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| 大数据赋能研究生学术不端行为治理——价值意蕴、现实梗阻与推进策略 |
| Postgraduate Academic Misconduct Governance Empowered by Big Data——Value Implication, Realistic Obstruction and Promotion Strategies |
| 投稿时间：2023-09-11 |
| DOI：10.19834/j.cnki.yjsjy2011.2024.02.06 |
| 中文关键词: 大数据;研究生学术不端;ChatGPT;学术不端行为治理 |
| 英文关键词: big data;postgraduate’s academic misconduct;ChatGPT;governance of academic misconduct |
| 基金项目:中国博士后科学基金第73批面上资助项目"大数据支持下德育评价变革的逻辑向度及其反拨机制研究"(2023M730762);国家社科基金项目"高校德育变革的大数据赋能与实现路径研究"(22FKSB038) |
| 作者 | 单位 | | 邹太龙 | 广州大学 教育学院, 广州 510006
湖北民族大学 教师教育学院, 湖北 恩施 445000 |
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| 中文摘要: |
| 作为一种全新的思维方式和先进的技术手段,大数据赋能研究生学术不端行为治理的价值意蕴主要体现在学术成果不断数据化让抄袭造假行为难以藏身、大数据检测软件防控各类学术不端行为的发生、互联网黑名单系统完善学术不端行为惩戒机制、聚类分析有助于开展针对性强的学术道德教育。然而,大数据赋能研究生学术不端行为治理也面临着学术监督主体缺乏使用大数据的行为意向、各类科研数据库的开放共享程度亟待提高、科研成果价值开发和合理使用的矛盾加剧、大数据固有缺陷妨碍学术不端行为的治理等现实梗阻。为此,需要打通科研数据开放共享的融合渠道、研发基于大数据的学术不端智能监督系统、提高学术不端监督主体的数据素养;全方位撑起科研数据的安全保护伞。 |
| 英文摘要: |
| As a new way of thinking and an advanced technical means, the value implication of postgraduate’s academic misconduct governance empowered by big data is mainly reflected in: the constant digitization of academic achievements makes it difficult for plagiarism and forgery to cheat; the big-data detection software prevents the occurrence of various academic misconducts; the interconnected blacklist system improves the punishment mechanism for academic dishonest behavior; and the cluster analysis benefits targeted academic moral education. However, the governance of postgraduate’s academic misconduct empowered by big data is still confronted with the following realistic obstacles: the academic supervising entity lacks behavioral intention of using big data, the degree of openness and sharing of various research databases needs to be improved urgently, the contradiction between the value excavation and the rational use of scientific achievements is intensified, and The inherent defects in big data hinder the academic misconduct governance. Therefore, it is necessary for us to open up an integrated scientific research data channel that is accessible and to be shared, develop a data-based intellectual academic misconduct supervision system, improve the data literacy of academic supervising entities, and build up across the board a research data security protection system. |
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