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The lesson began with a presentation of the asynchronous task on the history of data science (see Table 2, in Grading policy and submissions). The main conclusion of this presentation was that although data science emerged in computer science and statistics, the actual development of data science accelerated when researchers and practitioners realized that the application domain is an important component of data science that cannot be overlooked. This observation, in fact, turned data science into an applicable discipline for many professionals and populations, rather than remaining a discipline that is accessible only to computer scientists and statisticians.
Following our observation in the previous lesson (see Lesson 6) that students need to practice the algorithmic aspect of data science, and given the feedback they provided in the mid-semester questionnaire with respect to the challenges they faced and continue to face in the course and topics they wish to study in the course, we decided to dedicate this lesson to content knowledge (see Lesson 9) and to lead the students in a process through which they would gradually construct their own mental representation of the KNN algorithm from process conception to object conception. Therefore, in addition to a discussion of the content knowledge and pedagogical content knowledge of data science, this lesson also included Python programming based on our understanding that this group of students was also in need of some practical experience in Python. Specifically, the following steps were applied, in the following order: visualization, hands-on activity, and programming tasks.
At the end of the lesson, the students were asked to provide feedback on their experience in this lesson. They explained that the programming practice they gained in this lesson was important for their future teaching for two main reasons, referring to content and pedagogy, respectively: a) the actual Python programming and b) the introduction to different kinds of tasks and the order in which they should be implemented in class.
As teachers of the course, we felt that the decision we made to focus on data science content and on Python programming for one lesson was correct. Furthermore, we realized that not only did we address the prospective teachers’ request for additional focus on data science content knowledge, but the prospective teachers (as they indicated in their feedback) also learned some new pedagogical ideas and principles.