Multimodal learning analytics have significantly improved our understanding of learning processes. One of the most discussed advantages of multimodality is the holistic view of the learning processes. In this contribution, we explore whether giving feedback based on multimodal analytics benefits learners. We designed and developed a novel system that collects learners’ eye-tracking and heart rate variability data while they debug a given code. The system computes the cognitive load from the eye-tracking data and physiological stress from the heart rate variability data. If the cognitive load and/or physiological stress were more than a threshold, the system would provide feedback on the problem. We conducted a controlled study involving 120 students, with one control condition and three experimental feedback conditions (cognitive load only, stress only, and both cognitive load and stress). The results show that the debugging performance was improved with the feedback. More importantly, we found that the feedback also positively affected students’ cognitive load and physiological stress. We discuss the implications of these results in the view of scalability and impact.