(459e) Enhancing Student Reflection in Data Science and Engineering Education

In data science and engineering education, most existing online 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 reflection activities, which present significant barriers to learning. As John Dewey put it: “We do not learn from experience. We learn from reflection on experience”. We propose to address this unmet need by embedding reflection activities (and reflective assignments) into IDLE modules. 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 they derived from that experience. Therefore, it is vital that student reflection takes place before, during, and after an experience. To achieve that, our design of reflection 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 reflection activities was assessed by comparative studies where IDLE modules with and without reflection activities were used in the same class on two randomly assigned groups in the course “CHEN 5970/6970/6976 Big Data Analytics and Machine Learning in Process Industry”. The effectiveness is evaluated using the Metacognition Awareness Inventory (MAI) questionnaire to quantify students’ metacognition awareness gains. The scores are analyzed and compared categorically with a particular focus on reflection, and holistically on overall metacognition awareness gains.