Back to the Methods of Teaching Data Science course
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- Presentation of the asynchronous task (see Table 2 in Grading policy and submissions)
- Discussion: Data science and the 21st century skills as part of the 4th industrial revolution.
- Class activity: Learning environments for data science
- The students worked in pairs on a presentation (later shared with the entire class) which asked to find learning environments for data science (for any level of learners) and to present each environment on one slide.
- Colab, Orange and excel are among the environments the students located.
- Reflection on their work experience in breakout rooms and to share their reflections via the chat function.
- Interdisciplinarity:
- Discussion about the concepts of multi-, inter-, and trans-disciplinarity.
- The students are asked to organize the three concepts in a hierarchy according to the integration level of the disciplines represented in each concept and to share their answer via the chat function.
- Discussion: Is data science multi-, inter-, or trans-disciplinary?
- Feedback on the students’ first task (see Table 1 in Grading policy and submissions). The main topics they requested to learn in the course were:
- The Israeli high school data science curriculum
- Gaps in learners’ mathematical and statistical knowledge
- Learners’ difficulties in learning data science
- Experience of teachers who had already started teaching the new data science curriculum. Accordingly, we organized a panel of such teachers to speak in Lesson 10 of the course.
