In higher education, quality feedback for students is regarded as one of the main contributors to improve student learning. Feedback to support students' development into self-regulated learners, who set their own goals, self-monitor their actual performance according to these goals, and adjust learning strategies if necessary, is seen as an important aspect of contemporary feedback practice. However, only those students who are aware of the course demands and the impact of certain study behaviors on their final achievement are in a position to self-regulate their learning on an informed basis. Learning analytics is an emerging field primarily concerned with using predictive models to inform educational instructors or learners about projected study outcomes. In a scoping study, over 200 students of an introductory programming course (CS1) were supplied with information revealing performance indicators for different stages on the course and projecting final performance for various achievement levels. The study was set out to explore the impact of this type of feedback in the confined context of a CS1 course as well as to learn about students' attitudes toward diagnostic course data in general. The results from the study suggest that students valued the information, but, despite high engagement with the information, students' study behavior and learning outcome remained rather unaffected for the aspects investigated. Given these multi-layered results, we suggest further exploration on the provision of feedback based on diagnostic course data--a vital step toward more transparency for students to foster their active role in the learning process.