The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Penilaian rakan sebaya, di mana orang menyemak hasil kerja rakan sebaya dan mempunyai karya mereka sendiri disemak oleh rakan sebaya, berguna untuk menilai kerja rumah. Dalam sistem penilaian rakan sebaya konvensional, kerja biasanya diperuntukkan kepada orang sebelum penilaian bermula; oleh itu, jika orang tercicir (meninggalkan ulasan) semasa tempoh penilaian, ketidakseimbangan berlaku antara bilangan kerja yang disemak oleh seseorang dan rakan sebaya yang telah menyemak kerja itu. Apabila jumlah ketidakseimbangan meningkat, sesetengah orang yang tekun menyiapkan ulasan mungkin mengalami kekurangan ulasan dan tidak digalakkan untuk mengambil bahagian dalam penilaian rakan sebaya pada masa hadapan. Oleh itu, dalam kajian ini, kami mengguna pakai pendekatan peruntukan penyesuaian baharu di mana orang diperuntukkan semakan berfungsi hanya apabila diminta dan mencadangkan algoritma untuk memperuntukkan kerja kepada orang, yang mengurangkan jumlah ketidakseimbangan. Untuk menunjukkan keberkesanan algoritma yang dicadangkan, kami menyediakan sempadan atas jumlah ketidakseimbangan yang dihasilkan oleh algoritma yang dicadangkan. Di samping itu, kami melanjutkan algoritma di atas untuk mempertimbangkan keupayaan menyemak semula. Algoritma lanjutan mengelakkan masalah yang hanya pengulas tidak mahir (atau mahir) diperuntukkan kepada kerja tertentu. Kami menunjukkan keberkesanan dua algoritma yang dicadangkan berbanding dengan algoritma sedia ada melalui eksperimen menggunakan data simulasi.
Hideaki OHASHI
Kyoto University
Yasuhito ASANO
Toyo University
Toshiyuki SHIMIZU
Kyoto University
Masatoshi YOSHIKAWA
Kyoto University
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Salinan
Hideaki OHASHI, Yasuhito ASANO, Toshiyuki SHIMIZU, Masatoshi YOSHIKAWA, "Adaptive Balanced Allocation for Peer Assessments" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 5, pp. 939-948, May 2020, doi: 10.1587/transinf.2019DAP0004.
Abstract: Peer assessments, in which people review the works of peers and have their own works reviewed by peers, are useful for assessing homework. In conventional peer assessment systems, works are usually allocated to people before the assessment begins; therefore, if people drop out (abandoning reviews) during an assessment period, an imbalance occurs between the number of works a person reviews and that of peers who have reviewed the work. When the total imbalance increases, some people who diligently complete reviews may suffer from a lack of reviews and be discouraged to participate in future peer assessments. Therefore, in this study, we adopt a new adaptive allocation approach in which people are allocated review works only when requested and propose an algorithm for allocating works to people, which reduces the total imbalance. To show the effectiveness of the proposed algorithm, we provide an upper bound of the total imbalance that the proposed algorithm yields. In addition, we extend the above algorithm to consider reviewing ability. The extended algorithm avoids the problem that only unskilled (or skilled) reviewers are allocated to a given work. We show the effectiveness of the proposed two algorithms compared to the existing algorithms through experiments using simulation data.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019DAP0004/_p
Salinan
@ARTICLE{e103-d_5_939,
author={Hideaki OHASHI, Yasuhito ASANO, Toshiyuki SHIMIZU, Masatoshi YOSHIKAWA, },
journal={IEICE TRANSACTIONS on Information},
title={Adaptive Balanced Allocation for Peer Assessments},
year={2020},
volume={E103-D},
number={5},
pages={939-948},
abstract={Peer assessments, in which people review the works of peers and have their own works reviewed by peers, are useful for assessing homework. In conventional peer assessment systems, works are usually allocated to people before the assessment begins; therefore, if people drop out (abandoning reviews) during an assessment period, an imbalance occurs between the number of works a person reviews and that of peers who have reviewed the work. When the total imbalance increases, some people who diligently complete reviews may suffer from a lack of reviews and be discouraged to participate in future peer assessments. Therefore, in this study, we adopt a new adaptive allocation approach in which people are allocated review works only when requested and propose an algorithm for allocating works to people, which reduces the total imbalance. To show the effectiveness of the proposed algorithm, we provide an upper bound of the total imbalance that the proposed algorithm yields. In addition, we extend the above algorithm to consider reviewing ability. The extended algorithm avoids the problem that only unskilled (or skilled) reviewers are allocated to a given work. We show the effectiveness of the proposed two algorithms compared to the existing algorithms through experiments using simulation data.},
keywords={},
doi={10.1587/transinf.2019DAP0004},
ISSN={1745-1361},
month={May},}
Salinan
TY - JOUR
TI - Adaptive Balanced Allocation for Peer Assessments
T2 - IEICE TRANSACTIONS on Information
SP - 939
EP - 948
AU - Hideaki OHASHI
AU - Yasuhito ASANO
AU - Toshiyuki SHIMIZU
AU - Masatoshi YOSHIKAWA
PY - 2020
DO - 10.1587/transinf.2019DAP0004
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2020
AB - Peer assessments, in which people review the works of peers and have their own works reviewed by peers, are useful for assessing homework. In conventional peer assessment systems, works are usually allocated to people before the assessment begins; therefore, if people drop out (abandoning reviews) during an assessment period, an imbalance occurs between the number of works a person reviews and that of peers who have reviewed the work. When the total imbalance increases, some people who diligently complete reviews may suffer from a lack of reviews and be discouraged to participate in future peer assessments. Therefore, in this study, we adopt a new adaptive allocation approach in which people are allocated review works only when requested and propose an algorithm for allocating works to people, which reduces the total imbalance. To show the effectiveness of the proposed algorithm, we provide an upper bound of the total imbalance that the proposed algorithm yields. In addition, we extend the above algorithm to consider reviewing ability. The extended algorithm avoids the problem that only unskilled (or skilled) reviewers are allocated to a given work. We show the effectiveness of the proposed two algorithms compared to the existing algorithms through experiments using simulation data.
ER -