Author(s):
In data science and engineering education, most existing IDLE (interactive development and learning environment) modules, such as Jupyter Notebooks and Matlab Live Script, are developed as tutorials or documents without considering learner engagement, or more specifically, learner reflection. As a result, learners often scroll and glance through the material without much reflection on what they are learning. In other words, existing data science and engineering training/learning modules lack built-in reflective activities, which present significant barriers to learning. We view reflection as a critical component of experiential learning and an essential process that solidifies the connection between what a learner experienced and the meaning and understanding they derived from that experience. As John Dewey put it: “We do not learn from experience. We learn from reflection on experience”. Therefore, we propose to address this unmet need by embedding reflective activities into IDLE modules. To ensure that student reflection takes place before, during, and after an experience, our design of reflective activities follows Borton’s “What? So What? Now What?” model for pre-experience reflection, in-action reflection, and post-experience reflection. The effectiveness of these built-in reflective activities was assessed by comparative studies where IDLE modules with and without reflective activities were used in the same class on two randomly assigned groups in a data science and engineering class. The outcomes from this experiment will be presented and discussed in this talk.
