Lesson #10

Back to the Methods of Teaching Data Science course

Back to Technion Data Science Education website

Following the request prospective teachers expressed in the questionnaires, to hear about the actual teaching of the data science unit in high schools, this lesson was dedicated to a panel of high school computer science teachers who actually teach the Israeli high school data science unit. In the panel, the teachers were asked to share their experience with the students.

Following a message sent to the WhatsApp group of teachers who teach high school data science, five teachers expressed their interest in participating in the panel and consequently joined the panel. The panel was structured as follows:

  • Self-introduction of the teachers (up to two minutes per teacher), including their background, teaching experience, and which units they teach
  • Self-introduction of the students (up to one minute per student)
  • Each teacher then talked for up to five minutes about his or her experience teaching data science in the high school
  • Q&A session

Many interesting ideas emerged up during the panel in two main categories: (a) the panelists – the teachers, and (b) the pedagogy, in general, and project evaluation, in particular.

(a) The teaches

  • All of the teachers were science or engineering graduates.
  • All of the teachers had embarked on their teaching career as a second career after having worked in the high-tech industry for several years. This information was especially relevant for the students in the course, since five of the seven students in the course were studying for a teaching certificate while pursuing a career in the high-tech industry.
  • The teachers were in their second or third year of teaching the new data science program.
  • There is a large community of teachers who are tackling the challenges of teaching data science and who can assist and guide new teachers.
  • Many teachers have a lot of teaching materials.

(b) Pedagogy

  • Teachers should adopt individual learning and should demonstrate its practice to their pupils. Since the teachers are new to this subject matter, it is important to share with their pupils how they find solutions to problems they encounter.
  • Data science education is a new field and different teachers use different teaching approaches.
  • Teachers should constantly adjust their teaching approach to their pupils’ level during the entire teaching process.
  • It is important to impart skills and tools to the pupils so that they may seek data and solutions by themselves.
  • The teaching process is flexible in terms of the order of the teaching the different topics, the teaching tools that are used, e.g., Google Classroom, and additional pedagogical considerations.
  • Project-based learning was given a great deal of attention in the panel due to the fact that the pupils are required to develop a project as their final submission according to both versions of the data science high school curriculum (basic and advanced).
    • The importance of gradual project development was emphasized, for example, by meeting each pupil on a weekly basis. Thus, the pupils arrive at the final oral presentation and oral exam well prepared and can focus on ideas that are beyond the programming implementation of the specific algorithm used in the project.
    • Project evaluation – messages delivered to the pupils:
      1. Low performance indicators do not mean failure, as long as the pupil can explain the meaning of the indicators and analyze the results of the algorithm.
      2. The quality of the selected data base should not be an evaluation criterion for student grading. The focus should be placed on what the pupils do with the data base: how they cope with missing data, how they take advantage of high-quality data bases, how they analyze the data set, and so on.