientation regarding which NLP techniques can be applied to which data to effectively support the peer‐feedback process. What this paper adds A comprehensive overview model that describes the relevant activities and products in the peer‐feedback process. A terminological and procedural scheme for designing NLP‐based adaptive support measures. An application of this scheme to the peer‐feedback process results in exemplifying the use cases of how NLP may be employed to support each learner activity during peer‐feedback. Implications for practice and/or policy To boost the effectiveness of their peer‐feedback scenarios, instructors and instructional designers should identify relevant leverage points, corresponding support measures, adaptation targets and automation goals based on theory and empirical findings. Management and IT departments of higher education institutions should strive to provide digital tools based on modern NLP models and integrate them into the respective learning management systems; those tools should help in translating the automation goals requested by their instructors into prediction targets, take relevant data as input and allow for evaluating the predictions.

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Section: Current Challenges and A Rese Arch Agendamentioning

confidence: 98%