Open AccessProceedings Article10.1109/ESCIENCE.2018.00100

Automatic Recommendations for Data Coding: A Use Case from Medical and Teacher Education

- 29 Oct 2018

- pp 364-365

TL;DR: The NeuralWeb API tackles the problem of manual coding of data by providing automatic codings to other software components, which can for example be used in annotation platforms in terms of recommendations for expert coders from social sciences and humanities.

Abstract: Research in social sciences and humanities of ten involves analysing data to draw scientific conclusions. This however requires the manual coding of the data, which is highly time-consuming. A use case is the coding of students' essays in education to draw conclusions about students' reasoning and argumentation. The NeuralWeb API tackles this problem by providing automatic codings to other software components. These codings can for example be used in annotation platforms in terms of recommendations for expert coders from social sciences and humanities. After some initial manual annotations, the expert coders then merely need to verify the correctness of the automatic codings instead of manually annotating all data.

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The NeuralWeb API this paper provides automatic codings to other software components, which can be used in annotation platforms in terms of recommendations for expert coders from social sciences and humanities.

Citations

•Proceedings Article•10.18653/V1/D19-3013

FAMULUS: Interactive Annotation and Feedback Generation for Teaching Diagnostic Reasoning

Jonas Pfeiffer, +9 more

- 14 Aug 2019

TL;DR: It is argued that because the central model consistently improves, the interactive approach encourages both students and instructors to recurrently use the tool, and thus accelerate the speed of data creation and annotation.

...read moreread less

•Posted Content

FAMULUS: Interactive Annotation and Feedback Generation for Teaching Diagnostic Reasoning

Jonas Pfeiffer, +9 more

- 29 Aug 2019

- arXiv: Computation and Language

TL;DR: FAMULUS as discussed by the authors helps students learn to diagnose based on automatic feedback in virtual patient simulations, and it supports instructors in labeling training data, which is an exceptionally difficult skill to obtain but vital for many different professions.

...read moreread less

References

•Proceedings Article•10.18653/V1/D17-1035

Reporting Score Distributions Makes a Difference: Performance Study of LSTM-networks for Sequence Tagging

Nils Reimers, +1 more

- 01 Sep 2017

TL;DR: It is shown that reporting a single performance score is insufficient to compare non-deterministic approaches and proposed to compare score distributions based on multiple executions, and network architectures are presented that produce both superior performance as well as are more stable with respect to the remaining hyperparameters.

...read moreread less

429

•Posted Content

Reporting Score Distributions Makes a Difference: Performance Study of LSTM-networks for Sequence Tagging

Nils Reimers, +1 more

- 31 Jul 2017

- arXiv: Computation and Language

TL;DR: In this article, the authors show that reporting a single performance score is insufficient to compare non-deterministic approaches and propose to compare score distributions based on multiple executions instead of publishing and reporting single performance scores.

...read moreread less

376

•Proceedings Article

The INCEpTION Platform: Machine-Assisted and Knowledge-Oriented Interactive Annotation

Jan-Christoph Klie, +4 more

- 23 Jun 2018

TL;DR: INCEpTION is a new annotation platform for tasks including interactive and semantic annotation (e.g., concept linking, fact linking, knowledge base population, semantic frame annotation) that incorporates machine learning capabilities which actively assist and guide annotators.

...read moreread less

331

•Proceedings Article•10.18653/V1/N18-2006

Multi-Task Learning for Argumentation Mining in Low-Resource Settings

Claudia Schulz, +4 more

- 01 Jun 2018

TL;DR: This paper investigated whether and where multi-task learning (MTL) can improve performance on NLP problems related to argumentation mining (AM), in particular argument component identification, and they showed that MTL performs particularly well (and better than single task learning) when little training data is available for the main task, a common scenario in AM.

...read moreread less

•Journal•10.14786/FLR.V2I2.96

Scientific Reasoning and Argumentation: Advancing an Interdisciplinary Research Agenda in Education

Frank Fischer, +12 more

- 16 Jun 2014

TL;DR: In this paper, the main topics and key findings of three different strands of research are reviewed, namely research on the development of scientific reasoning, research on scientific argumentation, and research on approaches to support scientific reasoning and argumentation.

...read moreread less

Automatic Recommendations for Data Coding: A Use Case from Medical and Teacher Education

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Most frequently asked questions

1. What are the contributions in "Automatic recommendations for data coding: a use case from medical and teacher education" ?

Citations

FAMULUS: Interactive Annotation and Feedback Generation for Teaching Diagnostic Reasoning

FAMULUS: Interactive Annotation and Feedback Generation for Teaching Diagnostic Reasoning

References

Reporting Score Distributions Makes a Difference: Performance Study of LSTM-networks for Sequence Tagging

Reporting Score Distributions Makes a Difference: Performance Study of LSTM-networks for Sequence Tagging

The INCEpTION Platform: Machine-Assisted and Knowledge-Oriented Interactive Annotation

Multi-Task Learning for Argumentation Mining in Low-Resource Settings

Scientific Reasoning and Argumentation: Advancing an Interdisciplinary Research Agenda in Education

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