Using natural language processing to support peer‐feedback in the age of artificial intelligence

A cross‐disciplinary framework and a research agenda

Bibliographic Data

ID21297849
AuthorsElisabeth Bauer (0000-0003-4078-0999, Department of Psychology Ludwig‐Maximilians‐Universität München Munich Germany, corresponding author), Martin Greisel (0000-0002-9586-5714, Faculty for Philosophy and Social Sciences University of Augsburg Augsburg Germany, corresponding author), Ilia Kuznetsov (0000-0002-6359-2774, Ubiquitous Knowledge Processing Lab, Department of Computer Science and Hessian Center for AI (hessian.AI) Technical University of Darmstadt Darmstadt Germany), Markus Berndt (0000-0002-4467-5355, Institute of Medical Education, University Hospital of LMU Munich Munich Germany), Ingo Kollar (0000-0001-9257-5028, Faculty for Philosophy and Social Sciences University of Augsburg Augsburg Germany), Markus Dresel (0000-0002-2131-3749, Faculty for Philosophy and Social Sciences University of Augsburg Augsburg Germany), Martin R Fischer (0000-0002-5299-5025, Institute of Medical Education, University Hospital of LMU Munich Munich Germany), Frank Fischer (0000-0003-0253-659X, Department of Psychology Ludwig‐Maximilians‐Universität München Munich Germany)
Year2023
Volume54
Issue5
Pages1222-1245
Publication date2023-09-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueBritish Journal of Educational Technology (JOURNAL)
Journal identifiersISSN: 0007-1013 • E-ISSN: 1467-8535
PublisherWiley (PUBLISHER • GB)
DOI10.1111/bjet.13336
OpenAlexW4377041914
LanguageEN
Citations received39
References cited107

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 notes What 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

Data science · Discipline · Intersection (aeronautics) · Mathematics education · Natural language processing · Peer feedback · Process (computing) · Scheme (mathematics) · Artificial Intelligence · Computer Science · Neuroblastoma Research and Treatments · Online Learning and Analytics · Psychology · Student Assessment and Feedback

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