Human-Centered Development of Indicators for Self-Service Learning Analytics: A Transparency through Exploration Approach

• URL: http://arxiv.org/abs/2510.08395v1
• Date: Thu, 09 Oct 2025 16:15:54 GMT
• Title: Human-Centered Development of Indicators for Self-Service Learning Analytics: A Transparency through Exploration Approach
• Authors: Shoeb Joarder, Mohamed Amine Chatti,
• Abstract summary: The aim of learning analytics is to turn educational data into insights, decisions, and actions to improve learning and teaching.<n>The reasoning of the provided insights, decisions, and actions is often not transparent to the end-user.<n>In this paper, we shed light on achieving transparent learning analytics by following a transparency through exploration approach.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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 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 a self-service learning analytics 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.

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