以人为中心的自主学习分析指标开发：一种通过探索实现透明性的方法 (Human-Centered Development of Indicators for Self-Service Learning Analytics: A Transparency through Exploration Approach)

The aim of learning analytics is to turn educational data into insights, decisions, and actions to improve learning and teaching. The reasoning of the provided insights, decisions, and actions is often not transparent to the end-user, and this can lead to trust and acceptance issues when interventions, feedback, and recommendations fail. In this paper, we shed light on achieving transparent learning analytics by following a transparency through exploration approach. To this end, we present the design, implementation, and evaluation details of the Indicator Editor, which aims to support self-service learning analytics (SSLA) by empowering end-users to take control of the indicator implementation process. We systematically designed and implemented the Indicator Editor through an iterative human-centered design (HCD) approach. Further, we conducted a qualitative user study (n=15) to investigate the impact of following an SSLA approach on the users' perception of and interaction with the Indicator Editor. Our study showed qualitative evidence that supporting user interaction and providing user control in the indicator implementation process can have positive effects on different crucial aspects of learning analytics, namely transparency, trust, satisfaction, and acceptance.

翻译：学习分析的目标是将教育数据转化为洞察、决策和行动，以改进学习和教学。然而，所提供的洞察、决策和行动的逻辑通常对最终用户并不透明，当干预、反馈和建议失败时，这可能导致信任和接受度问题。在本文中，我们通过遵循一种通过探索实现透明性的方法，阐明如何实现透明的学习分析。为此，我们介绍了指标编辑器的设计、实现和评估细节，该工具旨在通过赋予最终用户控制指标实现过程的能力，支持自主学习分析（SSLA）。我们通过迭代的以人为中心设计（HCD）方法，系统性地设计和实现了指标编辑器。此外，我们进行了一项定性用户研究（n=15），以探究遵循SSLA方法对用户对指标编辑器的感知和交互的影响。我们的研究提供了定性证据，表明在指标实现过程中支持用户交互并提供用户控制，可以对学习分析的不同关键方面产生积极影响，即透明性、信任、满意度和接受度。