First-Year Engineering Students? Ideation in Data Analytics

Big data analytics has grown as a valuable tool for professionals from different fields to get insights from large volumes of data and make data-driven decisions. Given this, engineering students need access to learning environments that support learning data analytics. These activities should teach students the skills to assess data, design high-quality questions, perform data analysis, and provide recommendations in a manner that is aligned with client needs. To this end, we developed and implemented the data analytics activity called "The Bike-share problem " for a First-Year Engineering (FYE) design and modeling course. To analyze the students' ideation of questions and recommendations when working on the activity, our summarized research question is: What are the characteristics of FYE students' proposed questions and recommendations for a client as part of their data analytics project? We analyzed questions and recommendations from teams' final reports using qualitative content analysis. Our findings show that the students' questions ranged from superficial treatments of the data that required simple analyses to deep explorations of the problem that required more complex analyses. For the recommendations, we found that model responses include considerable detail, support with data, and justification based on the client needs. While both the questions and the recommendations were important separately, we also found differences among teams' ability to align their recommendations to the client with the actual questions they were trying to answer. The differences in student responses to the activity can have many explanations as to the cause; however, we have evidence that perhaps scaffolding in the way the activity is posed and team dynamics may have affected how students responded to the activity. Finally, we provide some effective practices that interested readers may implement to design analytics activities that promote students' ideation.