Abstract
This Research to Practice Full Paper presents a framework to evaluate and design flipped classroom activities for data science and management courses. Variants of flipped classrooms have been employed in STEM fields with great success in students’ learning outcomes. Research shows that flipped classrooms would improve students’ learning if it is implemented following rigorous procedures of an efficient instructional design. As a result, one of the critical focus of current flipped classroom research is what factors educators need to consider when designing a flipped learning environment. Currently, educators incorporate various factors such as pre-recorded video lecture, group activity as a trial and error basis and adjust these factors based on their own experience and students’ feedback. On the other hand, the emergence of big data expects a new graduate to demonstrate mastery of concepts and skills for data acquisition, management, and analysis of inference from data when they enter the workforce. Currently, there is no systematic approach available to design a flipped classroom that is for the data science and management courses. In this research, we develop a framework first to investigate and evaluate the flipped classroom factors mentioned in the literature and identify a few that are most relevant to the two data management courses at our institute. Then, we classify each course topics into broader categories. So that the flipped classroom model can be developed for each category. For the flipped classroom for each category, we identify the pre-class and in-class activities to meet a certain learning objective for that topic category for each course. To evaluate the effectiveness of different factors as well as our flipped classroom models, students’ performance data, interviews, and surveys are conducted. This process is transformative and can be employed by other STEM disciplines to find the most influential factors to design effective flipped learning classrooms.
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