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This paper investigates how first-year engineering undergraduates and their instructors describe the potential for learning analytics approaches to contribute to students’ success. Results of qualitative data collection in a first-year engineering course indicated that both students and instructors emphasized a preference for learning analytics systems to focus on aggregate as opposed to individual data. Another consistent theme across students and instructors was an interest in bringing data related to time (e.g., how time is spent outside of class) into learning analytics products. Students’ and instructors’ viewpoints diverged in the “level” at which they would find a learning analytics dashboard useful—instructors remained focused on a specific class, but students drove the conversation to a much broader scope at the major or university level but in a discipline-specific manner. Such practices that select relevant data and develop models with learners and teachers instead of for learners and teachers should better inform development of and, ultimately, sustainable use of learning analytics-based models and dashboards.
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