Cyber-Physical Systems integrate hardware with software in complex applications. To mitigate the complexity, engineers rely on model-based systems engineering approaches. Updates and function enhancements lead to frequently changing design constraints and objectives. These changes increase the need to rework and extend model artifacts of the system. This can cause quality degradation over time due to modeling errors, knowledge disparities, or a lack of guidelines. To enable efficient collaboration and reduce maintenance costs in model-based systems engineering, the industry needs a cost-efficient, scalable approach to monitor model quality. The work outlines a doctoral thesis investigating the potential of automated data-driven quality assessment strategies using model artifact history and model changes. We will extract metrics and model changes to establish quality feedback for system engineers. We aim to use manual model quality assessments to incorporate domain-specific expert knowledge into the automated strategy. The main goals are to lower the effort of model quality assessments, to provide practitioners with foresight on quality development, and to estimate task effort to improve model quality.