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ZENODO
Dataset . 2021
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2021
License: CC BY
Data sources: Datacite
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Research.fi
Dataset . 2021
License: CC BY
Data sources: Research.fi
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Research.fi
Dataset . 2021
License: CC BY
Data sources: Research.fi
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Improving Artificial Teachers by Considering How People Learn and Forget: Dataset

Authors: Nioche, Aurélien; Murena, Pierre-Alexandre; de la Torre-Ortiz, Carlos; Oulasvirta, Antti;

Improving Artificial Teachers by Considering How People Learn and Forget: Dataset

Abstract

This dataset contains the results of the experiment described in Nioche et al. (2021). This dataset contains 4 data files: data.csv: the main data file. stimuli.csv: the description/listing of the stimuli. demographic_info.csv: the demographic information about the users. data_incl_preliminary_exp.csv: an additional data file that includes the user of the preliminary experiments The main data file contains the logs of 53 different users using a self-teaching application for one week. The goal of the users was to learn the English meaning of Japanese kanji. Each user completed between 1370 trials and 1608 trials. Each user saw between 85 and 204 characters. Two additional files are also joint to the data files: info.ipynb: A Jupyter notebook that provides information about each data file, a few descriptive plots, and an example of data manipulation. info.pdf: A pdf rendering of the notebook. If you use this dataset, please refer to it by citing Nioche et al. (2021).

{"references": ["Aurelien Nioche, Pierre-Alexandre Murena, Carlos de la Torre-Ortiz, and Antti Oulasvirta. 2021. Improving Artificial Teachers by Considering How People Learn and Forget. In 26th International Conference on Intelligent User Interfaces (IUI '21). Association for Computing Machinery, New York, NY, USA, 445\u2013453. DOI:https://doi.org/10.1145/3397481.3450696"]}

Country
Finland
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Keywords

memory, kanji, human, self-teaching, vocabulary learning, intelligent tutoring

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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