远程学习中人格特质如何影响同伴评价

IF 3 Q1 EDUCATION & EDUCATIONAL RESEARCH

Technology Knowledge and Learning Pub Date : 2023-11-02 DOI:10.1007/s10758-023-09694-2

Tomás Alves, Francisco Sousa, Sandra Gama, Joaquim Jorge, Daniel Gonçalves

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引用次数: 0

摘要

最近的研究利用同伴评估作为一种评分系统工具，学习者参与学习和评估。然而，在同伴评估任务中，关于个体差异(如个性)的知识有限。我们分析了人格因素如何影响一个学期远程学习课程的同伴评价动态。具体来说，我们调查了心理结构如何塑造人们如何感知用户生成的内容，与之互动，并评估他们的同伴。我们的研究结果表明，人格特质可以预测同伴评估过程的有效性，以及学生向同伴提供的分数和反馈。总之，我们提供了基于人格构造的设计指南，作为有价值的因素，包括在同行评估系统的设计管道中。

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How Personality Traits Affect Peer Assessment in Distance Learning

Abstract Recent research has leveraged peer assessment as a grading system tool where learners are involved in learning and evaluation. However, there is limited knowledge regarding individual differences, such as personality, in peer assessment tasks. We analyze how personality factors affect the peer assessment dynamics of a semester-long remote learning course. Specifically, we investigate how psychological constructs shape how people perceive user-generated content, interact with it, and assess their peers. Our results show that personality traits can predict how effective the peer assessment process will be and the scores and feedback that students provide to their peers. In conclusion, we contribute design guidelines based on personality constructs as valuable factors to include in the design pipeline of peer assessment systems.

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来源期刊

Technology Knowledge and Learning EDUCATION & EDUCATIONAL RESEARCH-

CiteScore

9.50

自引率

6.10%

发文量

期刊介绍： Technology, Knowledge and Learning emphasizes the increased interest on context-aware adaptive and personalized digital learning environments. Rapid technological developments have led to new research challenges focusing on digital learning, gamification, automated assessment and learning analytics. These emerging systems aim to provide learning experiences delivered via online environments as well as mobile devices and tailored to the educational needs, the personal characteristics and the particular circumstances of the individual learner or a (massive) group of interconnected learners. Such diverse learning experiences in real-world and virtual situations generates big data which provides rich potential for in-depth intelligent analysis and adaptive feedback as well as scaffolds whenever the learner needs it. Novel manuscripts are welcome that account for how these new technologies and systems reconfigure learning experiences, assessment methodologies as well as future educational practices. Technology, Knowledge and Learning also publishes guest-edited themed special issues linked to the emerging field of educational technology. Submissions can be empirical investigations, work in progress studies or emerging technology reports. Empirical investigations report quantitative or qualitative research demonstrating advances in digital learning, gamification, automated assessment or learning analytics. Work-in-progress studies provide early insights into leading projects or document progressions of excellent research within the field of digital learning, gamification, automated assessment or learning analytics. Emerging technology reports review new developments in educational technology by assessing the potentials for leading digital learning environments. Manuscripts submitted to Technology, Knowledge and Learning undergo a blind review process involving expert reviews and in-depth evaluations. Initial feedback is usually provided within eight weeks including in progress open-access abstracts and review snapshots.