# Steering Recommendations and Visualising Its Impact: Effects on Adolescents' Trust in E-Learning Platforms

## TL;DR

This study introduces a control mechanism and visualization in an e-learning platform, demonstrating that visualizing control impact enhances adolescents' trust, perceived transparency, and encourages reflective engagement with recommendations.

## Contribution

The paper presents a novel control and visualization design for e-learning recommender systems, tested with adolescents, showing positive effects on trust and transparency.

## Key findings

- Visualizing control impact significantly increases trust.
- Control over mastery level encourages self-challenge.
- Perceived transparency improves through visualization.

## Abstract

Researchers have widely acknowledged the potential of control mechanisms with which end-users of recommender systems can better tailor recommendations. However, few e-learning environments so far incorporate such mechanisms, for example for steering recommended exercises. In addition, studies with adolescents in this context are rare. To address these limitations, we designed a control mechanism and a visualisation of the control's impact through an iterative design process with adolescents and teachers. Then, we investigated how these functionalities affect adolescents' trust in an e-learning platform that recommends maths exercises. A randomised controlled experiment with 76 middle school and high school adolescents showed that visualising the impact of exercised control significantly increases trust. Furthermore, having control over their mastery level seemed to inspire adolescents to reasonably challenge themselves and reflect upon the underlying recommendation algorithm. Finally, a significant increase in perceived transparency suggested that visualising steering actions can indirectly explain why recommendations are suitable, which opens interesting research tracks for the broader field of explainable AI.