网络微任务劳动平台的认知个性化:系统的文献综述

IF 3 3区计算机科学 Q2 COMPUTER SCIENCE, CYBERNETICS

User Modeling and User-Adapted Interaction Pub Date : 2023-09-19 DOI:10.1007/s11257-023-09383-w

Dennis Paulino, António Correia, João Barroso, Hugo Paredes

{"title":"网络微任务劳动平台的认知个性化:系统的文献综述","authors":"Dennis Paulino, António Correia, João Barroso, Hugo Paredes","doi":"10.1007/s11257-023-09383-w","DOIUrl":null,"url":null,"abstract":"Abstract Online microtask labor has increased its role in the last few years and has provided the possibility of people who were usually excluded from the labor market to work anytime and without geographical barriers. While this brings new opportunities for people to work remotely, it can also pose challenges regarding the difficulty of assigning tasks to workers according to their abilities. To this end, cognitive personalization can be used to assess the cognitive profile of each worker and subsequently match those workers to the most appropriate type of work that is available on the digital labor market. In this regard, we believe that the time is ripe for a review of the current state of research on cognitive personalization for digital labor. The present study was conducted by following the recommended guidelines for the software engineering domain through a systematic literature review that led to the analysis of 20 primary studies published from 2010 to 2020. The results report the application of several cognition theories derived from the field of psychology, which in turn revealed an apparent presence of studies indicating accurate levels of cognitive personalization in digital labor in addition to a potential increase in the worker’s performance, most frequently investigated in crowdsourcing settings. In view of this, the present essay seeks to contribute to the identification of several gaps and opportunities for future research in order to enhance the personalization of online labor, which has the potential of increasing both worker motivation and the quality of digital work.","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":"29 1","pages":"0"},"PeriodicalIF":3.0000,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Cognitive personalization for online microtask labor platforms: A systematic literature review\",\"authors\":\"Dennis Paulino, António Correia, João Barroso, Hugo Paredes\",\"doi\":\"10.1007/s11257-023-09383-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract Online microtask labor has increased its role in the last few years and has provided the possibility of people who were usually excluded from the labor market to work anytime and without geographical barriers. While this brings new opportunities for people to work remotely, it can also pose challenges regarding the difficulty of assigning tasks to workers according to their abilities. To this end, cognitive personalization can be used to assess the cognitive profile of each worker and subsequently match those workers to the most appropriate type of work that is available on the digital labor market. In this regard, we believe that the time is ripe for a review of the current state of research on cognitive personalization for digital labor. The present study was conducted by following the recommended guidelines for the software engineering domain through a systematic literature review that led to the analysis of 20 primary studies published from 2010 to 2020. The results report the application of several cognition theories derived from the field of psychology, which in turn revealed an apparent presence of studies indicating accurate levels of cognitive personalization in digital labor in addition to a potential increase in the worker’s performance, most frequently investigated in crowdsourcing settings. In view of this, the present essay seeks to contribute to the identification of several gaps and opportunities for future research in order to enhance the personalization of online labor, which has the potential of increasing both worker motivation and the quality of digital work.\",\"PeriodicalId\":49388,\"journal\":{\"name\":\"User Modeling and User-Adapted Interaction\",\"volume\":\"29 1\",\"pages\":\"0\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2023-09-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"User Modeling and User-Adapted Interaction\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1007/s11257-023-09383-w\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, CYBERNETICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"User Modeling and User-Adapted Interaction","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s11257-023-09383-w","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, CYBERNETICS","Score":null,"Total":0}

引用次数: 0

摘要

在线微任务劳动在过去几年中发挥了越来越大的作用，并为那些通常被排除在劳动力市场之外的人提供了随时工作的可能性，并且没有地域障碍。虽然这为人们远程工作带来了新的机会，但它也会带来挑战，即根据员工的能力分配任务的难度。为此，认知个性化可用于评估每个工人的认知概况，并随后将这些工人与数字劳动力市场上最合适的工作类型相匹配。在这方面，我们认为现在是时候对数字劳动的认知个性化研究现状进行回顾了。目前的研究是通过对2010年至2020年发表的20项主要研究进行系统的文献回顾，遵循软件工程领域推荐的指导方针进行的。结果报告了来自心理学领域的几个认知理论的应用，这些理论反过来揭示了数字劳动中认知个性化的准确水平，以及工人表现的潜在提高，这些研究最常在众包环境中进行调查。鉴于此，本文旨在帮助确定未来研究的几个差距和机会，以增强在线劳动的个性化，这有可能提高工人的动机和数字工作的质量。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Cognitive personalization for online microtask labor platforms: A systematic literature review

查看原文本刊更多论文

Cognitive personalization for online microtask labor platforms: A systematic literature review

Abstract Online microtask labor has increased its role in the last few years and has provided the possibility of people who were usually excluded from the labor market to work anytime and without geographical barriers. While this brings new opportunities for people to work remotely, it can also pose challenges regarding the difficulty of assigning tasks to workers according to their abilities. To this end, cognitive personalization can be used to assess the cognitive profile of each worker and subsequently match those workers to the most appropriate type of work that is available on the digital labor market. In this regard, we believe that the time is ripe for a review of the current state of research on cognitive personalization for digital labor. The present study was conducted by following the recommended guidelines for the software engineering domain through a systematic literature review that led to the analysis of 20 primary studies published from 2010 to 2020. The results report the application of several cognition theories derived from the field of psychology, which in turn revealed an apparent presence of studies indicating accurate levels of cognitive personalization in digital labor in addition to a potential increase in the worker’s performance, most frequently investigated in crowdsourcing settings. In view of this, the present essay seeks to contribute to the identification of several gaps and opportunities for future research in order to enhance the personalization of online labor, which has the potential of increasing both worker motivation and the quality of digital work.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

User Modeling and User-Adapted Interaction 工程技术-计算机：控制论

CiteScore

8.90

自引率

8.30%

发文量

审稿时长

>12 weeks

期刊介绍： User Modeling and User-Adapted Interaction provides an interdisciplinary forum for the dissemination of novel and significant original research results about interactive computer systems that can adapt themselves to their users, and on the design, use, and evaluation of user models for adaptation. The journal publishes high-quality original papers from, e.g., the following areas: acquisition and formal representation of user models; conceptual models and user stereotypes for personalization; student modeling and adaptive learning; models of groups of users; user model driven personalised information discovery and retrieval; recommender systems; adaptive user interfaces and agents; adaptation for accessibility and inclusion; generic user modeling systems and tools; interoperability of user models; personalization in areas such as; affective computing; ubiquitous and mobile computing; language based interactions; multi-modal interactions; virtual and augmented reality; social media and the Web; human-robot interaction; behaviour change interventions; personalized applications in specific domains; privacy, accountability, and security of information for personalization; responsible adaptation: fairness, accountability, explainability, transparency and control; methods for the design and evaluation of user models and adaptive systems