Transparency and Trustworthiness in User Intentions to Follow Career Recommendations from a Learning Analytics Tool

IF 2.9 Q1 EDUCATION & EDUCATIONAL RESEARCH Journal of Learning Analytics Pub Date : 2023-03-09 DOI:10.18608/jla.2023.7791
Egle Gedrimiene, Ismail Celik, Kati Mäkitalo, Hanni Muukkonen
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引用次数: 2

Abstract

Transparency and trustworthiness are among the key requirements for the ethical use of learning analytics (LA) and artificial intelligence (AI) in the context of social inclusion and equity. However, research on these issues pertaining to users is lacking, leaving it unclear as to how transparent and trustworthy current LA tools are for their users and how perceptions of these variables relate to user behaviour. In this study, we investigate user experiences of an LA tool in the context of career guidance, which plays a crucial role in supporting nonlinear career pathways for individuals. We review the ethical challenges of big data, AI, and LA in connection to career guidance and analyze the user experiences (N = 106) of the LA career guidance tool, which recommends study programs and institutions to users. Results indicate that the LA career guidance tool was evaluated as trustworthy but not transparent. Accuracy was found to be a stronger predictor for the intention to follow on the recommendations of the LA guidance tool than was understanding the origins of the recommendation. The user’s age emerged as an important factor in their assessment of transparency. We discuss the implications of these findings and suggest emphasizing accuracy in the development of LA tools for career guidance.
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透明度和可信赖的用户意图遵循职业建议从学习分析工具
透明度和可信赖性是在社会包容和公平的背景下合乎道德地使用学习分析(LA)和人工智能(AI)的关键要求之一。然而,对这些与用户相关的问题的研究尚不清楚,目前的LA工具对用户来说有多透明和值得信赖,以及这些变量的感知如何与用户行为相关。在本研究中,我们研究了职业指导背景下LA工具的用户体验,它在支持个人的非线性职业路径中起着至关重要的作用。我们回顾了大数据、人工智能和洛杉矶在职业指导方面面临的伦理挑战,并分析了洛杉矶职业指导工具的用户体验(N = 106),该工具向用户推荐学习项目和机构。结果表明,LA职业指导工具被评价为可信但不透明。准确性被发现是遵循LA指导工具建议的意愿的一个更强的预测因子,而不是理解建议的起源。用户的年龄成为他们评估透明度的一个重要因素。我们讨论了这些发现的意义,并建议在职业指导的LA工具的开发中强调准确性。
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来源期刊
Journal of Learning Analytics
Journal of Learning Analytics Social Sciences-Education
CiteScore
7.40
自引率
5.10%
发文量
25
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